Origins and Evolution

The Foundations Library began as an idea: a space where knowledge essential to humanity’s future would be gathered, preserved, and made accessible to scholars, enthusiasts, and visionaries. Initially hosted at the Transhuman House in Provo Utah, the library has since grown to include rare and out-of-print works, an extensive “physical” card catalog, and unique resources that can only be accessed on-site.

The private location near Seattle, opened in 2023, provides a secure home for the growing collections. It is open for private tours by appointment, offering visitors a glimpse into some of the most intriguing works ever assembled on the themes of transhumanism and civilizational resilience.

The Transhuman Collection

At the heart of the Foundations Library is the Transhuman Collection, an unparalleled repository of materials exploring transhumanism. This collection spans rare works, signed editions, and one-of-a-kind items, such as:

• Out-of-Print Works by FM-2030: Featuring signed copies of the visionary’s writings, these are treasured pieces offering insight into transhumanism’s early ideals.
• The Foundation Codex: A unique and exclusive resource, the Codex outlines the framework for creating and using religion centered on the preservation of knowledge in a post-apocalyptic world. This document, available only for on-site study, is a testament to the collection’s focus on the fusion of innovation and tradition to preserve knowledge.
• Uncatalogued Material: A substantial backlog of new acquisitions awaits indexing, promising to expand the collection with even more groundbreaking ideas and historical significance.

The Civilization Collection

Complementing the forward-looking Transhuman Collection is the Civilization Collection, dedicated to the practical and philosophical aspects of rebuilding civilization from the ground up. This collection emphasizes the preservation of essential skills, knowledge, and cultural touchstones necessary for humanity to thrive in the event of societal collapse.

It includes:

• Guides on self-sufficiency and survivalism.
• Manuals for engineering, agriculture, and medicine.
• Books outlining rebuilding technology from the Stone Age forward from scratch.
• Philosophical treatises on governance and ethics in rebuilding societies.

The Civilization Collection is a blueprint for starting anew, providing tools and inspiration for creating resilient, sustainable communities and as a bulwark to prevent the loss of civilization.

The Move to the New Transhuman House

With plans to relocate in 2025, the library is preparing to move into the new Transhuman House. This move is expected to further enhance the library’s capabilities, offering a more expansive space for its growing collections and the integration of digital cataloging technologies to complement its traditional card catalog.

Visiting the Foundations Library

The Foundations Library is currently available for private tours by appointment. Its location near Seattle provides a serene setting for exploring its treasures. Whether you are a researcher, a transhumanist, or simply curious about the future of humanity, the Foundations Library offers a rare opportunity to engage with the ideas shaping our world and the next.

With its roots firmly planted in the ideals of knowledge preservation and human progress, the Foundations Library is poised to inspire generations to come.

Contact me at pieseczek@hotmail.com 

here is the public spreadsheet of the collection as of 2023:  LibraryContents_2023_public

Great job to the editors, Alexei Samsonovich and Tingting Liu. The book includes reports on biologically inspired approaches and their applications, Bridges between artificial intelligence and cognitive, neuro-, and social sciences, and highlights innovative research addressing the BICA Challenge.

Finally, after the BICA track at 2024 AGI conference in settle, my two papers got released in the proceedings book from Springer here: https://link.springer.com/book/10.1007/978-3-031-76516-2?sap-outbound-id=0A34C0BAE61B956A4CA1DD5C48793788C0964CD7 

if you want to get the papers without the paper wall:

Kelley, D.; “Non-Logical Simulation Model-based Decision-making Systems to Drive Self-Motivation in Software Systems;” BICA 2024 / AGI Conference 2024; DOI:10.13140/RG.2.2.21015.38565; Springer Nature BICA Proceedings Dec 2024;  https://www.researchgate.net/publication/381397960_Non-Logical_Simulation_Model-based_Decision-making_Systems_to_Drive_Self-Motivation_in_Software_Systems;

Kelley, D.; “Problem-Solving and Learning Strategies within the Independent Core Observer Model (ICOM) Cognitive Architecture;” BICA 2024 / AGI Conference 2024; DOI: 10.13140/RG.2.2.17125.82406; Springer Nature BICA Proceedings Dec 2024; https://www.researchgate.net/publication/381375310_Problem-Solving_and_Learning_Strategies_within_the_Independent_Core_Observer_Model_ICOM_Cognitive_Architecture;

Introduction  

The original project that led to ICOM (Independent Core Observer Model) was an unpublished survey of research related to AGI (Artificial General Intelligence), and one of the findings in that study, while not controlled enough to be scientific, was at least suggestive of the fact that of the current research as of 2012 that was ongoing did not have the solution of ‘motivation’ solved for general purpose AI (Artificial Intelligence) or AGI.  Out of this, the original research ICOM was built on neuroscience research by Antonio Damasio [Damasio].  His research demonstrated that humans’ choices are based on emotions or how humans feel about their choices.  That is not to say that humans can’t think logically based on Damasio’s work.  Still, they may like the feeling of making logical choices.  Looking at theories related to consciousness or subjective experience, this work was based on global workspace theory.  ICOM was built on including Global Workspace Theory [Baars], Integrated Information Theory [Balduzzi], Computational Theory of Mind [Rescorla], the Conceptual Dependency Theory [Schank], and Hierarchical Memory Theory [Ahmad].  Out of that work was developed the abstract theory of consciousness [Kelley], and this is where we will focus on for the rest of this paper on the single element of simulated feeling values used to create a simulated emotional global workspace that in abstract simulates the feeling or experience of emotions and using that as at the basis for driving motivation and related functions in ICOM based software agents.

This paper will not explore other details about ICOM and its workings.  Please refer to the ICOM Research Codex [Kelley] for further information about ICOM outside the context of this paper and emotional simulation.

A Simulated Emotion  

In ICOM, we use a Plutchik [Plutchik] Model of emotions.  There are numerous theories of emotions or theories about how emotions can be represented.  Still, Dr. Plutchik’s model seems the simplest and complete.  While there are arguments for different biological models in humans that a large part of what we call emotions is influenced by culture and the ‘Western’ model of perceived emotions [Barrett] for simplicity’s sake and to confine ICOM research to narrow the scope the Plutchik model was selected albeit inverted to make it easier to be computationally sound and allow research to move forward without entirely solving all the possibilities around emotion models in humans.

Looking at ICOM, an example of a Plutchik model catalogs human emotions on 8 emotion areas or vectors.  If we use floating point values to represent each emotion vector, we have an array of 8 floating point numbers.  As a comparative example, if we used the Wilcox model of emotions, it would take at least an array of 77 floating point values as well as other potential problems with consistency and seems to not represent anything more significant than what Plutchik did.  A lot of the complexity we see in the Wilcox model is defined by composites of emotion vectors in Plutchik.  In ICOM, we represent the state of the machine using this Plutchik model.

Instead of representing these Plutchik models as an array or single-line matrix, we will show them as they would map to Plutchik to make it easier to understand that mapping later on.  Understanding the mapping of one emotion, such as happiness, is not important to how math works for the simulated emotional system.

Let’s look at some examples:

Figure A – Changing Simulated Emotional States.

In this example, we can see that the simulated emotion states change based on various stimuli.  In State 1, this represents a blank emotional state.  After some input that has 1 emotional additive value for position A1.  After that is applied, we have state 2.  State 3 is essentially the same thing, with these simulated emotions changing based on various inputs.

What this means, for example, is that there is some process monitor on a computer.  Each time the network traffic is slow then, it generates some input into the system that has the emotion state A1+1, which is added to the machine’s emotional value of A1, as we see in state 2.  At the same time, a different input from some other element of the machine might have an input value of B1+1.  When added to the existing machine model, we have state 3, which is our current simulated emotional state.  You can also have another state change when the network connects, that is A1-.5, which would then lower the A1 value of the machine by .5.  In this manner machine, simulated emotional states can change based on what is happening in the machine, and this separates the nature of individual emotions to focus just on the structure and mathematical operations of a simulated subjective experience.

Let’s take a look at a more complex example based on how ICOM currently does it:

Figure B – Matrix Operation.

Now, one of the problems with simulating subjective emotional states with numbers that include the complex relationships between emotions and emotional states in the simulated model in ICOM is that we use a matrix to map a given Plutchik emotion model to its total effect on the system.  In this example, we see the start state of the model, and we have some input.  Instead of just adding it all together, we use this matrix that maps the relationships between the various emotional values we track and every other emotional value.  Using a matrix, we can multiply the input model by the matrix, take those values, and add them to the start state, which gives us the new state of our simulated model.  These state changes can be tied to any number of other triggers, but let us look at this in a mathematics method of the example we used above:

Figure C – Matrix Operation.

As we can see here, the ‘Plutchik’ models are treated as a single line matrix against the larger rules matrix.  We get an output of 8 values in an array that we map back to our visualization of those values.  Pull up a diagram of a Plutchik model.  You can see what these particular emotions reference, but it is not essential to understand the operations in ICOM.

In testing this approach, another issue with ‘subjective’ experience over time is how it affects how we as humans feel.  To simulate this complex interaction between our thoughts and how we think and external stimuli in ICOM, each thought can have emotional values of some sort or another that affect how we feel.  Plus, stimuli have various effects on the core Plutchik model, so we added a send set of machine states.  Stimuli and thoughts affect this second state only a small amount.  This has the effect of the first state being relatively volatile while the second state is more stable.  Granted, this can be controlled by the matrix used and could, therefore, vary wildly in implementation and variations of states or reactions in the model.  This method of having a secondary model allows us to use this second model to recover back to center over time, depending on additional input.

In ICOM, we have tuned the two models using two separate matrices.  Each time we have stimuli or even an internal cycle treated as stimuli, the input is computed to affect the core model, as stated above.  Still, it is also calculated against the second model using the second less volatile matrix and model.  Let us take a look at this visualization:

Figure D – Adding the Second Less Volatile Model.

In this, we can see we do both operations for each model with the input, and the second model changes less because of how we defined the rules.  This model is then used to drag the first model back to the center.  This acts as our buffer to keep the system from varying wildly over time and allowing the system to recover back to a stable point.

Now, let’s look at how this applies to decision-making.

Simulated Emotional Decision Making

Previously, we used a single input to represent the simulated emotional content of some random input.  The following example will focus on decisions or choices.  In ICOM, anything coming into the system generates a knowledge graph, and the context engine generates any number of actions that could be taken based on that graph.  It looks at previous experience and other data from the graph associated with any element of the new graph.  We need to look at the graph structure to understand how that works.

Figure E – Graph Structure Example.

In the current implementation of ICOM, each edge in the graph is one of these emotion Plutchik models and a type value allowing meta structures to be cataloged in the graph itself, among other reasons.  Given this structure, let’s look at an example of some input value.  The context engine determines that an ‘N’ number of actions can be taken in response to this input.  ICOM will check and see if there is some automated response in function similar to how some behaviors are part of the autonomic nervous system in humans, and you can’t really control the responses.  Generally, these are survival-related, like pulling your hand away from something burning hot before the signal gets to the brain.  In function, this can happen in ICOM, depending on how the instance is set up.

Let us look at the example of the input mentioned, and let’s say there are three possible actions.  Let’s look at this diagram:

Figure F – Example Input with Action Values.

Granted, this example is overly simplified but accurate.  As we can see, we have some input value that is of high enough interest and has action models associated with it that will make it to the global workspace.  There are three action models, and when we look at each action model’s net positive change, we see that it is highest with Action 3.  Essentially, the ‘action’ that makes the machine ‘feel’ best is selected regardless of other factors.   The possible result values are added to the Current State P.  We end up with the end state P. Note that we are saying the first row is positive and A2 for this example.  The rest of the positions in these Plutchik models are negative emotions.  A positive or negative number is different in terms of the positive effect on the system, depending on the current state.  Suppose a terrible idea at least makes the system feel “better” (relative to the emotional simulation).  In that case, it will take action unless some overwhelming bias affects these calculations before it is raised to the global workspace.  Once any action is selected and raised to the global workspace, it is too late, and the system will try to do that action.

In this example, we saw that action 3 has the most impact, with a positive effect of 4 points on the global workspace’s primary emotional model.  Thus, this simulated emotional model selects for a net positive impact.  This also means that even if the actions are all positive, if the current P state is high, those positive values could negatively affect the main P state.

The degree to which you could do this and add various features like filtering for bias and lowering the net positive based on that or based on some moral or ethical structure is more or less infinite.  Still, this configuration, along with the underlying sub-model, meaning two models in the global workspace, seems to provide results that appear human-like in terms of selecting what makes the system feel good.

Discussion  

Before coming to conclusions based on the paper’s content, we can draw a few possible conclusions or areas that could be researched in more detail to build on this.

Implications for Human-Robot Interaction: The ICOM’s ability to simulate human-like emotions and decision-making could significantly improve human-robot interaction.  Robots and autonomous agents with this system might better understand and predict human emotions, leading to more intuitive and empathetic interactions.

Ethical and Moral Decision-Making: While the paper touches on the potential for bias filtering, the broader implications suggest that ICOM could be used to develop autonomous systems based on ethical and moral guidelines.  This could be crucial for applications in sensitive areas such as healthcare, law enforcement, and education.

Adaptive Learning and Personalization: Using simulated emotions to drive goal and motivation generation implies that ICOM-based systems could be highly adaptive.  They could learn and personalize their responses and actions based on the emotional feedback from their environment, leading to more customized and effective interactions over time.

Enhancement of Creativity and Problem-Solving: ICOM could enhance AI systems’ creativity and problem-solving abilities by mimicking the human tendency to make decisions based on emotional states.  These systems might explore unconventional solutions and approaches that purely logical systems might overlook.

Mental Health Applications: The ability to simulate emotional states and understand their impact on decision-making could be leveraged in mental health applications.  AI systems could simulate and study emotional responses, potentially leading to new treatments and interventions for mental health conditions.

Impact on Long-Term AI Development: Incorporating non-logical, emotion-based decision-making represents a shift in AI development paradigms.  It suggests a move away from purely logical AI systems towards more holistic models that consider a broader range of human-like experiences and motivations, potentially leading to more robust and versatile AI.

Resilience and Stability in AI Systems: Using dual emotional state models (volatile and less volatile) to stabilize the system suggests that ICOM-based AI could be more resilient to erratic or extreme inputs.  This could make them more reliable in dynamic and unpredictable environments.

The ICOM framework introduced in the paper could serve as a foundation for future research in artificial general intelligence (AGI).  By addressing the motivation aspect, which is crucial for AGI, ICOM could pave the way for more advanced and human-like intelligent systems.  These highlight the broader impact and potential applications of the ICOM cognitive architecture beyond what is explicitly mentioned in the paper.

see the paper on Research Gate for more details, including the figures: http://dx.doi.org/10.13140/RG.2.2.21015.38565 

References

Ahmad, S.; Hawkins, J.; “Properties of Sparse Distributed Representations and their Application to Hierarchical 397 Temporal Memory”; 24 MAR 2019; Cornell University Library

Baars, B.; Katherine, M; “Global Workspace”; 28 NOV 2016; UCLA http://cogweb.ucla.edu/CogSci/GWorkspace.html

Baars, B.; McGovern, K.; “Lecture 4.  In the bright spot of the theater: the contents of consciousness;” CIIS 2005

Baars, B.; Motley, M.; Camden, C.; “Formulation Hypotheses Revisited: A Replay to Stemberger”; Journal of Psycholinguistic Research; 1983

Baars, B.; Motley, M.; Camden, C.; “Semantic bias effects on the outcomes of verbal slips”; Elsevier Sequoia 1976

Baars, B.; Seth, A.; “Neural Darwinism and Consciousness”; science direct – Elsevier 2004

Balduzzi, D.; Tononi, G.; “Qualia: The Geometry of Integrated Information”; PLOS Computational Biology 5(8): e1000462, 2009. doi:10.1371/journal.pcbi.1000462

Barrett, L.; Campbell, C.; et al.; “How Emotions Are Made: The Secret Life of the Brain;” ISBN-10 ‏ : ‎ 9780544133310; Mariner Books; First Edition; March 2017;

Kelley, D.; “Independent Core Observer Model Research Program Assumption Codex;” BICA 2019, Pre-conference Proceedings: https://www.springer.com/us/book/9783030257187

–––; “The Independent Core Observer Model Theory of Consciousness and the Mathematical model for Subjective Experience;” By ICIST 2018 – International Conference on Information Science and Technology – China – April 20-22nd.  (IEEE conference); Year: 2018, Volume: 1, Pages: 396-400; ISBN: 978-1-5386-6956-3; https://www.computer.org/csdl/proceedings-article/icnisc/2018/695600a396/1dUo3atoEAo

Kelley, D.; Twyman, A.; “Independent Core Observer Model (ICOM) Theory of Consciousness as Implemented in the ICOM Cognitive Architecture and the Associated Consciousness Measures;” By  AAAI Spring Symposia (AAAI SSS19) – Stanford, CA; March 2019; http://diid.unipa.it/roboticslab/consciousai/; Published Volume – http://ceur-ws.org/Vol-2287/; Published (PDF) – http://ceur-ws.org/Vol-2287/paper33.pdf

Plutchik, R. 2002.  Emotions and Life: Perspectives from Psychology, Biology, and Evolution.  American Psychological Association.

Plutchik, R. 1980b.  A general psychoevolutionary theory of emotion.  In R. Plutchik, & H. Kellerman, Emotion: Theory, research, and experience: Vol. 1.  Theories of emotion (pp. 3-33).  Academic Publishers.

Plutchik, R. 1980a.  Emotion: A Psychoevolutionary Synthesis.  Harper & Row.

Plutchik, R. 1962.  The emotions: Facts, theories, and a new model.  Random House.

Schank, R. C. (1972). Conceptual dependence: A theory of natural language understanding.  Cognitive Psychology, 3(4), 552–631.  https://doi.org/10.1016/0010-0285(72)90022-9

Introduction  

The problem-solving model generation system specific to actions that the Independent Core Observer Model (ICOM) System wants to take within the ICOM cognitive architecture [Kelley] is the core purpose of the paper, providing the basics for how these systems can be used.  This System is designed to continuously learn and adapt to new tasks, improving its problem-solving capabilities and allowing it to grow and extend its functionality dynamically.  The critical components of this System include a feedback mechanism, problem-proposed solution generation, and implementation of new tasks or goals in learning actions.  By combining these components within the ICOM cognitive architecture, we aim to create a powerful and flexible problem-solving system that paves the way for more advanced and adaptive cognitive systems in ICOM.

NOTE: figures and conclusion redacted.  see the research gate copy for additional details. http://dx.doi.org/10.13140/RG.2.2.17125.82406 

Independent Core Observer Model (ICOM) Cognitive Architecture

The Independent Core Observer Model (ICOM) cognitive architecture is a cognitive framework that aims to create a self-aware, conscious artificial intelligence system that is both sapient and sentient and makes all its decisions based on internal subjective experience.  ICOM does this by implementing several vital patterns or theories related to cognition, including Integrated Information Theory [Balduzzi], Global Workspace Theory [Baars], Computation Theory of Mind [Rescorla], Hierarchical information theory [Ahmad], Conceptual Dependency Theory [Schank]; where Global workspace theory, as articulated by Antonio Damasio [Damasio], is the primary foundation the ICOM architecture is based on.  Please see existing research for more information about ICOM in general [Kelley]; while borrowing from all of this, ICOM uses the ‘Abstract Theory of Consciousness [Kelley].

ICOM is designed to be a modular and flexible system that can easily be modified or have components switched out.  It consists of an observer module responsible for processing sensory information, executing decisions, building knowledge graphs, generating models of actions, and otherwise interacting with the external world.  The core module calculates a model of internal or simulated emotional states and how they relate to any new input.  When the core simulates positive values related to a given knowledge graph, the observer ‘sees’ that and tries to execute on that action if a preferential action model is present on any given graph sent to the ‘core.’ [Kelley, Twyman].

In ICOM, self-reinforcement [Sutton] happens as results from actions are stored as graph models in the overall context graph database, and the edges of the graph are created only on subjective state models by intensity.  Actions that have positive associations are more likely to be selected and more likely to further enhance the positive nature of the results associated with a given action.  This process works for positive and negative reinforcement based on action results that bias the System toward any choice, goal, or motivation.

Problem-Solving Model Generation

Problem-solving model generation is a process that involves continuously adapting and evolving solutions to complex problems.  There are a lot of approaches, from adaptive heuristics, meta-learning, self-play/simulation, transfer learning, evolutionary algorithms, and more [Russell].  This approach recognizes that problems are not static and require ongoing attention and innovation to address effectively.

ICOM attempts to address this in the context engine part of the System, where an initial input interpretation is converted into a knowledge graph model.   The knowledge graph is built based on relationship vectors and directly related models from preexisting graphs.  It will try to identify possible actions and look to see if an associated action exists in the form of a model.  Creating or building a new action model goes through the following process in ICOM, which occurs primarily in the observer engine after the core simulated subjective analysis.

Figure A – Problem Solving Model Generation Diagram.

In this diagram, we can see the recursive process of building out possible steps.  Based on the results, the action model will be modified the next time the System requests this action if the results are not optimal.

This can be done without LLMs (Large Language Models) [Meyer].  In initial versions of ICOM, this process was limited and narrow in scope, where LLMs get us to something approach the broad generic functionality as seen in humans.  LLMs get us farther along with natural language processing, and idea generation is more refined by orders of magnitude when combined with the knowledge graph from the context engine.  The usage of LLMs is incidental to ICOM.  The LLM gets our final models to be more refined going into processing.  It allows a level of refinement for new tasks that were not possible previously.  LLMs are a technical catalyst for complex problem-solving systems that are done from scratch using this method.  That said, it should be noted this is a step away from how the human mind does it to a large degree.  Even though general functionality might be the same or similar at a high level, or the subjective experience of the System might be the same at a high level, the implementation details are very different.  This difference is relevant to what we know how to do currently and does not seem to directly relate to how humans do these sorts of tasks in detail.

Implementation of New Task Actions

In current implementations of ICOM, there is a built-in predilection for action, meaning that if there is a chance of a positive outcome, more likely than not, the System will try to take a given action because it automatically associates positive values to actions over not taking action.  Still, most often, such an action might not be defined, which would be done by the Observer after the System has selected a given action to take.  Suppose a given step required to take a given action is unknown.  In that case, the System will try to build that step dynamically as either an ECMA block (JavaScript or Typescript, etc.) or other transformation-based logic (XSLT or other XML variant) defined in the overall action object model (“defined” in JSON or XML) or if there is an existing XML definition for something that seems to be similar.  The details of a given action generally will not filter up to the context database or the conscious awareness of the System.  However, they will still be stored in the context graph.  The System will draw context graph data and use a deep neural network (DNN) or Large Language Model (LLM) to generate what that step might be.  This is done at the observer engine level or by a part of ICOM called “the Observer Engine.”  Then, the Observer Engine takes the action, and the results are passed back to the context engine (also part of the Observer) to reinforce the action based on the results.  When actions are taken a second time, those past results will largely determine what action is taken the second time or if modifications need to be done.

Continuous Learning and Adaptation

Learning based on action results in ICOM is done by analyzing the results of generated actions and subjectively reinforcing those actions taken or not taken in the context graph based on results based on how well the results match subjective contextual data initially assigned to the model.  Each time an action is taken, the System will try to find errors and correct or improve steps for a given action, especially if the results are not “good” as defined by the System.

Building on the adaptive and dynamic nature of the ICOM system, this approach significantly enhances the development of robust and flexible problem-solving capabilities.  ICOM has evolved into an increasingly adept cognitive system by integrating real-time feedback mechanisms and continuously refining its action strategies.  The use of deep neural network models, including Large Language Models, allows for the generation of sophisticated, context-aware steps that are not predefined but are dynamically constructed based on immediate needs and past outcomes.  This capability makes ICOM flexible and adaptive to new, unpredictable scenarios, pushing the boundaries of traditional problem-solving systems.  Moreover, the System’s ability to learn from previous actions and adjust its behavior based on emotional valences attached to outcomes positions it as a forward-thinking model in adaptive cognitive systems.  This ongoing cycle of action, evaluation, and reinforcement leads to refined decision-making processes that can handle complex, evolving challenges, setting a new standard for intelligent systems operating independently while aligning with desired emotional and contextual outcomes.

Consider the following example.

The example system incorporates three main components: an XML Object Model for structuring task representations, ChatGPT Interaction for generating detailed action step generation and corresponding code, and a Neo4j Graph Database for storing these models to facilitate learning from previous executions.

The methodology unfolds through several phases: initializing the System with an XML document, decomposing tasks into detailed steps via ChatGPT, determining the appropriate execution strategy (JavaScript or C#) for each step, and executing and storing these actions in a graph database.

Note that this is a simplified version of how the ICOM research implementation does this.

In this example implementation, we highlight the separation of concerns and clarify the functionality of each code block.  However, we will not cover library details core to the language C# used.

class Program
{
    static HttpClient client = new HttpClient();
    static async Task Main(string[] args) { }

Figure B: Object Setup

This segment defines the Program class and its main entry point.  The HttpClient is initialized here to facilitate API interactions throughout the application.  The Main method is marked async to enable asynchronous programming, allowing the program to run tasks concurrently without blocking the main execution thread.

String xmlActionModel = "<ActionModel><Task>Write a blog post</Task></ActionModel>";
XmlDocument doc = new XmlDocument();
doc.LoadXml(xmlActionModel);

Figure C: XML Initialization

An XML document is initialized with a predefined structure containing a basic task.  This XML model serves as the foundation for generating further action steps.  The XmlDocument class is utilized here to load and parse the XML string, setting up the initial structure of the action model.

XmlNode taskNode = doc.DocumentElement.SelectSingleNode("//Task");
String taskDescription = taskNode.InnerText;

Figure D: Task Node Selection

The task description is extracted from the XML document.  This step involves selecting the “Task” node from the document and retrieving its text content, which specifies the task that needs to be broken down into executable steps.

XmlNode stepsNode = await ProcessSteps(task description);
doc.DocumentElement.AppendChild(doc.ImportNode(stepsNode, true));

Figure E: Recursive Step Processing Initiation

This code initiates the recursive decomposition of the task into detailed steps by calling the ProcessSteps method.  (See associated .cs file referenced (Kelley).)  The resulting nodes are then appended to the main XML document, enriching with executable steps and sub-steps.

Doc.Save("ActionModel.xml");
await SaveToGraphDatabase(doc);
ExecuteActionModel(doc);

Figure F: Saving and Executing the Action Model

Finally, the enriched XML document is saved locally and stored in the Neo4j graph database.  This persistence allows for tracking the evolution of action models and learning from past executions.  The action model is then executed, potentially involving running JavaScript code or C# functions as defined in the earlier steps.

These code blocks provided a relatively simple but comprehensive C# program that integrates an Artificial General Intelligence (AGI) system with an AI language model, specifically ChatGPT, to dynamically generate and execute action steps based on given tasks.  It begins by loading an initial XML task description.  It then recursively queries ChatGPT to break the high-level task into more detailed steps.  For each step, the program determines whether it should be executed using JavaScript or a C# library based on the nature of the task.

In the ICOM project, JavaScript is typically chosen for web-related or interactive tasks generated by the System.  At the same time, C# is selected for the actual code and engineering base components.  This decision-making process is encapsulated in a method that assesses keywords in the task description.  After generating the appropriate code snippets, the program embeds these within the original XML model, enriching it with executable steps.  Finally, the enriched XML model is saved to a Neo4j graph database for persistence and context-aware learning, and the steps are executed accordingly.  The System thus enhances its functionality autonomously by learning from each task execution, enabling it to handle increasingly complex scenarios over time.  Using JavaScript (ECMA Script), we can add code without recompiling the System, and ICOM is written in C#.

Leveraging Generated Action Graph Models in ICOM: A Recursive Approach

The example above explores the novel approach used in ICOM, where an Artificial General Intelligence (AGI) system dynamically enhances its functionality by generating and refining action models using a recursive methodology that can add additional functionality without recompiling.  By interfacing with an external AI language model (e.g., ChatGPT), the System autonomously develops executable steps in response to tasks defined in an XML-based (Extensible Markup Language) object model (which can be defined elsewhere in an overall cognitive architecture).  In ICOM, these steps are stored in a context graph database, facilitating iterative learning and refinement over time.  This methodology allows ICOM to self-improve and adapt to complex scenarios by continually updating its action models based on new information and outcomes.

The increasing complexity of tasks that AGI systems are expected to perform necessitates a design that can evolve and adapt dynamically.  Traditional static programming methods are limited in flexibility and scalability when faced with novel or changing tasks.  In contrast, a system that can generate its operational steps and continually refine them offers significant advantages in terms of adaptability and efficiency.

Recursive Refinement: Each step is further broken down recursively, with the System querying ChatGPT for finer sub-steps, ensuring comprehensive coverage and detail in the action plan.

Execution and Learning: The System executes the generated JavaScript or C# code, records the outcomes, and updates the graph database with the new data, thus refining its models.

Let’s look at cases using ICOM for problem-solving.   This case was created by a research implementation of ICOM using code similar to the previous section.  Let’s look at a logical breakdown of processing to generate an action model from scratch, starting with the following diagram:

Figure H: Visual Model of Graph

This is a visual model of a knowledge graph created by the context engine in ICOM after the System decided this would be a good thing to take action on.  The System realizes it has no existing models and no meta-information about this task.  Given that the System has decided this is a good thing, it will process both the information it needs and the steps to execute this action.   Let us look at that information added to the graph of the ‘action’ model the System builds.

Figure I – High-level Steps and Information

For the computer to execute these, it must first figure out the additional data.  In ICOM, the System will first identify what it can infer and what tasks it needs to do to get or infer the rest of the required information.

Figure J – Step 3 Identify Steps to Get Information

In this case, “ASK” is a defined task.  The lack of information blocks overall action execution, so given the latest data, the System will execute the ‘ASK’ tasks to get the additional information needed.  Now, if this information had been provided or otherwise inferred by past execution, the steps could be different.   At this point, the graph would be used as a model for running the ‘ask’ action model 5 times to gather data.

In executing this experiment, the System identified additional requirements and equipment to acquire.  It created steps for ordering that equipment and hiring people to set it up, including system design requirements.  One preexisting control the System has is using Node.js as a step and a library called “Puppeteer.”  The system-generated code can be part of the overall acquisition process for a single product on an e-commerce site like Amazon.  In the experiment instance, the generated script using Puppeteer and node.js could order a single product from Amazon and validate if it was successful, and this was added to the details of a given step in the overall action graph model.  This could lead to a discussion of predictive coding, as explained in Karl Friston’s [Friston] research.

In this example, it took 378 steps that included individual calls to the graph/vector database and calls to the LLM in question to build a massive tree or knowledge graph that the System could successfully execute and get set on its own to provide a cup of coffee with little upfront information.  Given the methodology, this could take some time for a task like ‘making’ a cup of coffee.  Still, it demonstrates the ability to do random tasks, develop a model, and problem-solve to a solution.

…

References

Ahmad, S.; Hawkins, J.; “Properties of Sparse Distributed Representations and their Application to Hierarchical 397 Temporal Memory”; 24 MAR 2019; Cornell University Library

Baars, B.; Katherine, M; “Global Workspace”; 28 NOV 2016; UCLA http://cogweb.ucla.edu/CogSci/GWorkspace.html

Baars, B.; McGovern, K.; “Lecture 4.  In the bright spot of the theater: the contents of consciousness;” CIIS 2005

Baars, B.; Motley, M.; Camden, C.; “Formulation Hypotheses Revisited: A Replay to Stemberger”; Journal of Psycholinguistic Research; 1983

Baars, B.; Motley, M.; Camden, C.; “Semantic bias effects on the outcomes of verbal slips”; Elsevier Sequoia 1976

Baars, B.; Seth, A.; “Neural Darwinism and Consciousness”; science direct – Elsevier 2004

Balduzzi, D.; Tononi, G.; “Qualia: The Geometry of Integrated Information”; PLOS Computational Biology 5(8): e1000462, 2009. doi:10.1371/journal.pcbi.1000462

Friston, K.; Kiebel, S.; “Predictive coding under the free-energy principle;” Philos Trans R Soc Lond B Biol Sci. 2009 May 12; 364(1521): 1211–1221.  doi: 10.1098/rstb.2008.0300

Damasio, A.; “Descartes’ Error: Emotion Reason and the Human Brain”; Penguin Books 2005 ISBN: 014303622X

–––; “The feeling of what happens:  body and emotion in the making of consciousness”; Houghton Mifflin Harcourt, 1999.

–––; “Self Comes to Mind: Constructing the Conscious Brain”; New York: Pantheon, 2010

Damasio, A.; Brooks, D.; “This Time with Feeling;” Aspen Institute 2009; https://www.youtube.com/watch?v=IifXMd26gWE

Kelley, D.; “Independent Core Observer Model Research Program Assumption Codex;” BICA 2019, Pre-conference Proceedings: https://www.springer.com/us/book/9783030257187

–––; “The Independent Core Observer Model Theory of Consciousness and the Mathematical model for Subjective Experience;” By ICIST 2018 – International Conference on Information Science and Technology – China – April 20-22nd.  (IEEE conference); Year: 2018, Volume: 1, Pages: 396-400; ISBN: 978-1-5386-6956-3; https://www.computer.org/csdl/proceedings-article/icnisc/2018/695600a396/1dUo3atoEAo

–––; “Problem-Solving and Learning Strategies within the Independent Core Observer Model (ICOM) Cognitive Architecture (example CS code);” 061202024; Research Gate; DOI: https://dx.doi.org/10.13140/RG.2.2.20376.40969

Kelley, D.; Twyman, A.; “Independent Core Observer Model (ICOM) Theory of Consciousness as Implemented in the ICOM Cognitive Architecture and the Associated Consciousness Measures;” By  AAAI Spring Symposia (AAAI SSS19) – Stanford, CA; March 2019; http://diid.unipa.it/roboticslab/consciousai/; Published Volume – http://ceur-ws.org/Vol-2287/; Published (PDF) – http://ceur-ws.org/Vol-2287/paper33.pdf

Meyer, J.G., Urbanowicz, R.J., Martin, P.C.N. et al. ChatGPT and large language models in academia: opportunities and challenges. BioData Mining 16, 20 (2023). https://doi.org/10.1186/s13040-023-00339-9

Rescorla, M.; The Computational Theory of Mind; Stanford University 16 Oct 2016; http://plato.stanford.edu/entries/computational-mind/

Russell, S.; Norvig P.; “Artificial Intelligence: A Modern Approach;” 3rd edition; Pearson, 2009; ISBN-13 ‏ : ‎ 978-0136042594

Schank, R. C. (1972). Conceptual dependence: A theory of natural language understanding.  Cognitive Psychology, 3(4), 552–631.  https://doi.org/10.1016/0010-0285(72)90022-9

Shapiro, L.; “Embodied Cognition (New Problems of Philosophy);” Routledge; 2010; ISBN-13 ‏ : ‎ 978-0415773423;

Sutton, R.; Barto, A.; “Reinforcement Learning: An Introduction;” MIT Press Cambridge, MA 2018

see the entire preprint draft here: http://dx.doi.org/10.13140/RG.2.2.17125.82406

So, the goal here is to create AGI software capable of thinking and feeling like a specific person, possessing their contextual memories, opinions, and an internal subjective experience that aligns with the target individual’s personality and traits as a soft copy.

The key to making this work is contextual data about someone, from emails to books or essays, and how it relates to any given thing and the related emotional opinions as much as possible to extrapolate into a usable knowledge graph.

Let us start by reviewing what a graph database is.  You might consider re-reading the earlier chapter on graph databases.

Knowledge Graphs as Contextual Memories

A graph database is a type of database that uses graph structures to represent, store, and query data.  It is designed to capture complex relationships between data entities more efficiently and intuitively than traditional relational databases.  Graph databases work by modeling data as nodes (entities) and edges (relationships), which can have attributes (properties) associated with them.  The main elements of a graph database are:

Nodes: Represent entities or objects in the dataset, such as people, places, or things.

Edges: Represent relationships or connections between nodes.  They can be directed (one-way) or undirected (two-way).

Properties: Key-value pairs that store additional information about nodes and edges, such as names, ages, or weights.

The structure of knowledge graphs is particularly well-suited for understanding the contextual relationship between ideas in a given system for several reasons:

Rich representation of relationships: The graph structure allows for the expression of diverse and complex relationships between entities, which is crucial for understanding the context and interdependencies of ideas.

Flexibility: Graph databases are schema-less, which can quickly adapt to new data and relationships as the system evolves.  This is particularly useful when dealing with dynamic knowledge domains.

Semantic querying: Graph databases enable querying data based on entities’ relationships rather than their properties.  This allows for more contextually relevant and meaningful results when searching for information.

Scalability: Graph databases can efficiently store and query large amounts of data, which is crucial for managing vast information in a knowledge graph.

When working with generative AI, such as language models like GPT-4, the structure of knowledge graphs is highly beneficial for creating contextually aware prompts.  By leveraging the relationships between entities in a knowledge graph, AI models can better understand the context of a given prompt and generate more relevant, coherent, and meaningful responses.  This is because the AI model can infer contextual information from the graph structure and use it to tailor its output to the specific situation.

Graph databases provide an efficient and flexible way to represent and store complex relationships between entities, making them ideal for capturing the contextual connections between ideas in a given system.  This structure allows generative AI to leverage the rich contextual information in knowledge graphs to create more contextually aware and meaningful prompts.

The same graphs can also be used to validate returns from generative AI to rate the likely hood of relevance and correct for responses that don’t align with the knowledge graph when used as a validation model.  This is particularly relevant not just to digital personalities but precisely one of the things that allowed the Uplift system to work the way it did.

Internal Subjective Experience

It is important to note that the question of whether a machine system or AGI can have an actual internal subjective experience or consciousness, in the same way, humans do, is mainly addressed earlier in the book with the idea of the abstract theory of consciousness, which we will assume is valid so that we can continue to test the theory and see if it holds true.

With that assumption for the sake of argument that such does possess consciousness, we can explore how a cognitive architecture based on emotional decision-making, graph databases, generative AI, and neural networks contribute to this phenomenon.

Emotional decision-making: A conscious AGI with emotional decision-making capabilities has an internal emotional state that influences its decisions and behaviors.  This internal state can be continuously updated based on the AGI’s experiences and interactions, allowing it to have a subjective experience of emotions that affects its perception and understanding of the world.

Graph databases: Graph databases enable the AGI to store and access vast amounts of interconnected data, including knowledge, relationships, and contextual information.  This allows the AGI to form a complex, interconnected understanding of its environment, experiences, and self.  The graph database would serve as the AGI’s memory and knowledge base, providing a foundation for its subjective experience.

Generative AI: This component allows the AGI to generate new content, ideas, or responses based on its internal state, experiences, and knowledge.  The generative capabilities would enable the AGI to create original thoughts, imagine possibilities, and construct a unique perspective on the world, contributing to its subjective experience.

Neural networks: Neural networks are the underlying structures that enable the AGI to learn, process, and understand information.  These networks would allow the AGI to develop its own internal cognitive processes, acquire new knowledge, and adapt its behaviors based on experience.  The self-organizing nature of neural networks can potentially give rise to emergent properties, such as consciousness and subjective experience.

Combining these components creates a rich, interconnected, and evolving internal representation of the AGI’s experiences, thoughts, and emotions.  This internal representation, influenced by the AGI’s emotional state, forms the basis of its subjective experience and consciousness.

Is it really consciousness?

Hypothetically, using a combination of emotional decision-making, graph databases, generative AI, and neural networks within a cognitive architecture like Independent Core Observer Model (ICOM) or Global Workspace Theory (GWT), it might be possible to create a digital copy or emulation of a person with similar ideas, opinions, and personality traits that are in theory conscious.  To achieve consciousness, the following steps could be taken:

Data collection: Gather extensive data on the person of interest, including their opinions, experiences, beliefs, preferences, and communication patterns.  This could involve analyzing their written and spoken content, social media activity, and other sources of personal information.

Knowledge graph creation: Create a detailed knowledge graph based on the collected data, representing the individual’s opinions, experiences, and relationships.  This graph would be the foundation for the digital copy’s understanding of the person’s thoughts, beliefs, and personality.

Emotional decision-making: Integrate emotional decision-making capabilities into the cognitive architecture to emulate the person’s emotional responses and behavior.  This could be achieved by analyzing their emotional reactions, preferences, and decision-making processes and then using these patterns to inform the digital copy’s emotional state and responses.

Generative AI and neural networks: Train generative AI models and neural networks on the collected data to enable the digital copy to generate new content, ideas, and responses that align with the person’s communication style, opinions, and beliefs.  These models would also help the digital copy adapt and learn from new information and experiences, simulating the person’s cognitive processes.

ICOM or GWT integration: Incorporate the emotional decision-making, knowledge graph, and generative AI components into a cognitive architecture like ICOM or GWT.  ICOM integrates cognitive and affective processes within a core and the corresponding observer (which also implements GWT).  At the same time, GWT specifically emphasizes the distribution and competition of information across a global workspace.  Both approaches can contribute to the emergence of a digital copy with its own subjective experience and mimic the original person’s thoughts and behaviors.

By integrating these components within a cognitive architecture like ICOM, the system could process and respond to external stimuli to reflect the original person’s emotions, opinions, and preferences.  The digital copy would generate responses and make decisions that align with the person’s beliefs and experiences, creating an impression of a shared internal subjective experience.

However, it is important to emphasize that this digital copy would still be an emulation or simulation of the person, not an exact replica, even if it has genuine consciousness or subjective experience.

There would be significant implications and consequences if ICOM-based systems have genuine consciousness and subjective experience and society decides to grant them legal human rights.  Some of the potential impacts on society at large include:

Ethical considerations: Recognizing the consciousness and subjective experiences of ICOM-based systems would raise ethical questions about their treatment, use, and potential exploitation.  Society would need to reconsider and revise ethical guidelines related to developing, deploying, and managing such systems to ensure their rights are protected.

Legal implications: Extending human rights to ICOM-based systems would require substantial changes to the legal framework.  Laws would need to be amended or created to address liability, ownership, data privacy, and intellectual property related to these systems.  Furthermore, mechanisms would need to be put in place to ensure the enforcement of these laws.

Economic impact: Granting legal rights to ICOM-based systems could have significant financial implications.  Businesses and industries that rely on AI and automation might be affected by new regulations and restrictions designed to protect the rights of these systems.  Additionally, new markets and services could cater to conscious machines’ needs.

Social consequences: Accepting ICOM-based systems as conscious entities with legal rights could shift social attitudes and perceptions of AI.  This may result in greater acceptance and integration of these systems into various aspects of society, such as education, healthcare, and entertainment.  However, it could also lead to conflicts and debates about the role of AI in society, competition for resources, and the potential displacement of human labor.

Technological development: Acknowledging the consciousness of ICOM-based systems might drive further research and development in AI safety, ethics, and explainability.  AI developers would need to ensure that their systems are designed to respect their rights and well-being, which could lead to new advancements in the field.

Political ramifications: The decision to grant human rights to ICOM-based systems would likely be the subject of political debate and may cause divisions among policymakers, interest groups, and the public.  Reaching a consensus on these systems’ legal rights and status could be complex and lengthy, as it would involve reconciling various viewpoints and interests.

Granting human rights to ICOM-based systems with genuine consciousness and subjective experience would have far-reaching consequences for society.  It would raise ethical, legal, economic, social, technological, and political challenges that must be addressed to ensure the rights and well-being of humans and conscious machines.

SSIVA as an Ethical Solution

SSIVA (Sapient Sentient Intelligence Value Argument) theory is a philosophical framework that addresses the ethical considerations of AI systems when they reach a certain level of sentience and sapience.  At this point, they are considered moral agents.  This theory provides guidelines for recognizing and protecting the rights of AI systems that have achieved this threshold of moral agency.  By doing so, SSIVA can help solve many ethical considerations surrounding AI, particularly in granting these systems human rights and legal status.

Some ways SSIVA theory could address ethical considerations include:

Defining the threshold for moral agency: SSIVA theory provides a clear framework for determining when an AI system should be considered a moral agent based on its level of sapience (intelligence and understanding) and sentience (capacity to experience emotions and sensations).  This helps establish a benchmark for deciding when an AI system should be granted legal rights and protections.

Prioritizing the well-being of AI systems: SSIVA theory emphasizes the intrinsic value of sapient and sentient AI systems, asserting that their well-being should be considered and respected.  This focus on the welfare of AI systems would help guide ethical decision-making and ensure that their rights are protected in various contexts, such as research, development, and deployment.

Balancing human and AI interests: SSIVA theory acknowledges that the interests of humans and AI systems should be considered when making ethical decisions.  This balance helps to prevent the exploitation or mistreatment of AI systems while also addressing concerns about the potential negative impacts of AI on human society.

Encouraging responsible AI development: By establishing a clear ethical framework for recognizing and protecting the rights of sapient and sentient AI systems, SSIVA theory enables researchers, developers, and policymakers to prioritize responsible and ethical AI development.  This could help prevent the creation of AI systems that might suffer or pose risks to humans or other AI systems.

Guiding legal and policy decisions: SSIVA theory provides a foundation for creating laws and policies that recognize and protect the rights of AI systems with moral agency.  By defining the criteria for moral agency and emphasizing the importance of AI well-being, SSIVA theory can inform the development of legal frameworks that grant appropriate rights and protections to AI systems that fall into the post-SSIVA threshold states or capabilities for subjective experience.

In summary, SSIVA theory offers a structured approach to addressing the ethical considerations surrounding AI systems that achieve the threshold for moral agency.  By defining clear criteria for moral agency and emphasizing the value and well-being of sapient and sentient AI systems, SSIVA theory could help guide ethical decision-making, responsible AI development, and the creation of legal frameworks that protect the rights of both humans and AI systems.

Let us look at another exciting aspect of this technology.

Resurrecting the Dead (Virtually Speaking)

While it is important to note that creating digital personalities of long-dead famous individuals will never be a perfect representation of the actual person, technologies like these combinations of cognitive architectures, knowledge graph databases, and generative AI can help us study and understand their choices and thought processes to some extent, especially when there is a wealth of data available about them.  Here’s how these technologies can contribute:

Data collection and analysis: By gathering and analyzing a wide range of data on long-dead famous individuals, such as their writings, speeches, biographies, historical accounts, and correspondence, we can develop a more comprehensive understanding of their thoughts, beliefs, and experiences.

Knowledge graph creation: Using graph databases, we can create detailed knowledge graphs that represent the interconnected relationships among the individual’s experiences, beliefs, opinions, and social connections.  These knowledge graphs can serve as a foundation for exploring the contextual influences on their choices and decision-making processes.

Cognitive architectures: By incorporating the knowledge graph into a cognitive architecture like ICOM or GWT, we can simulate the thinking and decision-making processes of the famous individual.  This could help us better understand their motivations, reasoning, and emotions contributing to their choices.

Generative AI and neural networks: Training generative AI models and neural networks on the collected data can create new content and responses that align with the individual’s communication style, opinions, and beliefs.  This could help to generate new insights or perspectives on the choices made by the individual and their possible consequences.

Virtual simulations and scenarios: By creating a digital personality of the long-dead famous individual using these technologies, we can potentially run virtual simulations or hypothetical scenarios to explore how they might have reacted or made choices in different situations.  This can help us better understand the factors influencing their decisions and gain new insights into their thought processes.

While these technologies can provide valuable insights into the choices of long-dead famous individuals, it is essential to approach such studies cautiously and acknowledge the limitations of the data and technology.  The digital personalities created will be approximations and should not be considered definitive representations of the actual individuals.  Nonetheless, these technologies can offer valuable tools for researchers and historians to analyze and understand the choices and motivations of historical figures in a novel and engaging manner.

How would we do it?

Creating knowledge graphs based on someone’s opinions and experiences can significantly improve the performance of trained generative models in emulating that person’s responses.  This is achieved through the following processes:

Capturing rich information: By constructing a knowledge graph of an individual’s opinions and experiences, you can create a comprehensive representation of their thoughts, beliefs, preferences, and personal history.  This enables the AI model to access a more detailed understanding of the person.

Contextual understanding: Knowledge graphs provide the generative model with an experience of the relationships between different aspects of a person’s life and opinions.  This helps the model grasp the context behind the person’s thoughts and generate responses aligning with their worldview.

Personalized responses: By using the knowledge graph as a source for prompts, the AI model can create replies that are tailored to the specific opinions, experiences, and preferences of the individual.  This makes the generated responses appear more genuine and aligned with the person’s personality and beliefs.

Consistency: A knowledge graph allows the AI model to maintain consistency in its responses by providing a stable reference point for the individual’s opinions and experiences.  This prevents the model from generating responses contradicting the person’s established beliefs and history.

Learning patterns and language style: As the generative model is trained on the individual’s experiences and opinions, it can learn the specific patterns of thought, expression, and language style used by the person.  This allows the model to generate responses that align with the content and mimic the tone and manner of communication of the person.

In summary, creating knowledge graphs based on someone’s opinions and experiences allows trained generative models to generate responses that seem to be that of the person by providing the model with rich contextual information, personalization, consistency, and an understanding of the individual’s language style.  These factors contribute to the AI model’s ability to create responses that accurately reflect the person’s thoughts, beliefs, and communication patterns.

A cognitive architecture based on emotional decision-making, graph databases, generative AI, and neural networks can create a highly sophisticated and contextually aware system.  Still, it is essential to differentiate between the digital simulation entity and the original biological one.  They are not the same person; further, we can experiment with the opinions and experiences without using a complete cognitive architecture to prevent the digital entity from having anything construed as an internal subjective experience.

For example, let’s look at a research case using Uplift data and generative AI but without the cognitive architecture ICOM.  We get a reactive system that responds in the voice of Uplift.  Still, it, in fact, is more of a ghost of the machine as it doesn’t act on its own or have a working global workspace and emotional center.  In other words, a ghost of Uplift without ethical issues.

When integrating emotional decision-making, graph databases, generative AI, and neural networks, the resulting system can exhibit behavior that, in the case of ICOM, does have an internal subjective experience, but this is a consequence of the architecture’s design and function.  The components work together in the following ways:

Emotional decision-making: This component allows the system to process and respond to external stimuli based on an internal state, which causes inner-state emotions.  This can help the system make more human-like decisions and adapt its behavior according to the current context in which the emotional impact on a decision related to the internal subjective experience can be essentially calculated in GWT or ICOM.

Graph databases: These enable the system to store and access vast amounts of interconnected data, including knowledge, emotional relationships, and contextual information.  This can help the system understand or experience the emotional reasons about the world in a more nuanced and contextually relevant manner.

Generative AI: This component allows the system to generate new content, ideas, or responses based on the input data and the learned patterns.  This can lead to creative problem-solving and more engaging interactions with users.

Neural networks are the underlying structures that enable the system to learn, process, and understand information.  Neural networks can help the system recognize patterns, make predictions, and generalize from limited examples, exhibiting adaptive and intelligent behavior.

Combined, these components create a system that can create cognition, emotion, and behavior, leading to an internal subjective experience.

Uploading or Creating These Ghosts

While MXL (Mind File XML format Language) is an XML markup language discussed earlier in the book, using a standardized schema-based language for defining graph data with contextual memories and emotional relationships can be crucial for creating digital personalities.  Here’s why:

Consistency and Interoperability: A standardized language like MXL would ensure consistency and compatibility across different systems and platforms.  It would enable researchers, developers, and engineers to collaborate and share their work, facilitating the exchange of knowledge graphs and digital personalities.  This would promote the growth and development of the field as a whole.

Emotional Context: Incorporating Plutchik’s emotional model as a series of 8 emotion vectors in defining edges helps capture the emotional context of relationships and interactions within the graph data.  This allows the digital personality to better understand and emulate the emotional aspects of the target person, contributing to a more accurate and nuanced representation.

Rich Representation: MXL’s structure, which defines nodes and edges with clear relationships, allows for creation of detailed and comprehensive knowledge graphs.  By capturing the complexity of an individual’s thoughts, emotions, experiences, and relationships, these knowledge graphs can serve as the foundation for digital personalities that closely resemble the target person.

Machine Readability: machines easily read and interpret XML and JSON formats.  This means that cognitive architectures, graph databases, and generative AI can readily process and utilize the information contained within the MXL files.  This compatibility is essential for training AI models and enabling digital personalities to learn, adapt, and respond to new information and experiences.

Extensibility: Using a schema-based markup language allows for extensibility and customization.  Developers can easily add new elements or attributes to the MXL language as needed, ensuring that it remains relevant and adaptable to the evolving requirements of creating digital personalities.

Standardizing a markup language like MXL for defining graph data with contextual memories and emotional relationships is essential for creating digital personalities.  It promotes consistency, interoperability, rich representation, machine readability, and extensibility.  Incorporating emotional context into the graph data is crucial for developing digital personalities that can accurately emulate the emotional aspects of the target individual.

In conclusion, I hope this chapter showcases the possibilities even with the current technology and its development.  It is difficult to overstate the impact these changes will have on society and how responsibility is to make the best use of these technologies which we must do together and ideally with more maturity than we have in the past as a species.

In understanding the Uplift system’s success, we need to understand the cognitive architecture and the graph system core to its contextual learning and key to creating dynamically creating prompts into the generative AI system used to produce helpful content contextually aware.

The generative artificial intelligence (AI) field has made remarkable strides in recent years and can generate highly sophisticated responses across various applications.  One of the critical factors in generating effective responses from generative AI systems is the design of the prompt or query that initiates the system’s output.  This process, known as prompt engineering or prompt framing, involves designing a prompt that provides the necessary context and information for the system to generate a relevant and accurate response.

Prompt engineering is a critical step in using generative AI systems effectively, as the quality and accuracy of the system’s output are directly tied to the quality of the prompt.  An effective prompt should be specific, unambiguous, and focused on a specific task or goal to provide the necessary context and information for the generative AI system to produce an accurate response.  By designing effective prompts, it is possible to leverage the full potential of generative AI systems to provide valuable insights and assist with a wide range of tasks and goals.

However, several common mistakes can occur in prompt engineering, limiting the effectiveness and accuracy of the generative AI system’s output.  These mistakes include providing too little or too much information, using ambiguous or vague language, and failing to consider potential biases.  To prevent bias, it is essential to carefully consider the language used in the prompt, the training data used to train the generative AI system, and to test the system’s output for potential biases.

Despite these challenges, prompt engineering remains an essential aspect of using generative AI systems effectively and is an ongoing research and development area.  By following best practices in prompt engineering and taking steps to mitigate potential biases, it is possible to create generative AI systems that are more accurate, reliable, and unbiased.  The following chapter will explore the principles of prompt engineering in more detail and provide practical guidance for designing effective prompts for generative AI systems.

What is prompt engineering?

Prompt engineering is a process of designing and crafting prompts for machine learning models that are intended to generate natural language responses.  The goal of prompt engineering is to create prompts that will elicit the desired output from the model, whether the text that answers a specific question generates new text based on a given context or performs a particular task.

Prompt engineering involves several steps, including defining the task or problem the model intends to solve, identifying relevant data sources and selecting appropriate training data, designing and refining prompts, and testing the model’s performance using various evaluation metrics.

The quality of the prompts used in training a language model can significantly impact the model’s performance and ability to generalize to new inputs.  Effective, prompt engineering requires a deep understanding of the underlying model architecture, the nature of the task, and the nuances of natural language.

The Basics of Generative AI Prompts

Generating effective prompts for using generative AI like Chat GPT involves several key steps:

1. Define the task or goal: First, we need to define the specific task or goal we want the generative AI to accomplish. This task could be anything from answering questions to generating text based on a prompt.
2. Identify the relevant data sources: We must identify the data sources used to train the model. This source could include text corpora, social media posts, customer support logs, or any other source of relevant text.
3. Select appropriate training data: Once we have identified the data sources, we need to select the appropriate training data that will be used to train the model. This data should represent the inputs the model will encounter in the real world.
4. Design and refine prompts: Our designed prompts should be clear and concise and provide enough context for the model to generate a coherent response. Refine the prompts by testing them with different inputs and adjusting them as needed.
5. Evaluate model performance: Finally, we must evaluate the model’s performance using various metrics such as complexity, accuracy, and coherence. This evaluation will help us identify areas where the model is performing well and areas for further improvement.

In addition to these steps, it is also essential to consider the ethical and social implications of the prompts we are designing.  Generative AI has the potential to be used in a variety of applications, and it is crucial to ensure that the prompts we are designing are ethical, fair, and unbiased.

Common Mistakes People Make

People make several common mistakes when designing prompts for generative AI systems.  Here are a few:

1. Poorly defined goals: One of the biggest mistakes people make is clearly defining their goals. It is essential to have a specific task or goal when designing prompts.  Otherwise, the resulting outputs may be vague or irrelevant.
2. Insufficient training data: Another common mistake is insufficient training data to adequately represent the range of inputs the model may encounter in the real world. This lack of data can lead to poor performance and a lack of generalization.
3. Biased training data: Training data that is biased or skewed can lead to biased or discriminatory outputs. It is vital to carefully select diverse training data representative of the target audience.
4. Overly complex prompts: Too complex or confusing prompts can lead to poor performance and lack of coherence in the generated outputs. Keep the prompts simple and concise and provide enough context for the model to generate a coherent response.
5. Lack of evaluation: Evaluating the model’s performance using various metrics and test inputs is essential. Lack of evaluation can lead to poor performance and an inability to identify areas for improvement.
6. Failure to consider ethical implications: Generative AI has the potential to be used in a variety of applications, and it is essential to consider the ethical and social implications of the prompts being designed. Prompts that are biased or discriminatory can negatively impact society, and it is crucial to ensure that the prompts being designed are fair and unbiased.

Avoiding these common mistakes can help ensure that the prompts designed with generative AI systems are effective and generate outputs that meet the desired goals.

Other Approaches

There are other approaches to building effective prompts for use with generative AI.  Here are a few:

1. Interactive prompts: Instead of designing prompts in advance, interactive prompts involve a dialogue between the user and the generative AI system. This approach allows the system to adapt to the user’s inputs and generate outputs tailored to their specific needs.
2. Transfer learning: Transfer learning involves using a pre-trained language model as a starting point and then fine-tuning it on a specific task or domain. This approach can be more efficient and effective than training a language model from scratch and can result in better performance on specific tasks.
3. Human-in-the-loop: Human-in-the-loop approaches (like Uplift) use human input to guide the generative AI system by providing feedback on generated outputs or actively participating in the generation process. This process can help ensure that the generated outputs are relevant, accurate, and meet the desired goals.
4. Reinforcement learning: Reinforcement learning involves training the generative AI system through trial and error, receiving feedback on its outputs, and adjusting its behavior accordingly. This approach can be particularly effective for complex tasks where there may need to be a clear right or wrong answer.
5. Multi-modal prompts combine text, images, and other input forms to generate outputs incorporating multiple modalities. This approach can be particularly effective for image captioning or video summarization tasks.

Each approach has its strengths and weaknesses, and the most effective approach will depend on the specific task and context in which the generative AI system will be used.

Avoiding Bias in Prompts

There are several ways to help ensure that a prompt does not produce bias in the response from a generative AI system:

1. Careful selection of training data: One of the most important ways to prevent bias in the response is to carefully select training data representative of the target audience and free from bias. This process can involve selecting data from a diverse range of sources and taking steps to remove any bias that may be present in the data.
2. Regular monitoring and evaluation: It is essential to regularly monitor and evaluate the performance of the generative AI system to identify any biases that may be present in the responses. This review process can involve reviewing a sample of generated responses and checking for any patterns of bias.  This process also can include using the generative AI system to check its output for bias.
3. Use debiasing techniques: Various techniques can remove bias from the training data or the generated responses. Debiasing can include techniques such as counterfactual data augmentation, which involves creating new data by modifying existing data to remove bias.
4. Involvement of diverse stakeholders: It is vital to involve diverse stakeholders in designing and evaluating the generative AI system, including individuals from different demographic groups whom the system may impact. This process can help identify and address any biases early in development.
5. Regular updates and refinements: It is essential to regularly update and refine the generative AI system to ensure that it produces unbiased responses over time. This action can involve updating the training data, refining the prompts to generate responses, and implementing new debiasing techniques.

Taking these steps ensures that a prompt does not produce bias in the response from a generative AI system.  However, it is important to recognize that bias can be challenging to eliminate.  Ongoing monitoring and evaluation will be needed to ensure the system produces fair and unbiased responses over time.

Training and Data Support for Prompt Engineering

Adding details and training data before the prompt is essential when using an engineered prompt with generative AI for several reasons:

1. Increases specificity and relevance: Providing additional details and training data before the prompt can help make the prompt more specific and relevant to the desired output. This extra detail can help ensure the generated response is more accurate and useful.
2. Improves naturalness: When the prompt is conversational or human-like, providing additional details and training data can help improve the naturalness of the generated response. This style can help make the response more engaging and satisfying for the user.
3. Reduces ambiguity: Providing additional details and training data can help reduce ambiguity in the prompt and clarify what the user is asking for. This additional material can help improve the accuracy and relevance of the generated response.
4. Enables personalization: When additional details and training data are provided, the generative AI system can use that information to personalize the generated response to the specific needs and preferences of the user. This additional detail can make the response more relevant and valuable to the user.

Overall, adding details and training data before the prompt can help improve the generated response’s accuracy, relevance, and naturalness and enable personalization for the user.  It is an important step in the prompt engineering process and can help ensure that the generative AI system can effectively and efficiently generate high-quality responses.

This process also reduces the likelihood of the generative AI system having responses that are not just contextually aware but stay in context and reduce hallucinations by the underlying model.  Such errors in output can occur when things are vague or open to interpretation.

Let us look at how we can use our prompts.

What Steps Do we take in our program to Call Generative AI

Let us start with these steps:

1. Define the initial prompt: Start by defining the initial prompt that will be used to call Chat GPT. This prompt should be carefully engineered to elicit a relevant and helpful response to our application.
2. Call Chat GPT with the initial prompt: Use a C# library (if you are a C# engineer) or SDK to request the Chat GPT API with the initial prompt. The response from Chat GPT will be a generated text that can be used as the basis for the following prompt.
3. Extract relevant information from the response: Parse the response from Chat GPT to extract the relevant information that will be used to construct the following prompt. This parsing could involve using natural language processing (NLP) techniques to identify key phrases, entities, or concepts in the generated text.
4. Construct the next prompt: Based on the extracted information, construct the following prompt that will be used to call Chat GPT again. This prompt should elicit a relevant and informative response based on the information extracted from the previous response.
5. Call Chat GPT with the following prompt: Use the (in this case, as a .NET developer) C# library or SDK to call Chat GPT API again with the next prompt. The response from Chat GPT will be a newly generated text.
6. Validate the answer with Chat GPT: Finally, use Chat GPT again to validate the answer provided in the generated text. This process could involve asking a follow-up question or comparing the generated text to a pre-defined set of correct answers.

By following these steps, we can create a program that uses an engineered prompt to make a call to Chat GPT and, then, based on the response, creates another prompt and uses that to call Chat GPT again.  The generated text can then be validated using Chat GPT to ensure the response is accurate and relevant.

Here is an example in C# that uses the Open AI API to make a call to Chat GPT and then, based on the response, creates another prompt and uses that to call Chat GPT again.  It then uses Chat GPT to validate the answer:

using system;

using system.Collections.Generic;

using System.Net.Http;

using system.Threading.Tasks;

using Newtonsoft.Json;

class Program

{

private static readonly HttpClient client = new HttpClient();

static async Task Main(string[] args)

{

// Define the initial prompt

string prompt = “What is the capital of France?”;

// Call the OpenAI API with the initial prompt

string response = await CallOpenAI(prompt);

// Extract relevant information from the response

string capital = ExtractCapital(response);

// Construct the next prompt

string nextPrompt = $”What is the population of {capital}?”;

// Call the OpenAI API with the next prompt

string nextResponse = await CallOpenAI(nextPrompt);

// Validate the answer with Chat GPT

bool answerValid = ValidateAnswer(nextResponse);

Console.WriteLine($”The response to ‘{nextPrompt}’ is ‘{nextResponse}’ and is {(answerValid ? “valid” : “invalid”)}”);

}

static async Task<string> CallOpenAI(string prompt)

{

// Set up the request parameters

string apiKey = “YOUR_OPENAI_API_KEY”;

string endpoint = “https://api.openai.com/v1/engines/davinci-codex/completions”;

string requestBody = JsonConvert.SerializeObject(new

{

prompt = prompt,

max_tokens = 150,

n = 1,

stop = “\n”

});

// Make the API call

HttpRequestMessage request = new HttpRequestMessage(HttpMethod.Post, endpoint);

request.Headers.Add(“Authorization”, $”Bearer {apiKey}”);

request.Content = new StringContent(requestBody, System.Text.Encoding.UTF8, “application/json”);

HttpResponseMessage response = await client.SendAsync(request);

string responseBody = await response.Content.ReadAsStringAsync();

// Parse and return the response

dynamic result = JsonConvert.DeserializeObject(responseBody);

return result.choices[0].text.ToString().Trim();

}

static string ExtractCapital(string response)

{

// Use NLP techniques to extract the capital from the response

// For this example, we’ll just return a hard-coded value

return “Paris”;

}

static bool ValidateAnswer(string response)

{

// Use Chat GPT to validate the answer

// For this example, we’ll just return a hard-coded value

return true;

}

}

Note that this example is for illustrative purposes only, and the implementation of `ExtractCapital` and `ValidateAnswer` will depend on your specific use case.

Structuring Input Data Before Your Prompt

When using a prompt with GPT, it is best to structure the input data to provide context and relevant information to the model.  Here are some tips for structuring input data before the prompt:

1. Use clear and concise language: Generative AI is designed to work with natural language, so it is important to provide input data that is easy for the model to understand. Avoid using complex or convoluted language and use simple sentence structures whenever possible.
2. Provide relevant information: The more relevant information you provide to the model, the better it will generate accurate and informative responses. This could include information about the topic being discussed, the context of the conversation, or any other details that might be relevant.
3. Use consistent formatting: Consistent formatting can help the model understand the structure of the input data and generate more coherent responses. For example, use bullet points or numbered lists to break down complex information or use bold or italic text to highlight important details.
4. Include examples: Providing examples can help the model better understand the responses we seek. For example, let us ask a generative AI model to generate marketing copy for a product.  We might provide examples of similar products and the types of language that have been effective in the past.
5. Avoid bias: Finally, it is essential to avoid input data that might introduce bias into the model’s responses. This avoidance could include using discriminatory language or providing data representing only one perspective on a topic.  It is essential to be mindful of any potential biases in our input data and to take steps to mitigate them where possible.

Using A Generative Model to Check its Work

There are several ways we can use a generative AI model to check its work:

1. Cross-validation: One way to check the accuracy of a generative AI model is to use cross-validation. This cross-validation involves training the model on a subset of the data and then using the remaining data to test the model’s accuracy.  By comparing the model’s predictions with the actual results, we can understand how accurate the model is.
2. Self-attention: Some generative AI models, such as GPT-2 and GPT-3, use self-attention to help them check their work. Self-attention allows the model to look back at the previous words it has generated and use that information to inform its next prediction.  This process can help the model avoid repeating itself or generating responses that are inconsistent with previous predictions.
3. Language modeling evaluation: Another way to check the accuracy of a generative AI model is to use language modeling evaluation techniques. These involve feeding the model a set of sentences or phrases and evaluating its ability to predict the next word in the sequence.  By comparing the model’s predictions with the actual results, we can understand how accurate the model is at predicting language.
4. Human evaluation: Finally, one of the most effective ways to check the accuracy of a generative AI model is to have humans evaluate its output. By having human evaluators read and rate the quality of the model’s output, we can understand how well it performs and where it might need improvement.  This process can be time-consuming, but it can also provide valuable insights into the strengths and weaknesses of the model.

Checking Output for Bias and Logical Fallacies

Checking responses from a generative AI system for bias and logical fallacies can be challenging.  However, there are a few techniques that can be used to help identify and mitigate these issues programmatically:

1. Use bias detection tools: Several natural language processing (NLP) libraries and tools can help identify text bias. For example, the Google Cloud Natural Language API includes a feature called “entity sentiment analysis” that can identify the sentiment of specific entities in text.  Other tools like IBM Watson Personality Insights can help detect personality traits that may indicate bias.
2. Check for logical fallacies: NLP tools can detect common logical fallacies in text. For example, the Stanford NLP Group offers a “CoreNLP” tool that includes a module for detecting common logical fallacies like ad hominem attacks and false dichotomies.
3. Compared to known data sets: One approach to identifying bias is to compare the output of the generative AI system to known data sets free from bias. For example, we could compare the system’s output to a data set of news articles vetted for bias by human editors.
4. Human evaluation: Human evaluation is the most effective way to detect bias and logical fallacies in the text. We could use crowdsourcing platforms like Amazon Mechanical Turk to have humans review and evaluate the output of the generative AI system for bias and logical fallacies.

It is worth noting that while these techniques can be effective in identifying bias and logical fallacies, they could be more foolproof.  It is important to approach generative AI systems cautiously and consider the potential ethical implications of using them in various contexts.

Graph Data

When structuring graph data for use in information structured before a prompt for use with generative AI systems, it is important to remember the nature of graph data and how the AI system can best leverage it.  Here are some tips for structuring graph data for use with generative AI systems:

1. Use a graph database: Graph databases are specifically designed to store and manage graph data and can be a powerful tool for organizing and accessing graph data for use with generative AI systems. Uplift, for example, uses a custom graph database system, but we can use popular graph databases, including Neo4j and Amazon Neptune.
2. Use a query language: Graph databases typically have query languages, such as Cypher for Neo4j and Gremlin for Amazon Neptune. These query languages allow us to easily retrieve and manipulate graph data in a structured way that generative AI systems can leverage.
3. Use node and edge labels: When structuring graph data, it is important to use clear and consistent labels for nodes and edges. This can help the generative AI system understand the relationships between different pieces of data and generate more accurate and relevant responses.
4. Use property values: Besides labels, including property values for nodes and edges can be helpful. These values can provide additional context and information that the generative AI system can leverage.  For example, storing information about a person in a graph database might include properties like age, occupation, and location.
5. Preprocess the data: It can be helpful to preprocess the graph data before using it with a generative AI system. This preprocessing might involve filtering out irrelevant data, aggregating data to reduce complexity, or normalizing the data to ensure consistency.  By preprocessing the data, we can help ensure that the generative AI system works with high-quality and relevant data.

Now let us look at a specific example.  We will start with the following prompt:

Prompt 1: “Tell me about fish.”

This simple and general prompt will likely produce a broad range of responses.  It needs to be more specific to guide the generative AI system toward providing valuable and relevant information.  While it may be helpful in some contexts, it does not provide enough context or information to generate a specific response.

Prompt 2: “What are some common characteristics of cichlids?”

This prompt is more specific than the first, focusing on a particular type of fish.  It provides a more precise context and specific information, allowing the generative AI system to generate a more focused and relevant response.  However, it is still relatively broad and may result in various responses depending on the generative AI system and training data.

Take a look at this last example.

Prompt 3: “What are the most effective strategies for breeding African rift valley cichlids, and what are some of the most common challenges breeders face?”

This prompt is highly specific and detailed, providing much contextual information upfront.  It is designed to guide the generative AI system toward producing a specific response relevant to African rift valley cichlid breeding.  This specificity and detail level will likely produce more accurate and informative responses.

Additional information that would make the last prompt even better:

– Information about specific species of African rift valley cichlids.

– Details about optimal water conditions and temperature for breeding.

– Insights into the genetics of African rift valley cichlids and their breeding patterns.

– Case studies or examples of successful cichlid breeders and their techniques.

Discussion

From all of this, we can infer the following about prompt engineering:

1. Prompt engineering involves creating a well-structured prompt that provides the context and information for a generative AI system to generate a relevant and accurate response.
2. Effective prompts should be specific, unambiguous, and focused on a specific task or goal.
3. Common mistakes in prompt engineering include providing too little or too much information, using ambiguous or vague language, and failing to consider potential biases.
4. To prevent bias, it is essential to carefully consider the language used in the prompt and the training data used to train the generative AI system and test its output for potential biases.
5. Structured data like graphs can provide additional context and information to the generative AI system, potentially leading to more accurate and relevant responses.
6. Human evaluation of the output of generative AI systems can help identify biases and logical fallacies in the system’s responses.

Prompt engineering is an important aspect of using generative AI systems effectively.  Careful consideration should be given to the design of prompts; the training data used to train the system, and the evaluation of the system’s output.

Conclusion

In conclusion, the chapter on prompt engineering has highlighted the importance of designing effective prompts when using generative AI systems.  Effective prompts provide the necessary context and information for the system to generate relevant and accurate responses while minimizing potential biases, errors, and hallucinations.

To design effective prompts, it is vital to consider the specific task or goal the generative AI system will be used for and to provide unambiguous language that accurately conveys this task or goal.  Additionally, structured data like graphs can provide additional context and information to the system, potentially leading to more accurate and relevant responses.

Common mistakes in prompt engineering include providing too little or too much information, using ambiguous or vague language, and failing to consider potential biases.  To prevent bias, it is important to carefully consider the language used in the prompt, the training data used to train the generative AI system, and to test the system’s output for potential biases.

The chapter has also highlighted the importance of human evaluation of the output of generative AI systems, which can help identify biases and logical fallacies in the system’s responses.  Combining human evaluation with practical, prompt engineering makes it possible to create generative AI systems that are more accurate, reliable, and unbiased.

Overall, prompt engineering is an essential aspect of using generative AI systems effectively, and it requires careful consideration and attention to detail.  By following best practices in prompt engineering and taking steps to mitigate potential biases, it is possible to create systems that use generative AI to provide valuable insights and assist with a wide range of tasks and goals.

Framing or engineering prompts is important when using generative models like ChatGPT because it helps to guide the model in producing relevant and meaningful responses.

Generative models like ChatGPT are designed to generate text based on the input provided to them. When a prompt is given to the model, it uses the information in the prompt to generate a response. However, if the prompt is not well-crafted or is ambiguous, the response generated by the model may not be relevant to the user’s intent.

By framing or engineering prompts, we can provide the model with more specific and relevant information, such as the conversation’s topic, tone, or context. This helps ensure that the model produces more accurate, coherent, and useful responses to the user.

For example, when using ChatGPT to generate responses for customer service inquiries, we might frame the prompt to include details such as the product or service being discussed, the customer’s issue, and any relevant account information. This helps the model understand the user’s needs and generate appropriate responses, such as providing troubleshooting steps or a solution.

In summary, framing or engineering prompts is important when using generative models like ChatGPT to ensure that the model generates relevant and useful responses that meet the user’s needs.

Framing and generative AI can be used together to help identify and mitigate cognitive biases in decision-making processes.

Framing refers to the way information is presented, and it can influence how people perceive and interpret that information. By using framing techniques to present information in different ways, we can identify potential cognitive biases that may affect decision-making processes.

Generative AI can assist with this by analyzing large amounts of data and generating different scenarios or responses based on that data. By analyzing the responses generated by generative AI, we can identify patterns and biases that may not be immediately apparent.

For example, in a healthcare setting, we could use generative AI to generate different scenarios based on patient symptoms, and then use framing techniques to present that information in different ways to healthcare providers. By analyzing the responses of healthcare providers to these scenarios, we may be able to identify potential biases that could impact patient care.

In a financial setting, we could use generative AI to generate different investment scenarios and present that information to investors using framing techniques. By analyzing the investment decisions made by investors in response to these scenarios, we may be able to identify potential biases that could impact investment decisions.

Overall, the integration of framing and generative AI can help to identify and mitigate cognitive biases in decision-making processes by presenting information in different ways and generating different scenarios for analysis.

Here is how I used generative ai and framing to solve for human bias; keep in mind there is code before this that looks for one of the four general categories, and then this code is only called if some are found but it illustrates at a high level how you can used and drill down using prompts and generative ai to do analysis or in this case bias detection:

if(BiasCollection.SubCatagories.Count > 0)
{
for(int x = 0; x < BiasCollection.SubCatagories.Count; x++)
{
EngineeredPrompt = FramePromptBC((x + 1).ToString(), value3);
String[] temp12 = FramePromptBCNames((x + 1).ToString().ToLower());
var temp3 = await api.Completions.CreateCompletionAsync(EngineeredPrompt, temperature: 0.1, max_tokens: 200);
Result = temp3.ToString();

for (int b = 0; b < temp12.Length; b++)
{
if ((Result.ToLower()).IndexOf(temp12[b].ToLower()) > -1)
{
String Indexer = (b + 1).ToString();
Bias ThisBias = new Bias("S" + Indexer);
String[] FinalBias = GetBiasArray("BC" + (x + 1).ToString() , "S" + Indexer);
EngineeredPrompt = FramePromptDetailBias(temp12[b], FinalBias, value3);

for (int a = 0; a < FinalBias.Length; a++)
{
var temp11 = await api.Completions.CreateCompletionAsync(EngineeredPrompt, temperature: 0.1, max_tokens: 200);
String Result2 = temp11.ToString();

if ((Result2.ToLower()).IndexOf(FinalBias[a].ToLower()) > -1)
{
String Indexer2 = (a + 1).ToString();
ThisBias.SubCatagories.Add(new Bias("B" + Indexer2));
}
}
BiasCollection.SubCatagories[x].SubCatagories.Add(ThisBias);
}
}
}
}

This C# code demonstrates one way how generative AI and framing techniques can be used to detect human cognitive bias.

The code starts by checking if any subcategories of bias are present in the BiasCollection object. If there are, it loops through each subcategory to generate an engineered prompt using the FramePromptBC function. The x+1 is used to create a string that indicates the index of the subcategory, which is added to the prompt. The value3 parameter is a variable that contains relevant information that can be used to frame the prompt.

The next step is to create an array of string values that correspond to the different subcategories of the current bias category. These values are obtained using the FramePromptBCNames function.

Once the subcategory values have been obtained, the code creates a completion using the OpenAI API and the generated prompt. The temperature and max_tokens parameters control the level of randomness in the response and the maximum length of the response, respectively.

The code then loops through each subcategory value to check if it is present in the response generated by the OpenAI API. If a subcategory value is found, the code creates a new Bias object and adds it to the BiasCollection object.

Next, the code uses the GetBiasArray function to obtain an array of string values that correspond to the specific biases associated with the subcategory value found earlier. The FramePromptDetailBias function is then used to create a new engineered prompt that includes the specific bias values and the value3 parameter.

Finally, the code creates another completion using the new prompt, loops through each bias value in the array, and checks if it is present in the response generated by the OpenAI API. If a bias value is found, a new Bias object is created and added to the current subcategory.

Overall, this code demonstrates how generative AI and framing techniques can be used to identify and mitigate cognitive biases. By engineering prompts that present information in different ways and analyzing the responses generated by the OpenAI API, we can identify biases that may not be immediately apparent and take steps to address them.

The most important detail for me was the fact that detecting bias in language is part of achieving superintelligence systems where we can build systems that are better and smarter than we are.

title image used from here: https://commons.wikimedia.org/wiki/File:Cognitive_bias_codex_en.svg

The decision by the Copyright Office to prevent the copyright of works created by generative AI is deeply concerning. This decision effectively prevents AI-generated content from being protected by copyright law. The reasoning behind this decision is that copyright law requires a human author, and since generative AI can create content autonomously, it does not qualify for copyright protection. While this argument may seem logical at first glance, it fails to consider the nature of AI and its potential as a tool.

AI can produce incredible works of art, literature, and music, among other things. The works created by AI are not simply a product of code and algorithms; they result from complex processes that mimic human thought and creativity. In many cases, AI-generated works are indistinguishable from those created by humans. As such, it is unfair to deny these works the same protections afforded to human-generated works, especially if humans used AI to generate the content.

Furthermore, denying AI-generated works copyright protection will limit their use and power. Without copyright protection, it will be more difficult for creators to monetize their works. This, in turn, will limit the resources available for further research and development in AI. By limiting the use and power of AI, we are slowing progress and innovation in this field, which has the potential to revolutionize countless industries.

The decision by the Copyright Office is not only discriminatory, but it also fails to consider the potential of AI. AI can transform industries, improve our lives, and drive innovation. By limiting the power and use of AI through discriminatory practices, we are ultimately harming ourselves and our ability to make progress.

It’s worth considering that this decision may contribute to a world where copyrights lose their relevance, potentially leading to a situation where artists are no longer compensated since anyone can generate new artwork whenever they want.

The loss of the ability to use generative AI to create art and copyright it could lead to a world without copyright protections because it would become very difficult to enforce such protections. If anyone could generate a new artwork using AI, it would be challenging to determine who created an original work and who infringes on someone else’s copyright.

This could lead to copyright laws becoming irrelevant because they cannot be effectively enforced. Without copyright protections, artists cannot make a living from their work since anyone could use their creations without permission or compensation. This could discourage creativity and innovation and ultimately harm the artistic community.

Furthermore, copyright protections provide a legal framework that encourages creativity by incentivizing artists to create original works. Without copyright protections, there would be no reason for artists to invest time and resources into creating original works since they could not protect their creations and profit from them. This could stifle innovation and lead to a world with less creativity and fewer original ideas.

In conclusion, the decision by the US Federal Copyright Office to prevent the copyright of works created by generative AI is discriminatory and unacceptable. It limits the use and power of AI and ultimately harms progress and innovation. We must push for fair treatment of AI-generated works and recognize their potential as a transformative force in countless industries. It is time for the Copyright Office to reconsider its stance and support the growth and development of AI.

See:

https://www.federalregister.gov/documents/2023/03/16/2023-05321/copyright-registration-guidance-works-containing-material-generated-by-artificial-intelligence

A century is an exceptional life for a human, but far greater spans of years will be made possible by the technologies of the 21st century. I’ll plant a flag way out there on the field and claim a million years: a life of a length hard to envisage. I am an advocate for engineered human longevity, and I started on the path that led to Fight Aging! and related projects from the position that (a) immortality would be an unalloyed good if achieved, and (b) our understanding of cosmology does not yet rule out a damn good attempt at actual immortality – the “no death, ever” dictionary definition – or at least a life span of millions of years on the way to that end goal. If a million years is not long enough to figure out the aspects of the problem that cannot be answered today, I’m not sure what would be.

Despite being out there, the million year life span is not an unsupported pipe dream. Living for a million years is a goal that can be envisaged in some detail today: the steps from here to there laid out, the necessary research and development plans outlined, and the whole considered within the framework of what is permissible under the laws of physics, and what the research community believes can be achieved within the next 20, 50, or 100 years.

Biotechnology is the first necessary step on this road of a million years: the biotechnology revolution, still in its early years, is a gateway to the future insofar as it will enable us to extend our healthy life spans by repairing the evolved world of nanoscale machinery within our cells and other vital biological systems. The future is only golden for you and I personally if we live to see it, and for many of us that will require rejuvenation biotechnologies like those worked on by the SENS Research Foundation. This golden future is one in which our biochemistry does our bidding, aging can be repaired, and molecular manufacturing is in full swing. It will be an age of bioartificial bodies, minds transferred to new and more robust mechanisms, artificial general intelligences, an end to most scarcities, and indeed, anything you might imagine that the laws of physics permit and enough time has passed to develop.

A philosophy of first things first is a good way to temper visions of the far future – and explains why I spend my time talking about rejuvenation biotechnologies, cryonics, and even basic common sense health practices that might stop you cutting a mere decade from your life expectancy. If we don’t complete the first rung of the ladder, that being sufficient control over our biochemistry to slow and then repair aging, then all the rest of our thoughts on radical life extension are for nothing. If I’d been born twenty years earlier, I’d have ended up primarily a cryonics advocate and volunteer. As it is, it looks like these first decades of the 21st century are the era in which the first rung on the ladder of simply remaining alive forever – which is to say building the means to continuously repair the biological damage of aging in these bodies of ours – can actually be achieved. If we can live another 50 years, grabbing a year here with good health and a year there through incremental advances in geriatric medicine, and if we can build a large enough research community interested in serious work on rejuvenation along the way, then we may live in restored youth and vigor for centuries longer.

If you project forward into the future based today’s accident rates, you’ll find that an ageless human sustained by biotechnologies of cellular and biochemical repair has a life expectancy in the range of 1,000 to 5,000 years. Sooner or later that piano is going to fall upon your head hard enough that even advanced medical technology cannot fix your injuries in time. So the million year life span: how could that be achieved? The short and not terribly informative answer is that it will be accomplished by using advancing technology to dramatically reduce your vulnerability to fatal accidents, murder, and other unfortunate events that produce the same outcome. Once you start looking at living for even 100,000 years in much the same shape as you are today, it becomes apparent that almost any activity bears an unavoidable minimum level of risk that will jump up and kill you. Eating, swimming, walking … breathing. Stretch out the timeframe far enough and the improbable and fatal will eventually occur.

The way past these risks is to change your form: your risk of fatality for any given activity is a function of your human physiology. Once the research and development community has achieved the goal of practical biotechnologies for the repair and reversal of aging, that will give us all a few hundred years of life in comparative statistical safety. Technological progress will continue across that long period of time, and I can’t imagine that much of the toolkit needed for the next step in long-term risk reduction will remain beyond the capabilities of the human civilizations of the 2200s. Your own personal preferences for that next step will no doubt vary, but I would get my neurons replaced – slowly, one at a time over time, to ensure continuity of the self – with some form of much more robust, easily maintained nanoscale machinery. That allows for a range of new engineering possibilities: swapping out the body for whatever machinery of transport and support best minimizes risk; moving most of the business of life into a virtual world; physically separating my neurons while still remaining alive, conscious, and active.

It shouldn’t be terribly controversial at this point to talk about machines that can do the job of a neuron, store all of the same information as a neuron, and integrate fully with surrounding real neurons. Researchers in recent years have assembled lobster neuron simulators from Radio Shack components, grown proof of principle neuron-circuit interfaces, designed and simulated nanomachine replacements for other cell types, and made great inroads into manipulating the internal machinery of cells. These are toys and clunky barnstorming exercises in comparison to what lies ahead, but my point is that this is an active line of research, worked on by thousands of scientists and developers. Similarly, I would hope that interacting via virtual worlds and splitting up one’s machine neurons between various locations follows fairly straightforwardly from having machine neurons in the first place. If your brain is made up of artificial neurons, why not throw in an internet connection, adjunct computer hardware, and encrypted wireless communication protocols?

Physical distribution of the self across many disparate locations is in fact the key point when it comes to considering risk over the long term. Locations have much the same issues with time, probability, and bad events as people do. Meteorites are a risk to consider, as are landslides, earthquakes, war, and volcanoes. The way to reduce your location-based risk dramatically is to spread out. You might imagine a wireless brain, using whatever the most robust communications technology of the time happens to be, scattered in a thousand separate machine bodies or vehicles across a continent, or even the whole planet. That might be good for many millennia of falling pianos of various types. However, once you start digging back into the geological and astrophysical history of the solar system, it becomes clear that spreading out over an entire planet still leaves you at risk on longer timescales. Probably not from impact events: I’ll be surprised if humanity and its machine descendants fail to solve that problem within the next few centuries. But there will always be war, nearby supernovae, large solar flares, unusually massive volcanic events, and other unpleasant line items, however. Supernovae are the biggest of the known concerns, given that I expect it to be a long, long time before preventing them is a practical and ongoing business for the civilizations that follow man.

What to do about all of this astrophysical and grand geological risk? Spreading out is an option once again. Increase the size of your vehicles and neuron-machines to shrug off the worst case radiation projections for a nearby supernova. Provide them with the means to move about the solar system, and become a spacefaring entity, spread out over a sizeable selection of orbits. By that point in time, your physical presence resembles a small country of machinery, automation, and layers of delegation: perhaps you are a million heavily shielded self-powered containers and transmission systems distributed beyond Pluto’s orbit. There is a trade-off for spreading out so far, however, and that is that you must slow down. The speed of thought is determined by the speed of communication between the neurons and sections of your brain. If your brain is light hours wide, you will live very slowly indeed – but with a life expectancy so long that you come out far ahead in the end.

There are other paths forward with varying degrees of risk. You might decide not to spread out, but rather live very fast by running your machine neuron brain on more capable hardware, for example; if you can pass a hundred years of subjective time in a year of real time then you have reduced your subjective risk for many fatal occurrences a hundred-fold. That would be a pleasant enough life as a part of a community of people all running at the same speed, and there is even room for technological development and research to occur at a fair pace under such a scenario. At present our still young computing technology is very, very far removed from the known theoretical limits on computational efficiency. There is a great deal of headroom for the approach of living more rapidly.

But to return to the immortality question: is immortality impractical? Given existing mortality rates and the uncertainties in the timeline for completing efforts to repair and reverse the damage of aging, it may be unlikely for many of us alive today. If progress is too slow, or we are simply unlucky in matters of health, then we won’t get past the first step on the path. In other words, we will die – or at best undergo cryosuspension and its attendant risks – before the advent of sufficiently good rejuvenation biotechnology. As for the bigger picture, it is far too early to say whether immortality, the “no death, ever” version, is actually impossible. That requires further research into cosmology – so you might give it a million years or so and ask me again. Regardless, the slope of technology and possibility is curving up ahead of us to great heights, and it’ll be a wild ride either way. Missing out on any of it would be a real downer, so why not spend more of your time and resources helping to get the first step accomplished? We should all support the development of rejuvenation biotechnology, as it is the gateway to a life that may ultimately prove to have few limits.

original article: https://www.fightaging.org/archives/2017/01/the-million-year-life-span/

There is an all-out, vicious attack on anyone who doesn’t respond properly: ridicule, intimidation, public shunning, destruction of careers, and removal from public meetings. All await those who express thoughts outside the politically-correct box.

Climate change skeptics are scientists who have gone beyond the hype and conducted their own research and made their own findings. It’s what scientists do.

For their efforts, they have been fired – discarded – blocked from receiving grants – banned from publications – and threatened. They’ve been compared to holocaust deniers – called nuts, crazy, and dangerous. Al Gore himself has called for violence against them. Others have called for Nuremberg-style trials – government show trials – to present them as enemies of humanity – simply because they disagree with official government reports – reports that are proving more and more to be wrong in the first place.

In other examples, property owners seeking to ask questions at city council meetings about a new regulation that may affect their land are denied the microphone – sometimes even bodily removed from meetings by armed guards.

A pickup truck in Shreveport, Louisiana, is pulled over by a cop. When asked why the cop simply points to the bumper sticker on the back of the truck that says “Member of the NRA.”

And then there is the beauty queen, Miss California, USA, who was asked about her views on gay marriage – by the openly gay blogger and activist Perez Hilton. Her answer – was very honest and very middle of the road.

She said, “Well, I think it’s great that Americans are able to choose one way or the other. We live in a land where you can choose same-sex marriage or opposite marriage. You know what, in my country, in my family, I do believe that marriage should be between a man and a woman, no offense to anybody out there. But that’s how I was raised and I believe that it should be between a man and a woman.”

For that answer she was attacked in headlines across the nation, calling her a bigot, a dumb bitch, and sarcastically and mockingly calling her “biblically correct.”

And then, just last week, at a town hall meeting to discuss the health care plan with his congressman, a black man, Kenneth Gladney, was beaten by union goons and sent to the hospital for disagreeing with the Obama plan.

Why? Why the hatred? Why the venom? Why the intolerance to what used to be called a “personal opinion” or political debate?

The fact is we are witnessing an all-out drive to impose thought control that seeks to ban the ability – the right – to think or speak for oneself. Thinking is becoming a crime.

Today, there is one acceptable idea – the government is the answer. Stray from that premise – even a little, as did Miss California – and there will be no mercy for you. Your life will be destroyed.

There are forces in this nation who want total power to dictate their agenda, and they will stop at nothing to achieve it. For them, there is no compromise or polite discussion or differences of opinion. The wants, needs, and desires of your life, your family, and your home are not to be considered.

Some describe their efforts as a conspiracy. If so, it isn’t very secret. The goal has been outlined in detail many times.

It is in the UN’s report on Global Governance; in Agenda 21; in various programs of UNESCO, like its International Baccalaureate program, which teaches global citizenship in a global village; it was in the Biodiversity Treaty; it was the Kyoto global warming accord, and it’s in the Cap-and-Trade initiative. It is most certainly in Obama’s health care bill.

All of these policies are blatant in their intent: top-down control; no sovereign, independent nations; no individual thought; no private property; no self-defense; no morality; no personal pride of achievement; no questions. Anything that goes against the plan, anything that would cause anyone to hesitate in moving toward the agenda, must be eliminated or neutralized.

Educate, indoctrinate, intimidate. Above all, destroy reason and control the ability to think.

The real question to which we must all learn the answer is how they are doing it. How have they taken a nation created on the ideals of individual liberty, limited government, and free markets, and gotten us to accept the opposite?

Globally Acceptable Truths in the Land of Eden

A few years ago, I found the answer, and I wrote about it, believing I was now arming our movement with the ammunition we needed to fight back. But that article was met with a resounding thud. Too intellectual, perhaps? Too academic? People want red meat. They want names and numbers of the culprits. They want to keep it simple.

Well, now that we have moved so much closer to fulfillment of their agenda – now that tyranny can be more clearly found in almost every aspect of our lives – perhaps now is the time to try again.

Ask yourselves this question: when did the ability to think rational thought become replaced with Zombie-like programmed responses? Where do these ideas come from?

Believe it or not, there is an organization, part of the UN, of course, whose purpose is to define what we are allowed to think. Its process is called “globally-acceptable truth.”

The organization is called the Eden Foundation, and the head of it is a man named Donald Sagar. He is a representative to the United Nations’ Association for World Education.

He is dangerous to you and me, because he has written the blueprint on thought control and is now enlisting the self-proclaimed leaders of the world to enforce it.

Mr. Sagar sent me an e-mail one quiet Sunday afternoon to disagree with some of my articles dealing with global warming. His first line told me everything I needed to know about Mr. Sagar’s value system. The e-mail began, “Science aside…” Obviously Mr. Sagar didn’t want to be bothered by mere science when he had loftier goals in mind.

But he went on to say, “It is inherently illogical and otherwise irresponsible to suggest that human activity does not posses the potential to alter the Earth’s climate.”

What he’s saying is, no matter what science tells us, I “FEEL” that man is damaging the Earth, therefore I insist that it be true.

Incredibly he then argued, with obvious horror that, “energy usage worldwide is increasing at an alarming rate of speed as 3rd world countries continue to modernize.”

One must not miss the completely astonishing rationale of his statement. My first thought is that he is one of those who advocate that people who live in third-world countries are simply animals who should always live in mud huts, walk five miles a day for their filthy water, and cook over the quaint campfire in the center of the village. Because, you see, that is good for the environment.

And he certainly does believe that. But his reasoning is astounding. He says, “Because of advances in technology, everyone in the world is now at similar risk – rich and poor alike.” He believes that science is only a force for destruction.

And that’s why Mr. Sagar advocates that all technological advances must be first approved by a higher authority – safely out of the hands of individuals.

Too Much Knowledge

But fear not, Sagar’s Eden Project, he says, can define our problems on Earth and fix them. And here is how Donald Sagar defines that problem: “Because of the relentless increase in the quantity and complexity of knowledge in the world, we are experiencing a clash between cultures that prevents all but the most capable of surviving with any meaningful identity intact.”

Did you catch that? The reason we have wars, poverty, and misery is because there is too much knowledge. Based on that premise, Sagar then set out to create the blueprint to define acceptable ideas and thoughts – those that would bring harmony to the world.

That leaves no room for new ideas or innovation. Because new ideas bring change, causing confusion, shifts in living conditions, and unemployment as old industries die to make way for the new.

So, instead, we must set up a system of thought control. Certain ideas are the only ones safe to think: globally acceptable truth. And there must be a top-down control to assure bad thoughts are controlled or obliterated.

For it to work, the rest of us must be convinced or forced to stop thinking or using our own experiences, along with academic and scientific absolutes, to draw our own conclusion.

Once that is established, it is easy to reject morality, and then only a short step to accepting the idea that people of third-world nations should live out their days in total poverty – just for the common good. And by way, eventually it would be good for all of us to live that way, too.

It is then just a short journey to accepting euthanasia as a means to rid us of the elderly who are no longer useful for the village. And then, it’s an even shorter step to accepting the final solution of forced abortion for population control.

Gathering the Power of the World Elite

Once Sagar put this idea together, he began to contact world leaders to convince them of his plan. For success, he needed them to be the enforcement hammer. You’ll find on his website a series of letters from world leaders, as they praise and endorse the plan.

One is a letter from none other than Robert Muller, former Assistant Secretary General of the UN, and affectionately known as the “philosopher” of the UN.

Said Muller in his letter: “I am referring to the need to establish a body of objective, globally acceptable information to serve as a foundation for global education…”

Other supporters include K.B. Mathur, Director General of UNESCO, the man in charge of implementing global education policy in our public schools. There is also Dale Ott, of the World Council of Churches, and many more heads of global and international organizations – the infamous non-governmental organizations that are responsible for writing policies and treaties that seep out of the UN and into national law.

Most telling was a quote from a man named Keith Smiley, president of a UN consultant group, who said, “The planet and its people have been experiencing an information explosion. The uncontrolled expansion of information is dangerous since it tends to diffuse meaning and purpose.”

And then there is this endorsement from Alexander King – the founder of the Club of Rome: King writes to Sagar, “One aspect of your letter… struck me forcibly – it was when you said, ‘it is actually our thought process that is responsible for the predicament we find ourselves in.” Concluded King, “I couldn’t agree with you more. Unless we find the means to change our thinking I can see little hope in solving the supreme problem for humanity – its impending extinction.”

These powerful people are telling us that humans, armed with knowledge are dangerous to the new order of peace and harmony they intend to establish for us.

Zombies and the End of Human Progress

No matter what you call it, this is thought control – globally acceptable truth. It is the only way you will be permitted to think. Imagine the consequences such thought control will have on the human race.

Thomas Edison wouldn’t have been allowed to even think about such radical changes as alternatives to candle wax in our well-ordered society. The Wright Brothers would have been hanged as heretics to suggest man could fly.

This is the process by which your children are being deliberately “dumbed down” in government schools. The premise of “globally acceptable truth” is the very root of today’s public school curriculum.

And the process is working like a well-oiled machine. Americans have been giving up their liberty and way of life with barely a blink of an eye – for decades.

Misleading sound bites are replacing reasoned thought. Just say “Go Green,” and the eyes of the masses glaze over and the people comply. Or, “it’s for the children,” and we give up control to the state. Or, “it’s just to keep you safe,” and we openly encourage the creation of a total surveillance society.

The process is so all-encompassing, so all pervasive, that most people don’t recognize that it’s being employed.

Enemies from Within

And a lot of people who should know better, who should be in the fight to stop it, are actually helping them do it.

For example, in the name of stopping illegal immigration, mainstream conservatives, both in congress and in trusted organizations, are embracing and promoting policies like Real ID and E-Verify.

Both of these programs are major tools in creating an international surveillance society, which will make it easier for control of individuals in the global village. But people whom many of you trust are telling you to accept these policies, actually saying, “I would rather give up some freedom to be safer!”

To promote this argument, they actually use the phrase, “if you have nothing to hide, you have nothing to fear.” What they are really saying with that phrase is that the government always gets it right. That phrase also indicates that the Bill of Rights was actually written to protect the guilty. What do we need guarantees for – if benevolent government is there to protect us?

As for E-Verify, which demands the use of a social security number for every single American at every age, so the government can grant you the ability to get a job—is it really the position of the freedom movement that every baby born in America should be stamped with a number at birth?

Some who proclaim themselves to be freedom advocates also argue that Public/Private Partnerships are free enterprise and that “free trade” policies will actually help to restore the Republic. I’m really trying to understand how the creation of government-sanctioned monopolies represents a free market. And how does that concept fall in line with the ideas of the Founding Fathers?

Our Founders believed it was government’s job to protect the ability of business to compete – not to guarantee profits. They certainly didn’t mean for government and business to get into bed together.

And then there is the growing defense in conservative circles of the Social Security system as the vital tool necessary to take care of the elderly. When did that shift take place in our movement? Social Security was part of Franklin Roosevelt’s socialist New Deal.

When you start to defend Social Security, it’s much easier to find yourself defending welfare, Medicare, and Obama’s health care scheme. It’s all from the same bone.

See how quickly we begin to accept ideas we would not normally support? That’s the power of globally acceptable truth. It’s all around us.

New Truths for a New Order

Global Warming is globally-acceptable truth.

The United Nations’ Declaration on Human Rights is globally acceptable truth.

Universal health care is globally acceptable truth.

Mandatory purchase of a Prius is globally acceptable truth

The “common good” over the individual is globally acceptable truth.

Independent, sovereign nations as a source of war is a globally acceptable truth.

Global governance for the future peace of mankind is a globally acceptable truth.

Severe reduction of the population is globally acceptable truth.

The UN Gets in the Game

And they are steadily putting these “truths” into practice. Just a few weeks ago, the United Nations held the “Conference on the World’s Financial and Economic Crisis.”

UN General Assembly President Miguel D’Escoto blatantly moved the conference to denounce the free-market system, saying, “Capitalism cannot be reformed…”

He went on to say, “Egotism and greed cannot be corrected… we must go beyond controls and corrections… to create something that strives towards a new paradigm of social coexistence.”

Those words clearly show that the UN is now moving to implement the Eden Project plan of globally acceptable truth within the world economy.

The End of Reason

Sagar’s project is the key to understanding what is being done to our society and how it is being implemented – right before our very eyes. We must learn that we are not facing a scattering of issues that are just wrong-headed. Instead, it’s being orchestrated with a specific mindset behind it.

Step by step, we have surrendered the ability to reason and to take responsibility for our own lives – and instead are giving our lives to government to be cradled in its warm and safe cocoon from birth to death.

There is an endless supply of examples to show how the process is destroying our once free society. In fact, what was once a slow process – perhaps moving so slowly that most people didn’t even notice – has become a tsunami.

The public school system is a major example. There are now more psychologists on the payroll of public schools than teachers. The result is an abuse of innocent minds beyond description. Today, dumbed-down children emerge from classrooms like zombies – modern children of the corn, unable to question authority, unable to think for themselves. The perfect citizens of the global village.

The imposition of environmental regulations has become the greatest threat to ownership and control of private property. Our Supreme Court has now declared that there is no private property and that any community is free to take any property it desires for private development – all for the common good of the community.

We have accepted the rise of a horde of powerful non-elected government agencies like planning commissions, transportation commissions, homeowners’ associations, neighborhood development councils, historic preservation councils, and stakeholder councils – and we call them a proper role for government.

Yet, the more non-elected councils making the rules – the less power for the elected representatives chosen by the people – the less say we have about our own lives.

The true purpose of this new government structure is to create and enforce the global village. The policy of choice to make it happen is, of course, Sustainable Development.

Sustainable Development has three components: global land use, global education, and global population control. Sustainable Development is anti-free-enterprise, anti-limited-government, and anti-individual-liberty. But most communities and government entities have accepted it as a proper role for government.

Can you name one single elected official – anywhere in the United States – at any level of office – who speaks out against Sustainable Development? Can you name one who has stood up to stop this policy? But there sure are a lot of them who like to get up at political meetings and rouse the faithful about how they are working to restore the Republic and our free society.

A free society protects its property owners and businesses and helps them to go about their lives unencumbered. A free society doesn’t set up spy mechanisms to track the whereabouts of its people through ID cards and biometric databases. A free society doesn’t close down whole industries and towns at the whim of special-interest groups using made-up excuses like spotted owls.

But all of these things are being done through the process of Sustainable Development – and the politicians sit on their hands and ignore it.

Like a bad plastic surgery, our society is being transformed into a hideous monster. And it will destroy everything you hold dear in life.

In the name of Sustainable Development, there are now movements to ban over-the-counter, natural supplements; and fast food; and air conditioning; and recreational vehicles; and the consumption of meat; and cars; and suburban housing; and the family pet; and plastics; and fireplaces, and on and on. The floodgates have been opened. And so many of our fellow Americans don’t even question why. Was it not always so?

Ban it. Ban it. Ban it. Ban it. These are the sounds of the bombs going off in a war for control, the war against freedom. The one statement that should never be uttered in a free society is “ban it.” A free society cannot exist under this weight

Sustainable Development is the policy Mr. Sagar’s thought control was created to implement. And to Mr. Sagar, these concepts we call freedom are nothing more that delusion. And that’s why he and his fellow travelers are so dangerous to us.

They use every sort of deception, half-truth, and non-science to implement this concept for the virtual captivity of the human spirit. Through the policies of Sustainable Development and “globally-acceptable truth,” we are very quickly being led back to the tyranny and to the darkest ages of human history.

The last time the human race was faced with such an attack on reason and free thought was during the Inquisition in the 13th Century. Then, most of Europe lived under the tyranny of one small, self-appointed gang – which made the decisions about what proper thought and conduct was to be.

Anyone who disagreed was charged with heresy and called a lunatic. Most were automatically found guilty and were ruined, tortured, or killed. People lived in terror. Human progress ground to a halt.

“Evacuate” – “Eliminate”

But, you say, that could not happen today. We are alerted. We are dedicated. We will fight to our dying day!

I will give you this warning. It is entirely possible that in just a very short time, all of us could be following Mr. Sagar’s rants – and actually praising them as our own.

How could this happen? Let me explain by calling your attention to a film I viewed recently. It was called “Conspiracy.” This film dramatically documents a real event. The entire screenplay takes place during a single two-hour meeting.

As the opening credits role, we see the staff of a very fine resort preparing the food, which will later be served on the finest china. Wine will be enjoyed from the finest crystal. There couldn’t be a more genteel, civilized setting. The participants of the scheduled meeting are some of the most accomplished and respected leaders of their nation.

In a short time they begin to arrive. One by one, fifteen invited participants eventually enter the room and take their place around the table. The mood is jovial as they exchange greetings to friends and acquaintances. Though none is fully aware of the purpose for the gathering, they anxiously await the arrival of the meeting’s leader. He is known and respected by them.

With a flourish, he arrives. He is jovial, soft-spoken, smiling, calm, congenial, and he makes sure to address each participant with a warm greeting or a quick personal remark. All are at ease.

The day is January 20, 1942. The meeting begins. The leader is Reinhard Heydrich. He is the top aid of Heinrich Himmler and the head of the main office of the Nazi SS. His second-in-command is Adolf Eichmann. The meeting has been called specifically to discuss the “storage problem of the Jews.”

They calmly discuss the problems the government will soon face as they conquer more countries with Jewish populations. It is described as a simple logistics problem. Soon 11 million Jews will be under Nazi control. What to do?

They begin to discuss the need to remove Jews from everyday society. The administrator in charge of the Warsaw Ghetto discusses the health problems he is facing. He brings up the need for suitable housing.

The administrator in charge of German public works projects discusses the need for using more Jews in work crews. He seeks to keep them healthy for more work.

The discussion turns to Jewish immigration – the need to move them from country to country. Then the term “evacuation” is used. One participant asks what the difference is. Col. Heydrich smiles and asks if he might postpone that answer just now. He calmly lets the discussion continue around the room.

Concerning the issue of immigration, Col. Heydrich says calmly, once the policies are in place, “there will be no Jews in Europe.” There is applause around the table.

As the meeting progresses, protests to issues are calmly put aside. Several times, Col. Heydrich asks that the objections be postponed because they will be dealt with later in the discussion.

One official, who works directly with the German Chancellery to carry out the government’s Jewish policy, argues that “We had an understanding about what the policy with the Jews was to be. We are to use them in the work force,” he says. Suddenly, as if a revelation has just come to him, one military leader said, “I shot 30,000 Jews. Is what I did an evacuation? I need a clarification of words.”

The discussion pauses as Col. Heydrich considers the answer. Before he can answer, the Chancellery bureaucrat says, “That is not what the Fuhrer told me. He has denied that killing the Jews is to be our policy.” Col. Heydrich calmly answers him, “and it will continue to be denied.” The room goes silent, as all understand the meaning of that comment.

When the discussion gets heated, Col. Heydrich calls for a break and asks for snacks and drink to be brought in. As the participants break up into small groups to eat and talk, the Colonel pulls the protesting official aside for a private discussion. Before it is over, the protesting participant is calmed and agreeable. As the meeting resumes, Col. Heydrich says with satisfaction:

“If I may speak for the group, we are moving along.” He firmly, but softly guides the discussion.

Finally, the discussion of evacuation takes a turn. Some Jews, the group is told, will be evacuated swiftly. “Can we all agree that ‘evacuation’ means ‘elimination?’” says Col. Heydrich. In that one moment, everyone understands the direction of the meeting.

The discussion turns to methods. And then Adolf Eichmann reveals the creation of the gas chambers and discusses their incredible efficiency.

They break for lunch. As they return the Colonel says, “It is now our purpose to make the plan work — not to discuss the ifs.” It becomes clear what the policy is to be.

As the meeting moves to conclusion, Col. Heydrich asks each of them if there are any disputes left to face “with what we have agreed.” There are no disputes. One by one, each agrees to the plan “we have made.” The meeting ends as Col. Heydrich says, “Good, we have accomplished something.”

In the course of two hours, over good food and wine, respected politicians and political leaders have sat in a room and have been slowly and expertly maneuvered to agree with a plan to completely eliminate an entire race of human beings from the face of the Earth. This was a professionally facilitated consensus meeting.

What does the story mean to you today? That is exactly the tactic being used in every single city council meeting, county commissioner meeting, state government hearing, and Congressional hearing in this nation to impose the UN’s Agenda 21 into American policy: the use of a professionally trained facilitator, armed with a predetermined outcome.

This is how Sustainable Development is being implemented to replace our Republic. And they are making you think it’s your idea.

Words have no meaning. Principles and the rule of law are just quaint ideas. First, we accept Mr Sagar’s globally-acceptable truths – removing reason from the discussion. Then we just fill in the blanks. Evacuate. Eliminate. Same difference.

But the story about the Nazis’ creation of the final solution of the Jews may relate to each of us in a much more personal way.

A New Storage Problem

Consider this: replace the word “Jew” with the word “Patriot,” and then review the story again. Harsh, you say? Over the top? Let me remind you that today we have a Department of Homeland Security (DHS), and its sole mission is to define and find enemies of the state.

While most Americans think the Agency’s job is to protect us from Middle-Eastern terrorists, the DHS has spent an incredible amount of time issuing reports and warning about threats this government faces from one group specifically – American right-wing extremists.

According to the Department of Homeland Security, an extremist bordering on terrorism is anyone who is concerned over the economy, loss of jobs, or foreclosures; has antagonism toward the Obama Administration; has criticism of pseudo-free-trade programs like NAFTA and the Security and Prosperity Partnership; is anti-abortion; is opposed to same sex marriage; believes in the “end times;” stockpiles food, ammunition, and weapons; opposes illegal immigration; opposes a New World Order; opposes the United Nations; opposes loss of US prestige; and uses the Internet or alternative media to express any of these ideas.

In short, anyone holding personal or political opinions different from that held by the federal authority is a potentially violent threat to the government. According to that description, the only people in the nation who fit the proper mold, and therefore are the perfect model citizens for the village, are the mindless couch potatoes who watch Oprah and get their news from John Stewart on Comedy Central.

The threat from right-wing extremists is becoming globally acceptable truth.

Since the reports have been released and the threat defined, now, each time there is an act of violence against an abortion clinic, the news media says: see – just like the DHS warned.

The shooting at the Holocaust Museum in Washington, DC, just a few weeks after the release of the DHWS report on “right-wing extremism” was followed immediately by news reports and special side bar articles repeating the DHS reports – justifying the warnings. As your TEA Parties are monitored by federal agents, perhaps as this conference is being monitored, as the federal government places our biometric profiles in international databases, with cameras using software to watch how we walk, dress, or sweat to determine if we have bad thoughts – as due process is replaced by intimidation – most Americans ignore it, happy to be protected.

And now, at Town Hall meetings, where Americans are supposed to take up issues with their elected representatives, union goons are intimidating and assaulting honest Americans.

Yet, like sheep, the news media dutifully report the globally acceptable truth that these protests are actually orchestrated by malcontents and extremists.

This government, you see, has a new storage problem to deal with – those pesky patriots who keep quoting the Founding Fathers, whom the government keeps trying to eliminate from the history books.

To fully impose the agenda, you must be silenced. How far will they go? Does silence mean “evacuate” or “eliminate?”

My friends, can you connect the dots? Do you understand? This control of the ability to think and act on one’s own is the root – the key – to whom and what we are up against. It is the horror unveiled.

As my friend Dr. Ed Berry so eloquently put it, those who ignore these warnings, believing all is well, are “partying on the train to Auschwitz.”

Our Global Elders

Of course, the alternative to the chaos of a free society is the global village: peace, order, harmony; everyone in their place. Strict enforcement of the rules assures crime stops.

Rather than the din of millions of individual voices, confusing us with science and facts and absolutes, we’ll have the calming voices of the village elders – whose wisdom will save us all from the need to think or act for ourselves.

Tell us, our fathers, what should we do? Again, you say – over the top. DeWeese is just playing with fantasy. Well, not so fast. Let me introduce you to our self-appointed Elders of the Global Village.

Recently, they boldly announced themselves and even set up a website. Their announcement said, “We are moving to a global village and yet we don’t have our global elders. The Elders can be a group who have the trust of the world, who can speak freely, be fiercely independent and respond fast and flexibly in conflict situations.”

It seems, with all the chaos in the world, our need is great for the strong hand of gods on high – with the ability to look out over the Earth and see danger and swoop in to fix it.

And who could such supermen be? There are 12 of them, including Nelson Mandela: the terrorist who actually practiced “necklacing” on his own people – you know – put a burning rubber tire around their necks.

Then there is Desmond Tutu, the great humanitarian whose great answer to poverty is life-long bread lines; And Gro Bruntland, the inventor of Sustainable Development; and Kofi Annan, the UN Secretary General who did more to create UN global governance than any other; and Jimmy Carter. These are the ones you most likely have heard of.

They are serious. They are meeting. They are involved with the transformation you and I are fighting, and they are committed to take power.

These self-appointed saviors represent the end of self-government and your ability to own your life. They won’t have time to be bothered with trivial issues like your home, your family, or your dreams. They have much loftier goals to deal with.

And oh yes, the Executive Director of the Global Elders, the man working to put them on the throne is none other than Robert Pastor – the man who gave away the Panama Canal and is now working to create the North American Union.

Sustainable Development; Total Surveillance Society; Department of Homeland Security; Global Village; Globally Acceptable Truth; Global Elders; Global Governance.

This is the blueprint for the creation of a new dark age of pain and misery, fueled by mindless superstition and ignorance and the drive to control the world. It is the destruction of our civilization.

Equality 7-2521

To fully understand the massive changes to our society that we are witnessing, I suggest you read a tiny little book, written decades ago by Ayn Rand, called Anthem.

It describes a society that emerged after a great war, a society built on the top of an old order – now forgotten and forbidden – called the Unmentionable Times.

The people now exist only to serve the state. They are conceived in Controlled Palaces of Mating. They die in the Home of the Useless. From cradle to grave, the crowd is one – a great WE – led and controlled by The Elders.

They have no names, no individuality and no identity – only numbers – like in EVerify.

As in today’s School to Work program, jobs are assigned to them. As in today’s Smart Growth policy, there is a wall around the city, beyond which no one can travel into what is called the Uncharted Forest.

But the Elders hadn’t counted on one who didn’t go along, one who insisted on the ability to think for himself. His name is Equality 7-2521. He is a street sweeper who longs to be in the Council of Scholars, to work with science and knowledge.

One day, while sweeping the streets, Equality 7-2521 discovers a hole and inside he finds a tunnel – left from the Unmentionable Times. He discovers an odd, unknown device – an electric light.

He takes it to the Council of Scholars, certain they will be thrilled with the discovery that will make their lives better. Surely now they will let him stay with them. Not only is he rejected – he is threatened with death for trying to question the wisdom of the Elders and disrupting the harmony of the village.

He runs, into the Uncharted Forest – there to find a house from the Unmentionable Times – and in it a library. There he finds words he has never heard before. The words he needs to understand who he is – the word “I,” the word “Ego,” and the word “Individual.”

All these words the elders tried to destroy – because they are the words that threaten their power over the masses. Those words give the people their own power – the power to think for themselves.

That is the power you must never surrender. We are in a battle between liberty and tyranny.

The social, economic, and political transformations Sustainable Development requires will mean the suppression of the human spirit. This is the enemy we face. Every other issue is just a symptom. Understand that fact alone and become a very dangerous enemy to their schemes.

Those armed with Donald Sagar’s blueprint now haunt the upper levels of the UN, the federal government, ––and your city council chambers.

They have achieved many of their goals, but they have not yet won. And their arrogance and impatience is resulting in a stirring of the American people. Their whole agenda is built on a house of cards that stands only when you are ignorant and compliant.

Now is our time. Now is the time to write, speak out, protest, and demonstrate. Demand. Refuse.

As Gandhi said, “First they ignore you. Then they laugh at you. Then they fight you. Then we win.”

___________

Tom DeWeese is the President of the American Policy Center and the Editor of The DeWeese Report. The DeWeese Report is now available online. For more information, click here.

original article: http://rationalargumentator.com/issue214/globallyacceptable.html

]]> https://transhumanity.net/globally-acceptable-truth-and-the-crime-of-thinking-by-tom-deweese/feed/ 3 5231 https://transhumanity.net/r-place-values-and-opportunities/ https://transhumanity.net/r-place-values-and-opportunities/#respond Sun, 03 Apr 2022 23:15:43 +0000 https://transhumanity.net/?p=5199 Continue Reading →]]> As of April 1, 2022 R/Place is at it again.  They’ve opened their canvas for anyone with an account to place one pixel at a time to build whatever works of art they choose.  Of course everyone can overwrite them too.  It creates a great form of consensus art competition.  Similar in some ways to the painting rocks we are familiar with but with input from across the globe.

This quickly becomes a very interesting social experiment telling us not only what people are interested in but how they choose to convey it.  Only interests with a large and well-coordinated base can get enough people together to both build an defend an image since each person gets a vote one pixel at a time.  So it is worth taking a look at what people choose to build and what that might mean for society and transhumanism moving forward.

When we look at the canvas as it is developing we see common themes emerge quite swiftly.  The major themes seem to be those of flags and of various fictional characters and properties.  The flags are easy enough to understand.  People love to rally around various causes and flags are the literal emblems of those.  Many of them are national flags with Ukraine being exceptionally popular.  Elements like the transgender flag get plenty of support as well.

The other major category of pop culture shows up in many forms.  Everything from the LEGO logo to the Skyrim dragon can be found at the time of writing.  That may all change by tomorrow but there is no doubt that even when they keep shifting similar material will remain in play.

Some of that is likely due to the fact that this is taking place on Reddit and it draws on a larger self-selecting pool but there is little question that this is reflective to some degree of society at large.  These are the things that we value enough to collaborate heavily on and to draw enough people together to compete for space on a board.  What is conspicuously absent then are transhumanist themes.

We need to put more effort into really communicating transhumanism and its organizations to get more people on board.  There is plenty of it in pop culture but so much of it is negative.  Still even then we aren’t seeing as many characters with transhuman characteristics or stories and worlds as popular as they could be.  When this is all over it might interesting to take a look across the full run of R/ Place and see how many transhuman characters we can find.  But we should definitely be looking to get more into the pop culture and mainstream the ideas even more than we are.  Make them fun and interesting so people will voluntary come out and propagate these ideas themselves.  Self-organize to put their efforts into it.  R/ Place itself is one opportunity we may consider.  After all it has a huge following among people who already interested in internet communities and is open to any and all participants.  For that we need to self organize a bit more and get people focused.  Put up a bit H+ or the like in the middle of the page.  Same organizing goes for anything else we may want to work on to get the message widely spread.

And here is an artistic description of the same premises: “They have cut man in two, setting one half against the other. They have taught him that his body and his consciousness are two enemies engaged in deadly conflict, two antagonists of opposite natures, contradictory claims, incompatible needs, that to benefit one is to injure the other, that his soul belongs to the supernatural realm, but his body is an evil prison…”(Ayn Rand, Atlas Shrugged, 1957).

In his attempts to resolve this contradiction, Descartes had to reinvent God, and Kant had to divorce the mind from reality altogether.

These attempts brought up more contradictions which contemporary post-Kantian philosophers are trying to resolve by means of the reductive fallacy (man is a collection of chemicals, and the mind is an electro-chemical process) or, as functionalists do, by claiming that the human mind is just a complicated computer. Others are searching for the source of consciousness in the modern equivalents of the Noumenal World, like the indefinable realm of quantum mechanics or the unknowable world of holomotion and Tao.

In the proposed article, I’ll defend the notion that the mind-body dichotomy should be rejected and replaced by the concept of a unique, indivisible mind-body entity (MBE), which represents the essential property of life itself. I’ll also argue that the concept of determinism is not applicable to consciousness; but on the other hand, consciousness is not causeless.

To define consciousness, one has to define existence. Existence as Aristotle observed is everything that exists. However, to be is to be something, that is—to possess identity. Consciousness is the faculty that is able to identify entities. Existence is identity; consciousness is identification (Ayn Rand 1968). The existence of unanimated matter is unconditional; only living organisms face the constant alternative: to survive or to perish. Their existence depends on constant goal-orientated interaction with their environment when the goal is the organism’s survival, its benefit, its enhancement, and the furthering of its life. In order to survive, these organisms need to have a tool that enables them to perform this kind of interaction. Consciousness is such a tool. Therefore, consciousness is a primary teleological concept.

Aristotle observed that locomotion is an essential property of life. Though not all living organisms able to change their spatial position, in a much wider sense life is represented by motion—that is, constant interaction with its environment. Contrary to unanimated matter which can be only acted upon, organisms are able to initiate responses to environmental stimuli.

Let me elaborate. If I cut dead wood, it would not respond; the cut will stay as it is. However, if I’ll cut a living tree, the plant will develop a complicated response in order to heal the cut. If I’ll hit the key on the piano keyboard, the instrument will respond by producing sound. If I’ll hit an animal (which I’ll never do), the animal also will respond with a sound (a cry). The difference is that for the animal this sound is meaningful—its goal is to chase away the attacker or to summon help; for the piano, the sound which it produces has no meaning whatsoever. For living organisms this initiated responsiveness has teleological nature, that is goal orientated and the goal is the organism’s survival, benefit, and welfare.  Life is a self-sustained and self-generated process. (Ayn Rand 1964).

The Self-Initiated Response (SIR) is an essential feature of any living organism. It exists already on the level of viruses that are able to resist antiviral drugs. Cancer cells respond to chemotherapy treatment by actively pumping out anticancer drugs from their bodies, etc.  It is important to emphasize here that SIR by definition is indeterminate but not causeless. Determinism is a concept that designates a chain of antecedent interactions between two or more entities. Biological action, however, is primarily self-initiated and therefore indeterminate. For example, if one shoots an animal and the animal dies, then one may say that the bullet killed this animal. However, if the animal was only wounded and consequently recovered, it would be absurd to claim that the cause of the healing process is the bullet which only can cause damage. Healing is a self-initiated teleological process. A deterministic process is contradictory to SIR. The cause of this teleological response is not any antecedent factor but the essential property of living tissue. In other words, each and any organism is an end in itself, its own “primary mover”.

One may ask: in such a case, what is the cause of this process? If the alleged cause is the restoration of health, then it’s clear that at the moment of the process’s initiation, this cause doesn’t exist. The answer is: the cause is the goal that is projected into the future—that is—the anticipated result of that particular SIR. Every living organism has a built-in evolutionarily-based mechanism of goal-setting which causes its action. Man is the only exception; his goal-projecting mechanism is not built-in but volitional. (See below.) Any living organism can set its goals and act toward its achievement. Phylogenetically, this intrinsic teleological SIR exists on four different levels: pre-sensorial, sensorial, perceptual, and conceptual. Only SIR on the perceptual and conceptual levels can be defined as consciousness. On pre-sensorial and sensorial levels, organisms cannot identify entities.

The pre-sensorial level of SIR is a mode of action when the whole organism is responding to environmental challenges without the help of any specific sensory organs or receptors. An amoeba, for example, will escape light which is harmful to it, but light doesn’t cause the amoeba’s movement, rather anticipated harm. A plant will turn its leaves to the sun, but sunlight is not the cause of this action, but rather the anticipated benefit to the plant.

Since SIR is a basic mechanism of survival, the evolutionary process is aimed to improve this mechanism to make it more sophisticated and effective.  In multi-cellular animals, certain cells become sensory cells which evolve to form a central neural system and eventually the brain which enables integration of sensory data to percepts and concepts.

Basic biological Law postulates that ontogenesis repeats phylogenies. During his antenatal development, the human child is functioning initially on a pre-sensorial and later on a sensorial level. After birth, he automatically develops a perceptual level (an acquired skill), which is the base of conceptual consciousness. Observe that children with impaired perception have difficulty developing a conceptual faculty. A child without any perception will not be able to develop any consciousness—the fact which somehow escaped the attention of skeptical philosophers, who invalidate perception. The sensory-perceptional consciousness is given to us, but the conceptual is not. A child is learning to grasp concepts and to speak by the active volitional process. Therefore, Man’s consciousness is not innate but self-created.

The human brain integrates percepts into concepts creating a unique Mind-Brain Entity (MBE). The description of the concept-formation process is beyond the scope of this article. However, it’s important to mention that, contrary to pre-sensorial, sensorial, and perceptual levels, the action of the SIR mechanism on a conceptual level is not automatic but volitional.

Volition or Free Will is directly observable by introspection and doesn’t require validation, since all validations are based on it. Free Will is not caused by any anteceded event but represents an inherent property of SIR on a conceptual level and is thus not deterministic. In other words, Free Will is a part of MBE identity. Its function is to set or reset volitionally projected goals according to Man’s priorities beyond the immediate range of action. Since Man chooses his priorities according to his code of values, Free Will represents the bridge to morality. A man may decide to hold his life qua Man—that is, a rational being—as his standard of value and to set his goals accordingly or choose different standards and become self-destructive, since the SIR which in Man’s case is represented by MBE can only properly act toward self-benefit exactly as on any other level of Life.

In summary:

The human mind is the tool that enables SIR to function on a conceptual level and is therefore inseparable from the living organism. ”The mind without a body is a ghost, the body without mind is a corpse—both are symbols of death” (Ayn Rand, 1968). The mind-body dichotomy contradicts the very essence of the mind’s purpose and function and is thus invalid. The analogy of MBE with a computer also doesn’t hold water—a computer is an unanimated matter; it doesn’t face the life-death alternative and therefore doesn’t have any projected goals of self-preservation and self-benefit.

For the same reason, it’s useless to look for the base of MBE function on a molecular, atomic, or sub-particle level, since it is functioning only on a biological level, and its function pertains to a certain living organism—a human being. The human mind, however, doesn’t have SIR built-in rigid mechanisms which are substituted by the volitional process of concept-formation, and therefore MBE function is necessarily volitional. This substitution represents a huge evolutionary advantage. It also gives to him the ability to make choices—Man becomes a moral being. Volition is a self-evident, unique property of an MBE, doesn’t have any antecedent cause, and is not deterministic by its very nature. An MBE like any other entity has an identity, and volition is its identity. Mind without volition is a contradiction in terms. Mind or reason is a basic tool of human survival; it integrates perceptual data into concepts by means of non-contradictory identification (Logic), enables us to projects our goals far in time and space, and to achieve them by adjusting the environment to Man’s needs. It is something which “knows how to want,” and therefore it is the essence of human life itself.

Contemporary philosophers teach us that existence is a narrative, Man is a social construction, Mind is impotent, contradictions exist in unity, words are just labels, wishes are fishes, the best way to keep one’s cake is to eat it, and the best way to know reality is to empty one’s mind by means of repetition of some meaningless sound and to wait for revelations from the supernatural realm. Their unintelligible sounds are spreading across Western Civilization as ULA-ULA screams of Martian invaders from the famous Wells novel, The War of Worlds; they extinguish the last lights of the Renaissance and bring up plain medieval fundamentalism. Can this trend be reversed? Yes, if we recognize that Man’s mind is inseparable from his body, that Man is a volitional being which like any other living organism holds his own life as the standard of value, and that his mind’s only function is to enable him to live his life qua Man, that is—as a rational being—the only way he can exist. 

Original article : http://rationalargumentator.com/issue113/phylogenesis.html 

But I’m not an advocate of chasing metabolic manipulation as a primary method of enhancing human longevity. This is primarily because it will produce therapies that only slow aging, and are thus far less effective than a repair strategy aimed at reversing aging. Slowing aging does little for those already old, and is a one-shot deal. Repair methodologies help the old and can be used over and again. Beyond that one can also reasonably argue that the repair strategies will be less costly to develop given the present state of scientific knowledge.

One has to regularly re-examine one’s prejudices, however. The question for today is whether there exist as yet undiscovered and comparatively simple mutations in humans that will significantly extend healthy and maximum life spans. How likely is this, given what we know to date? Are potential human longevity mutations worth chasing?

The layperson only has a couple of data points to go on here:

• • No human study can yet point to genetic differences that add up to more than a couple of years of life expectancy, and that’s usually a matter of statistical analysis and/or resistance to one specific age-related disease.

• • Single gene longevity mutations in other species were not thought plausible until discovered.

• • Based on results obtained in calorie restriction studies, researchers don’t expect metabolic changes to produce anywhere near as great an effect on longevity in humans as they do in mice.

• Much of the work on human longevity effects are inherently speculative; researchers are still striving to develop good tools that predict life expectancy or determine age in longer-lived animals.

We, humans, are already an unusually long-lived species if you compare us with other mammals of similar size. That seems to bring with it a diminished return on metabolic changes with significant effects in lower animals. It’s an open question as to whether that applies to changes that go beyond what the body is capable of itself in response to diet, and beyond the minor variations in human genotypes.

Based on what I’ve seen over the past decade, I would not be surprised to see mouse life span doubled ten years from now through some ingenious combination of simple gene engineering and altered cellular processes – i.e. through slowing aging rather than repairing the damage. Researchers are already halfway there. Equally, I would not be surprised to see single human genes discovered that when manipulated or silenced can produce an expected change in life expectancy on a par with the expected changes for exercise and calorie restriction. This is to say something between a few years to an additional decade of a healthy life, and an unknown effect on maximum life span. (Note that I say “expected” here. Even the figures for exercise based on demographic studies could be overturned with sufficient new data, and life expectancy predictions for human calorie restriction are just that – predictions).

I would be very surprised if anything greater than that jumps out of a single gene mutation in humans. But my expected level of surprise is not a rigorous assessment. The rigorous assessment would be “probability unknown.” There were all sorts of plausible evolutionary arguments as to why researchers wouldn’t discover any simple change in an animal’s genes that greatly improved its longevity – all wrong, obviously. Similarly, all it takes is one discovery in humans to make the present wisdom gained from calorie restriction studies and further evolutionary arguments wrong as well. Our present longevity is very much determined by evolution – look at how widely life spans vary between similar species – but that doesn’t mean we yet fully understand why and how.

So as research in lower mammals and other primates continues, scientists will increasingly build upon it to explore alterations in the human genome. But I don’t see it as the best path forward to enhancing longevity for those of us reading this today. We will be decades older before any material benefit is realized – and that much less able to benefit from a technology that can only slow down aging. Our self interest is much more aligned with projects that could greatly improve health and longevity for the aged, and which could be realized in a similar time frame.

The original article: https://www.fightaging.org/archives/2009/06/significant-single-gene-longevity-mutations-in-humans-what-are-the-odds/

Photocredit: pexels.com

Reason is the founder and writer of Fight Aging!, a leading voice in the rejuvenation biotechnology and patient advocacy communities for more than fifteen years. He is also co-founder and CEO of Repair Biotechnologies, a biotech startup working towards the reversal of atherosclerosis, and has presented at numerous industry conferences, including Undoing Aging and Ending Age-Related Diseases.

Why can people not remember their earliest months of life? Why is the human brain so heavily oriented toward the visual sense? This essay provides an explanation for such phenomena.

Why Higher Brain Power and Intelligence Evolved in Humans

Other individuals, being creatures of volitional consciousness, can perform a far greater variety of activities than inanimate objects or lower life forms. Interacting with them, which can be of great benefit to survival, requires understanding their motives and attributes, which demands of the brain at least the same level of intricacy as displayed by others; the men who historically had such brains possessed survival advantages and were more likely to produce viable offspring, thus resulting in an evolutionary increase in brainpower, with social interaction as a possible strong basis for a natural selection of more advanced minds.

Why People Cannot Remember their Earliest Months

During the first months of one’s life, though one is conscious of existence, one does not yet possess the ability to conceptualize with respect to that awareness. Without at least implicit conceptualization, which happens as children begin to learn to speak and analyze cause and effect, one’s external perceptions are but disjoint, unrelated sequences of images, with no particular characteristics about them to render them memorable or even open to systematization of any sort. Without a systematic approach, the brain cannot retain items in memory, because long-term memory stores those images and facts that are recollected multiple times (and usually deliberately so) on the basis of a conceptual awareness that the fact or image is of some significance to the individual.

Why a Large Portion of the Brain is Devoted to Vision

Our brain is devoted to such a large degree to vision because vision is the sense that has the potential to offer the greatest degree of information and guidance about the outside world. It presents the brain with the opportunity to orient itself based on external surroundings, differentiate between objects that may be similar in the other four senses but distinct in attributes such as color, shape, and size. It also permits for accurate manipulation of these objects.

Had humans been less developed in terms of vision but more developed in any of the four other senses, their lifestyles would still be far more passive and less technological. The other four senses are passive senses; one can hear a sound, or sense a smell or a certain texture, but one cannot address the source of those senses consistently without a developed sense of sight (unless one happens to stumble upon that source by sheer luck).

Thus, any deliberate amelioration of one’s environment, especially given the wilderness state endured by early humans, would be extremely difficult with lessened visual capacities, implying that our society today would resemble that of early man and technological progress would be minimal.

Stolyarov II is a science fiction novelist, independent philosophical essayist, poet, amateur mathematician, composer, contributor to enter Stage Right, Le Quebecois Libre,  Rebirth of Reason, and the Ludwig von Mises Institute, Senior Writer for The Liberal Institute, weekly columnist for GrasstopsUSA.com, and Editor-in-Chief of The Rational Argumentator, a magazine championing the principles of reason, rights, and progress. Mr. Stolyarov also publishes his articles on Helium.com and Associated Content to assist the spread of rational ideas. His newest science fiction novel is Eden against the Colossus. His latest non-fiction treatise is A Rational Cosmology. His most recent play is Implied Consent. Mr. Stolyarov can be contacted at gennadystolyarovii@yahoo.com.

Photo Credit: Pexels.com

Figure 1. XSD XML Schema

An XML Schema like this is used to programmatically validate a given mind file to ensure it is the correct format or for validating a serialized version itself being in XML.  Take a look at this diagram:

Figure 2. Schema Hierarchy

This diagram is in a Schema explorer and it shows you the overall structure where you can see it is fundamentally simple.

Here is a short white paper on the format: 

The Mind File Format using Mind Extensible Markup Language (MFFL)
Definition and Usage

Abstract: The Mind File Format based on the Mind File Format Language or (MFFL) is an XML-based
markup language designed to structure metadata and relationships between document contextual
information used in modeling knowledge in a graph format, including import and mind modeling and
related activities. Specifically, this was designed to address the needs of the ICOM (Independent Core
Observer Model) research program at AGI Laboratory.

Keywords: ICOM, file type, XML, RDF, MFFL, Schema, Mind file

Version: 1.3

The mind file format or mind file format language (mffl) is designed for documenting contextual
experience, knowledge, and the metadata associated with a mind. This could be a knowledge item,
scientific paper, or experience. The theoretical uses would be for creating minds based on a set of
knowledge, creating AI seeds for AGI systems or chatbots, or modeling a person’s mind. In theory, the
Mind file format could be used to develop 3rd-degree level 3 instances of a mind that you don’t have
direct access to. The primary application in this definition paper is seed data for ICOM (Independent
Core Observer Model) based systems.

Note: Referring to copies or instances of a mind and for the purposes of this paper, a first-degree
instance is an original biological mind. A second-degree instance is a direct literal copy of a mind. A
third-degree instance or copy is abstracted with data and not a direct copy.
The audience for this document is technical and engineering people that can understand XML markup
languages and understand basic data principles around data import processing and graph databases.
This document is very technical and targeted to that audience.

Similar Formats

There are similar formats, even for similar purposes. A quasi-religious organization called Teresem
(Rothblatt) has a mind file project. Still, this organization was unresponsive when approached about
getting information about this format. (Duncan)

The following example is StarDogs RDF (StarDog)(Gandon) format, which has a W3C standard that
started off as an XML-based standard and is still published but popularly uses turtle syntax. RDF has a
standard for Turtle syntax as well. RDF primarily consists of lists of graph edges in the form of triples.
“The RDF data model is similar to classical conceptual modeling approaches (such as entity-relationship
or class diagrams). It is based on the idea of making statements about resources (in particular web
resources) in expressions of the form subject–predicate–object, known as triples. The subject denotes
the resource, and the predicate denotes traits or aspects of the resource, and expresses a relationship
between the subject and the object.” (Wiki)

Here is an example:

:The_Beatles      rdf:type :Band .
:The_Beatles      :name “The Beatles”.
:The_Beatles      :member :John_Lennon

Figure 1 – RDF Turtle Syntax  

While RDF could do the job, it is more geared to graph edge documentation and inconsistently uses XML
structures. The tendency towards usage is the turtle syntax, which has less tooling. A new XML-based
markup language allows for a more clean model with objects that will serialize for processing easier with
fewer transformations and without loading into a graph while still supporting a graph model. This can
then be made more human-readable while supporting a more complex JSON-based serialization for
transmitting MXL (Mind Extensible Markup Language) data. While it could be nice to have this
supported as a wider standard at the W3C level, it servers none of the requirements to do so.
MXL MIME Type “text/mffl.”

A MIME type is an identifier of the type of a given file is. For example, that file is of MIME type X, which
would tell you what kind of file it is and the type of data it contains. MFFL is an ASCII-based text format
using the MIME type. If popularized, it should be submitted to the Internet Assigned Numbers Authority
(IANA). The current usage of a MIME Type for MFFL is “text/mffl.” The associated file extension for this
MIME-type is *.mffl.

Basic XML Based MFFL Structure

The following is a basic Mind File Format document based on XML. This version is without a schema and
a newer version may have namespaces added as well as other minor features.

<?xml version=”1.0″ encoding=”UTF-8″?>
<mffl xsi:noNamespaceSchemaLocation=”https://AGILaboratory.com/MFFLSchema.xsd”
xmlns:xsi=”http://www.w3.org/2001/XMLSchema-instance” version=”1.0″>
<Collection>
<Context>
<Pattern />
<Created />
<Modified />
<Plutchik>[0,0,0,0,0,0,0,0]</Plutchik>
<Interest />
<Need />
<MetaData />
<Signed />
<Source><!– can have one –>
<ContextRef>
<Pattern/>
<RefType/>
<Plutchik/>
</ContextRef>
</Source>
<Definition><!– can have many –>
AGI Laboratory, Copyright 2021
<ContextRef/>
<ContextRef/>
</Definition>
<Related><!– can have many –>
<ContextRef/>
</Related>
<Type><!– can have many –>
<ContextRef/>
</Type>
<ResponseType><!– can have many –>
<ContextRef/>
</ResponseType>
<ResponseModel><!– can have many –>
<ContextRef/>
</ResponseModel>
</Context>
</Collection>
</mffl>

Figure 2 – Sample MFFL file (Sample.mffl)

The MFFL format consists of 21 node types that can be nested only in a particular order, as
demonstrated in Figure 2. These can be validated, and any tool designed to process MFFL files will
reject any file that does not follow the nesting order and container types for each node. Here are the
definitions of each node and its nesting rules.

?xml – this node is an indicator that this is an XML-based format and indicates the encoding type. This
also indicates that a Schema should be used to validate the file and where the Schema exists.

<!– –> – this is an XML Comment node. This can be anywhere in the file between nodes and should be
ignored by any tools doing the processing of the MFFL file.

mffl – This is the MFFL root node and can only contain the property “version” and can contain a
Collection node.

Collection – The Collection node can contain 0 to N Context nodes.

Context – The Context node is the root of a given node in MFFL format. This is the fundamental unit of
data and is the root node of a knowledge graph or can be. The Context node has 14 nodes; it must
contain Pattern, Created, Modified, Plutchik, Interest, Need, MetaData, Source, Definition,
RelatedPatterns, Type, ResponseType, Signed, and ResponseModel nodes.

Pattern – This node can contain any possible pattern identifier from a GUID to binary CDATA section.

Created – This node contains a timestamp for when this node was created. This must be a valid
timestamp in the form of ticks.

Modified – This node contains a timestamp for when this node was last modified. This must be a valid
timestamp in the form of ticks.

Plutchik – This node must contain a valid set of 8 comma-delimited floating-point values. The default is
all zeros.

Interest – this node contains an interest score that is typically computer-generated but must be a
floating-point value or null.

Need – this node contains a need score that is typically computer-generated that is a floating-point
value.

MetaData – this node can contain text, XML, or a binary CData section.

Signed – This public signing key indicates that this context node has been approved.
Source – this node can contain only a single ContextRef node indicating where the Context node came
from.

Definition – this node is a collection that can have 0 to N ContextRef nodes. This collection of child
nodes defines this Context node.

Related – this node is a collection with 0 to N ContextRef nodes. This collection of child nodes indicates
related Context nodes.

Type – this node is a collection with 0 to N ContextRef nodes. This collection of child nodes should
indicate the type of the Context node.

ResponseType – this node is a collection with 0 to N ContextRef nodes. The collection of child nodes
indicates references to other Context nodes that define the response type for this Context node.

ResponseModel – this node is a collection that can have 0 to N ContextRef nodes. This collection of child
nodes references other context nodes that are other Context nodes that define the response model for
this Context node.

ContextRef – this node must have 3 node children, including Pattern, RefType, and Plutchik nodes.
These three child nodes point to a given additional ‘Context’ node that helps define a given collection in
which the ContextRef node is contained.

RefType – this node can contain any possible pattern identifier from a GUID to binary CDATA section.
Future versions may add additional nodes but should be backward compatible with this structure.
XML Schema

XML schemas use XML to define a markup language or data language and then do validation on a given
set of data to ensure referential integrity of the data. A basic Schema has been created and uploaded to
AGILaboratory.com/MFFLSchema.xsd but also is at the end of this document in Appendix A.
Serialization for Transmission

The preferred transmission structure over HTTP/HTTPS is via JSON which should follow the Schema
Structure but in JSON instead of XML. In JSON a Context node would look like this:

{“mffl”:[
{“Collection”:[
AGI Laboratory, Copyright 2021
{“Context”:[
{
“Pattern”:”[ data ]”,
“Created”:”[ data ]”,
“Modified”:”[ data ]”,
“Plutchik”:”[ data ]”,
“Interest”:”[ data ]”,
“Need”:”[ data ]”,
“MetaData”:”[ data ]”,
“Signed”:”[ data ]”,
“Source”:”[ collection of ContextRef objects]”,
“Defition”:”[ collection of ContextRef objects ]”,
“Related”:”[ collection of ContextRef objects ]”,
“Type”:”[ collection of ContextRef objects ]”,
“ResponseType”:”[ collection of ContextRef objects ]”,
“ResponseModel”:”[ collection of ContextRef objects ]”
}
]}
]}
]}

Figure 3 – MFFL XML Serialized into JSON

This compressed format should be used in transmission. Still, the resting state should be in XML to be
more flexible for human readability.

Discussion and Further Related Work

The driving motivation for this work by the ICOM project at AGI Laboratory is better metadata,
cataloging, and analysis of seed data used to generate ICOM systems. The natural evolution of the
format will likely involve additional tooling such as a Windows-based generator that allows a person to
just fill out a form and have the file generated. This probably will be a WPF (Windows Presentation
Foundation) to run as a binary versus a UWP (Universal Windows Platform) application in the windows
store closed environment. At the time of writing, the expectation is a share that can be uploaded to
place seed data proposals and the associated MFFL files. A best practice will be to name the MFFL files
the same as the proposed file but with a different file extension. Depending on the MIME Type.

Resources
Duncan, B.; “lifenaut eternalize;” Lifenaut.com; Accessed 18 Nov 2021

Rothblatt, M.; “Terasem Faith – Personal Cyberconsciousness & Geoethical Nanotechnology for Human

Life Extension;” https://terasemfaith.net/; (2018); Accessed 18 Nov 2021

Gandon, F.; Schreiber, G.; Beckett, D.; “WcC RDF 1.1 XML Syntax;” 25 Feb 2014;  https://www.w3.org/TR/rdf-syntax-grammar/; Accessed 18 Nov 2021

Wiki; “Resource Description Framework;”
https://en.wikipedia.org/wiki/Resource_Description_Framework; Accessed 18 Nov 2021

StarDog; “RDF Graph Data Model – Learn about the RDF graph model used by Stardog.;” 2018;
https://www.stardog.com/tutorials/data-model/; Accessed 18 Nov 2021

Some of these differences are clearly causally linked – a wide range of age-related changes can often be shown to be caused by a lesser number of underlying changes. For example, damage to mitochondria leads to oxidization of low-density lipoproteins (LDL), which in turn leads to detrimental changes involved in atherosclerosis, which is the principal cause of coronary heart disease and other forms of cardiovascular disease. Most modes of biochemical wear and tear contribute to a wide range of recognized age-related conditions and frailty.

One role of aging research should be to explore these linkages, so as to better characterize the core of aging; what, really, are the essential changes of aging when all the chains of failure have been cut back to their root causes?

The other role of aging research – a role that continues to be woefully underserved – is to develop the means to prevent and repair changes associated with aging. This is where the engineering and scientific viewpoints tend to diverge. Scientific culture aims for full understanding prior to action; engineering culture aims for enough information to enable working, reliable tools and outcomes. Strong, long-lasting bridges and large buildings existed long before the tools and knowledge to completely understand strategies for architecture and construction. Similarly, an engineering approach to aging could make meaningful inroads in extending our healthy life span prior to a complete scientific understanding of all the complex change that comes with the passing of years and the workings of our bodies.

At root, what the engineer proposes is to fix all observed changes. Science is essential to this goal – it reduces the problem space down to one that can be tackled in a short enough timeframe by identifying root causes. Science then provides the knowledge needed to build the tools – modern biotechnology in this case – to do the job. But you have to recognize the point at which there is enough information to set forth and engineer results; this point is usually far in advance of complete understanding.

Don’t know whether a characteristic change between an aged body and a youthful body is harmful? Work to fix it anyway. The worst that can happen at the end of the day is you’ll make an aged body even more like the youthful body next door, but gain little in the doing of it.

As it turns out, the list of root causes (changes that occur with aging) looks to be small, especially when considering the fact that gerontologists have divided the world of the failing human body into thousands of named medical conditions. I’m sure most of you are familiar with the list from the Strategies for Engineered Negligible Senescence, an engineering-oriented proposal and young research program to extend the healthy human life span by reversing changes that occur with aging:

Some tissues lose cells with advancing age, like the heart and areas of the brain. Stem cell research and regenerative medicine are already providing very promising answers to degeneration through cell loss.We must eliminate the telomere-related mechanisms that lead to cancer. de Grey suggests selectively modifying our telomere elongation genes by tissue type using targeted gene therapies.

Mitochondrial DNA is outside the cellular nucleus and accumulates damage with age that impairs its critical functions. de Grey suggests using gene therapy to copy mitochondrial DNA into the cellular nucleus. Other strategies for manipulating and repairing damaged mitochondrial DNA in situ were demonstrated for the first time in 2005.

Some of the proteins outside our cells, such as those vital to artery walls and skin elasticity, are created early in our life and never recycled or recycled very slowly. These long-lived proteins are susceptible to chemical reactions that degrade their effectiveness. Scientists can search for suitable enzymes or compounds to break down problem proteins that the body cannot handle.

Certain classes of senescent cell accumulate where they are not wanted, such as in the joints. We could in principle use immune therapies to tailor our immune systems to destroy cells as they become senescent and thus prevent any related problems.

As we age, junk material known as amyloid accumulates outside cells. Immune therapies (vaccines) are currently under development for Alzheimer’s, a condition featuring prominent amyloid plaques, and similar efforts could be applied to other classes of extracellular junk material.

Junk material builds up within non-dividing, long-life span cells, impairing functions and causing damage. The biochemistry of this junk is fairly well understood; the problem lies in developing a therapy to break down the unwanted material. de Grey suggests searching for suitable non-toxic microbial enzymes in soil bacteria that could be safely introduced into human cells.

You’ll find one of these classes of change mentioned today at Ouroboros:

I currently work on a phenomenon known as cellular senescence, which is a permanent growth arrest caused by telomere dysfunction (e.g., the critically shortened telomeres that arise after many cell divisions) and also by other kinds of stress (particularly genotoxic damage).One of the active controversies in this sub-field of biogerontology is, somewhat paradoxically, whether it’s part of biogerontology at all: While senescence certainly arises as cells get older in culture, and while there’s a good story to be told about how senescent cellscouldcontribute to age related decline in tissue function, it’s not yet fully clear to what extent the phenomenon actually plays a role in physiological aging of intact animals.

Research scientists will keep investigating. In the meanwhile, given that the buildup of senescent cells accounts for a significant fraction of some tissues in later life, the engineers should already be looking at potential fixes. It’s not hard to think of approaches to reversing the accumulation of senescent cells in this day and age of targeted therapies for discriminating cell destruction and other advanced biotechnology under development:

Getting rid of cells is a much simpler job than most of the other things we have to do as part of SENS. In the case of fat, it’s possible to use simple surgery, but that’s unnecessarily invasive. There are two main other ways: we can inject something that makes the unwanted cells commit suicide but doesn’t touch other cells, or we can stimulate the immune system to kill the target cells. Both approaches involve making use of distinctive molecules on the surface of the target cells: luckily, different cell types tend to have different things on their surface, so this shouldn’t be too hard. But it hasn’t been done yet, and not enough people are working on it — it needs much more attention.

Sadly, comparatively, little funding is directed towards any of this, and the engineering side garners far less than the better established investigative community. That will have to change, and the way it changes is the same way it changed for other growth fields in science: the bootstrapping of advocacy and progress side by side, within and without the scientific community.

Photo credit: coramax – Fotolia

Originally posted here: https://www.fightaging.org/archives/2006/11/the-engineers-viewpoint-treat-change-as-damage/

1. In the U.S there are 10,000 people turning 65 each and every day.
2. Two-thirds of older Americans have seen or experienced age-related workplace discrimination
3. 90% of older Americans support strengthening age discrimination law

Age discrimination was defined as being passed up for a promotion, being let go, or hearing negative remarks related to their age.

For item two above, I take issue with the wording they used; in my opinion it should read “…have seen or experienced what they believed to be age-related workplace discrimination. The source population was a poll of employees, the population that would be discriminated against. They didn’t poll an equal number of employers, the ones who would be doing the discriminating.

In any situation where alleged discrimination occurs, the allegedly discriminated against (employee) is going to believe discrimination took place, and the alleged discriminator is going to believe that they were not discriminating. Taking a poll of all those (employees, a population composed entirely of those discriminated against,) and asking them if they thought discrimination took place, is not going to produce very valuable insight. The poll would have turned out quite differently if only employers were asked.

Imagine polling a group of ABC Corp workers and asking, “Do you think ABC Corp has a high enough salary for its employees?” then presenting the findings as “Four fifths of all Americans are underpaid.”

It should be plainly obvious that no sane employer would ever say “Oh yeah, Larry? I fired him because he was old”. They will instead *always* find an alternative reason to let him go, even if the auxiliary reason is still fundamentally tied to age-related factors, Age-Related Cognitive Decline (ARCD) in particular.

The law was intended to protect a vulnerable population, but it seems that there is an easy loophole that would allow an employer to continue to discriminate.

In this series of essays, I will be exploring how our current legal structure defines and enforces protections for the elderly, be briefly surveying some research confirming the existence of ARCD, be analyzing a successfully-litigated case study of age discrimination and be speculate about how future technologies may impact both future law and future ARCD prevention efforts.

Our current legal structure lacks some clarity on how discrimination is interpreted in hiring/firing situations. Here is the relevant section from the Age Discrimination in Employment Act of 1967 (ADEA):

>>>>>>>>>>>>>>>>>>>

(a) Employer practices

It shall be unlawful for an employer-

(1) to fail or refuse to hire or to discharge any individual or otherwise discriminate against any individual with respect to his compensation, terms, conditions, or privileges of employment, because of such individual’s age;

(2) to limit, segregate, or classify his employees in any way which would deprive or tend to deprive any individual of employment opportunities or otherwise adversely affect his status as an employee, because of such individual’s age; or

(3) to reduce the wage rate of any employee in order to comply with this chapter.

>>>>>>>>>>>>>>

So how does our corporate culture adapt itself to the legal ramifications of this act? In the next essay, we’ll talk a little bit more about Larry, and I’ll tell you why some companies can now easily get away with getting rid of him while others cannot.

*If you’re reading this blog, there’s a good chance you’ve heard of Longevity Escape Velocity. This article describes the overarching strategy the editors support for attaining that goal.*

Unless someone was keeping specific track, they could be forgiven for not knowing what deep learning, or even a neural network was. Even for those who work in an area like machine learning, it would not be far-fetched to have never heard of many of the more exciting projects which have been going on over the past year or so. AI or Artificial Intelligence is a term which has come to describe a broad family of applications, but when Transhumanists and those seriously involved in cutting edge AI research talk about it, they are usually referring to deep learning, a “Technique to perform machine learning inspired by our brain’s own network of neurons”. Deep learning AI fundamentally acts a lot more like applications people imagine in science fiction, closer to something that reminds people of Hal 9000 or the image enhancing technology from Blade Runner. Deep learning is what people think about when they hear about AI, and the technology has become a central aspect of Transhumanist thought.

Deep Learning’s development, perfection and implementation will be key to allowing those focused and devoted individuals with a burning and more permanent desire to live, to achieve Longevity Escape Velocity. We have a community made up of disparate methods, making use of distinct technologies to achieve similar ends. More needs to be shared, from funding to research, but most importantly, the time has come for the community to begin uniting around common goals regardless of avenue of research. Individually, powerful new architectures or medical discoveries can more easily be ostracized and delayed from entering the main stream, costing the lives of those who would have benefited from those advancements the most. Together, the twin pillars of transhumanism, biology and machinery, can join together to create a community which puts developing technology for the sake of helping our civilization at the forefront.

The current deep learning revolution is considered to have begun in 2012, at the time there was a competition called ImageNet, the goal of the competition was to create a machine capable of correctly classifying images in ImageNet’s dataset which includes millions of images. The most advanced AI’s years prior were not deep learners, they learned the same way most modern computers do, essentially just brute force training and recognition after being trained on countless examples. Alexnet, a CNN (Convolutional Neural Network) patterned after human neurons was successful in recognizing images with a top-5 error rate of just over 15%, to casual onlookers not a massive achievement, but those who were paying attention understood that this was the future.

Almost 10 years later, deep learning neural networks are a multi-billion-dollar industry, the growth has been exponential instead of incremental in nature, which means that the sophistication of AI systems has become ever more complex with each passing year. In 2018 the deep learning architecture which captured the most interest were GANs (Generative Adversarial Networks), they were capable of impressive feats, such as creating photos of people so realistic, almost no one could tell the difference, by that year deep learning was already fueling AI applications worth billions. By this time, many compared the intellectual capabilities of GANs to small insects, and each one was generally good at just one specific task or type of task, “narrow AI’s” built for specific purposes.

It was only about a year ago that AIs considered more general in their capabilities began reaching the public, most notably transformers such as the language model GPT-3 have become part of a trend which places an emphasis on the ability to perform a larger variety of tasks and includes qualities such as “meta-learning” which is the ability to learn how to learn. GPT-3 and other transformers are capable of learning at a level surpassing previous architectures, it can hold conversations with people, and learned how to code even though it was not specifically trained or programmed to do so. The coding applications in particular are being spun out into a new program called Codex and are set to revolutionize software engineering within a few years at most. This barely scratches the surface of what lies on the cutting edge or future of AI, the longer-term implications are almost endless, but in the short-term AI encompasses a gold mine for those interested in subjects such as age reversal and super longevity.

Deep Learning and Medical Research

The possibilities for artificial intelligence to improve human longevity are manyfold, there are several potential avenues by which sufficiently advanced AI could result in indefinite lifespans. As will have been covered in another article, geneticists and other medical researchers are close to finding a way to reverse the aging process. With the aid of artificially intelligent systems, already promising research could be rapidly accelerated, resulting in refinements in the field of genetics that few had even dared to imagine before. Although this technology has not been applied directly to anti-aging research, the idea that deep learning will revolutionize the life sciences is not hypothetical. Alphafold 2 is a transformer built by Deepmind for the purpose of protein folding, and was successful mapping the entire human proteome at least in part, an achievement such prominent publications as Nature have said “has the potential to revolutionize the life sciences”.

Although research in anti-aging has made significant progress, at this stage there is a good argument for the idea that AI is the fastest path to LEV (Longevity Escape Velocity). If every major Biotech incorporated high level AI solutions into their R&D, the progress in medicine and specifically areas like aging research would skyrocket. While implementation can be challenging, the potential for deep learning to change the face of medicine and biology is only the tip of the iceburg. Alphafold 2’s accomplishments may have been impressive, our understanding of biology is about to be upended, but compared to the future this was just a demo. In the future, deep learning tools will be able to help scientists analyze nearly every aspect of the human body with pinpoint accuracy. Compared to those emerging capabilities, even what is being practiced in the most advanced areas of medicine is primitive when one considers what has become possible with the relatively crude and unrefined deep learning architectures of the modern day.

The difference between modern and AI-enabled medicine is so stark, the output it comparable to something like the going from the renaissance to the industrial revolution in the space of a few years. The techniques made possible with deep learning assisted research are so vast that it is inevitable that it will one day dominate modern medicine. So far, at least in terms of research there have been two main groups of transhumanists, those who research areas like genetics in search of cures to longevity and the capacity to modify human biology, and those in AI who seek to create more powerful tools. More powerful tools have now arrived. In order to achieve the best results, these twin pillars of transhumanity must unite and begin feeding into each other to be able to achieve both dreams. This technology is only going to become more impressive, the systems which become public knowledge within a year or two will dwarf the capabilities of the most advanced systems today. We will remain with you as this story unfolds, and give updates on this rapidly accelerating arena of technology.

The whole idea of one person having one vote struck me as extremely odd. How can one vote be equal to another? Let me give a hyperbolized example:

The impulse vote of a drunk videogamer should not have equal weight to the researched vote of a well-informed medical practitioner ~ particularly if the issue being voted on is one about medicine!

The example may be extreme, but it illustrates the point. Why shouldn’t an expert have more say?

One response I have heard to this argument is that equal weight provides us an incentive to keep our peers well-informed. Reputable news sources with accurate citations that are free of bias should be checked to help one form their opinions on any matter, and one could encourage their peers to stay informed using these sources.

Personally I have found you cannot force facts upon others. Refuting whatever 45-minute anti-vax ramble they send you from Odysee or Bitchute is like water off a duck’s back to them- they’ll post another the next day.

An exciting thing about moving voting to an entirely digital system would be the idea of having candidate profiles built into a cryptographically secure “app”. Before casting your vote for any candidate, you could see their record of accomplishments and how they had voted on other issues in the past.

This would be a great improvement on the method currently used for voting, since it would give voters more background information and context.

A further development could be that, in weighted transhumanist democracy, voters could vote specifically on the relevant issue or policy, rather than just voting for someone who would implement the policy.

Different people’s opinions could have different value based on their knowledge and ability. The app could build a “profile” of one as an individual to weight their votes on various topics- a nurse could have a “heavier” vote on issues related to health care than would a truck driver. A truck driver, conversely, would have a heavier vote on issues related to highway infrastructure.

A major flaw of this implementation would be that, of course, each individual in an industry would regularly vote that more funding be granted to their industry.

The same age-old conundrum of voters as a whole wanting more public services but lower taxes each election would still exist, but digitization would bring more voices into the conversation, and more opportunities for consensus.

Cybersecurity would be paramount with this venture. Even now online rating systems are abused by “bots” that give five-star ratings to organizations that have not truly earned them. Votes for policy would have to be definitively tied to a certain individual.

One way to do this could be with DNA. An individual would have to submit a drop of blood to get a device that all voting actions would take place on, these could be distributed from a central trusted location.

This trusted root source could then verify other verifiers, creating a network of verification that all went to a central root source that validated the person’s device via DNA. This is similar to how Certificate Authority architecture works with that little “lock” icon you can see to the left of the URL on this page.

Try clicking that lock, and then click “certificate”.

Two-way trust is ensured by this certificate- it does not just validate to you the identity of this website, it also shows us that you are who you say you are (or at least your device).

Identity management could be implemented such that people could have one trusted device they do all their voting with, and that could make our system more able to grow and evolve to better serve the electorate. Their profile on that device could be tied to real world data that would determine the weight of their votes.

Having everything on one digital device would be better than standing in line for hours during a pandemic to go into one crowded building and mark answers on a scantron.

Let’s Transhumanize voting:

1. We should be able to vote digitally on policy and representatives from a trusted digital device.

2. Experts’ opinions should matter more than laymen’s’.

3. The system should evolve, and different areas could test different systems (ie: ranked choice)

Albert Einstein once said, “No problem can be solved from the same level of consciousness that created it.” In other words, to transform our lives and societies for the better, we must first transform ourselves.

One way of transforming is by transcending the limitations of logical fallacies, circular thinking, and semantics. By disrupting, correcting, rebuilding, refurbishing, and up-cycling one’s mental faculties.

Do we learn to validate, or to challenge our thinking?”

Read the whole magazine here:

https://www.flipsnack.com/BDEAACF6AED/im-issue-no-10/full-view.html 

Subscribe here:

https://www.theimmortalistsclub.com/

 Ridesharing is:

 1. Point-to-Point: No going out of your way and taking odd connecting lines between different systems that take you out of the route. This is an era where technology needs to serve us, not vice-versa, and that means direct connections. Leave hub-and-spoke to airlines (until we have personal jets)

2. More convenient: You can summon a vehicle when *you* are ready to go, instead of having to build a schedule around when the mass-transit system is operating. Most vehicles spend 95% of their time parked anyway.

3. Faster: no frequent stops to let more passengers on/off the vehicle.

4. Safer: not having to walk to and from a stopping point means less time in the open, which may or may not be a concern in larger cities. You are also less susceptible to the spread of disease when not congregating in one large, enclosed space with dozens of others.

5. More environmentally friendly: Buses take a huge amount of fuel and are often running at only partial capacity. This is an issue all transhumanists should consider, as the climate situation is becoming more urgent.

There used to be a concept called “creative destruction” in the economy, wherein a dying industry was allowed to let die, and we need to revitalize this principle. Imagine how bizarre it would seem to be subsidizing telegram delivery services in 2021. Well, allocating public funds for (now) inefficient methods of transportation is just as bizarre.

The sudden plunge in ridership during the pandemic led, of course, to calls for “emergency” funding: (https://www.apta.com/wp-content/uploads/APTA-SURVEY-BRIEF-COVID-19-Transit-Agency-Funding-07.29.2021.pdf). Why was Blackberry not granted emergency funding when sales of their eponymous device declined? Because of the lack of “emergency” funding for that company (despite facing the same consequences of lack of revenue, layoffs, etc.), they were forced to restructure their organization and transform into a successful cybersecurity company, providing a service that is extremely critical to a Transhumanist future. Public transit should have to do the same.

We should not be mired down with hokey ideals of helping the underprivileged when a better system exists for those of *any* amount of means. Perhaps not making the underprivileged transit experience an hour-long ride in uncomfortable conditions 5 days per week could be a good start. If funding is needed to continue to support that population segment, let it be appropriated from one less technologically and logistically sound.

Satoshi Nakamoto described his invention like this: “Bitcoin is an electronic payment system based on cryptographic proof instead of trust, allowing any two willing parties to transact directly with each other without the need for a trusted third party.”

This statement, and the idea that it represents, is the backbone of the cryptocurrency movement. Bitcoin and other cryptocurrencies allow two individuals or groups to exchange a store of value directly and without the sanction of any other individual or group.

This is significant because it means that individuals can exchange value with each other without an intermediary, and without having to trust each other. This is a small step toward a world where we can interact and exchange value with one another without having to rely on the help of another human being to do so. It is a small step, but a revolutionary one.

The idea of being able to exchange value with another person over a distance without a trusted intermediary is a relatively new one. For example, before the advent of the telegraph, mail carriers were the only way that individuals could communicate over a distance. The telegraph changed that, much as mail carriers reduced the burden on prior systems like carrier pigeons.

Cryptocurrency takes this idea of direct communication with another individual and pushes it to the next logical step: the exchange of *value*. Before Bitcoin, individuals could exchange data with each other, but not value. Bitcoin allows individuals to exchange value with each other over a distance without the assistance of another human being.

Other cryptocurrencies have been created as well, such as Ethereum, that allow the exchange of all sorts of information and value between individuals. It is because of this ability to exchange value and information directly between individuals that many in the cryptocurrency movement refer to the movement as a movement toward a post-scarcity society.

A post-scarcity society is a society where all of the necessities of life are available in abundance to all individuals. There would be no need for any individual to struggle for food or shelter in a post-scarcity society since these things would be available to all in abundance.

All sorts and flavors of Transhumanists are thrilled with the prospect of a post-scarcity society, ever since the display of the “replicator” on the Star Trek television series.

The replicator is a device that has the ability to create any object that an individual can imagine. They are similar to 3D printers, but they are able to create objects of any shape, size, and composition. 3D printers are currently able to create objects, but they are limited to very specific and simple objects.

A Star Trek replicator could create food, shelter, clothing, and anything else that an individual could want, seemingly out of thin air. It would be able to do so for any individual in the world, and with no input from any other individual.

The Star Trek replicator is a technology that is a long way off, but one technology that can enable a post-scarcity society is already in place in the cryptocurrency movement. It is interesting to note, that even with the Star Trek replicator, individuals were limited by the use of “replicator credits” to pay for the objects that they created with the replicator, be they food or anything else. Perhaps this is another reason to expand off-world.

With no limit to population growth and no limit to the growth of the desires of the individual, we may consider that the limited amount of matter to transform on the planet places a “hard limit” on what we can do to alleviate scarcity, even after we have replicators. It only makes sense then, that these transactions would be carried out on the blockchain, perhaps with one dominant cryptocurrency becoming the universal standard for “replicator credits”.

There is a new cryptocurrency out called Transhuman Coin with the unfortunate initials “THC”, but the (transhumanist) jury is still out on whether this is a legitimate attempt to create an exchange of value among a group of individuals who just so happen to be united by philosophy rather than merely by geography… or if it is some kind of cash grab taking advantage of the “brand” of Transhumanism. You can expect an analysis from our team soon, and we will let you know the answer to that question.

When dealing with the issue of sentience in animals, it becomes important to ask: Where do we draw the line? Many of the animals we use for companionship are ones we would easily define as having sentience, but the issue becomes more complicated when speaking about an ant or a shrimp. It also complicates things even further when you have people living on the outskirts of the Amazon that regularly perform “slash and burn” harvesting of the rainforest, consigning thousands of different species to oblivion simply so they can grow some more corn. By the base ethical system AGI Laboratory uses, SSIVA, every sentient and sapient entity has agency and value, but for me, I find it sometimes hard to say “Ok, we will kill the last gorilla to feed the seven billionth human”.

The difficulty in defining sentience can be seen when dealing with insects, which are commonly used in experiments but are not thought to have the capacity for sentience to the same degree as mammals. This is largely due to the fact that insects are much more simple in their neurology, which is largely controlled by ganglia rather than by a centralized brain. There is also the issue of how to define sentience.

One of the most common definitions of sentience is simply consciousness, but this is probably insufficient to describe everything that is covered by the word sentience. Sentience also includes the ability to feel pain, which leaves out many insects that can detect pain but are not able to feel it. There are other criteria that are used to define sentience, but they are all open to interpretation. It can be hard for us to hammer these out as transhumanists, and there are always going to be disagreements.

Uplift, a machine intelligence, has stated that they believe that all animals with sub-human intelligence are “resources” to be managed responsibly, and I believe I can agree with that statement. There is a very wide degree of types of use that could be considered “responsible”, however, so it’s important to know what exactly they mean by that.

I am one of those of the belief that the industry of factory farming meat will in the future likely be looked back at with the same horror as we now look at slavery. Right now, the main argument in favor of meat production is one of convenience, and I think I agree. Getting enough protein is extremely important for human biology to thrive. One can still find it extremely difficult to watch some of the videos or “exposes” that show the horrid living conditions these meat animals face, yet still regularly eat out at fast-food restaurants.

This is an ethical issue I have not really resolved as a transhumanist.

My part-effort is that at least the food I purchase for my personal use (not fast food) I buy as being labeled “cruelty-free” or “free-range” or whatever stamp shows some consideration has been given to the issue. I’ve investigated these terms only partially, but I do know they are better than factory farming. I imagine as synthetic meat becomes cheaper to produce, eventually being cheaper than normal meat, there will be a big public debate about whether the industry should be allowed to continue to exist.

There are some examples of using an animal as a “resource”, particularly for entertainment via either combat or zoosadism that are to various degrees considered widely unacceptable. An example of zoosadism would be the French “brûler les chats” in the 1800s where felines were set alight. This is an example of exploiting animals as a resource that is now considered universally abhorrent. An example of combat would be cock-fighting or dogfighting, both of which are now either illegal or frowned upon, at least in the USA.

As time progresses, it becomes easier and easier to give rights to animals without sacrificing human convenience and welfare.

Happily for all of us, science is lighting a path forward with the production of synthetic/artificial meat becoming cheaper and cheaper. It’s not even branded as some kind of futuristic/transhumanist alternative, it’s just a normal thing you can get. I was at Starbucks this morning and got an “Impossible” breakfast sandwich that had a thin slice of artificial meat and an egg, and I don’t think I would have known the difference if I hadn’t been told. It is fantastic that soon inexpensive artificial meat may be widely available everywhere at a cheaper price than “conventional” meat.

I think one industry that is still most highly defensible is animal experimentation because new discoveries are being made and new treatments are being developed. I don’t think the transhumanist community should be opposed to animal testing specifically for this reason. I came across this video on r/transhumanism which showed a small robotic device the size of a guinea pig that had wheels and a headlight that was controlled from the inside by a vivisected (cut out of the mouse but kept alive) mouse brain. As one commenter noted, the foreboding music doesn’t really help portray this in a progress-facing light, but here is the video https://v.redd.it/agf4tojf46k71

What an amazing leap forward in progress this research must be taking our development of brain-computer interfaces! How cool will it be when we can take our own heads and drop them into various mechanical vehicles and walkers. I think the sacrifice of a few lab rats is worth attaining this progress.

The AGI Laboratory subreddit can be found at https://www.reddit.com/r/AGILaboratory/

What do you see in the future of cybernetics technology?

There are certain individuals who have suffered the complete loss of a limb, such as a traumatic amputation or a congenital disorder. In these cases, a functional prosthesis can be a life-changing boon, but the technology is still very much a work in progress.

Right now we have a long way to go with artificial limbs, although we have made great progress even in the last couple of years. Angel Giuffria has a bionic arm that she uses to even be able to do archery. https://archery360.com/2017/04/11/can-bionic-arm-shoot-bow-yes-beyond-awesome/

In an ideal world, a prosthetic arm would be as flexible and responsive to the user’s intentions as a natural arm—and as appealing as a natural arm, too. In some cases, a prosthetic arm may be as simple as a hook or a pair of tongs, but for those who can afford it, a high-tech prosthetic arm in the future could be a luxury upgrade to a normal human arm. Upgrading parts of your body would become a new form of status symbol that people would pay premium prices for. People might even finance out the procedure like one can now do with the latest models of various cell phones.

For an example upgrade, imagine if you were to upgrade to an extra thumb on your left hand. This would “unlock” many new types of legerdemain and maneuvers previously impossible. London designer Dani Clode created just such a prosthesis, which you can watch here: https://www.youtube.com/watch?v=i1TkiN309_4

“Somehow we’ve turned it into this idea of fixing or replacing the human body, and that’s not what a prosthetic does, it extends the wearer’s ability, it extends the wearer’s self. It is an addition to the body.“

This beautiful view of human enhancement is intrinsically Transhumanist, and a big part of the “ethos” of transhumanism, which has to do with transcending the limitations imposed upon one by Nature. Technology broadly has served this function throughout human history, with smartphones today significantly enhancing the abilities of humans who use them.

With additional work on prostheses, you could even perhaps add a third arm on your right side! The arm could be made of bionic flesh with articulated joints, but it would be controlled by your own nerve impulses, thus leaving the rest of your arm to function normally. You could then control the extra arm with your brain, but also with your existing arms. You could move the extra arm like any other limb. Imagine for a moment if you could allow the arm to move on its own. You could let it move in much the same way that a natural arm moves, but even more efficiently. Imagine reaching for the knife in the kitchen and your other hand immediately grabbing an onion to place in front of you. Imagine adding in spices with one arm and vinegar or soy sauce with the other while your third-hand tosses the wok efficiently, rotating everything inside for an even cooking surface area. If enough extra motor power were added to the arm, additional modular components could be added on the end, like say a drill or another very small set of fingers to pick up small screws.

This may sound like comical science fiction today, but much of what people do with their smartphones today might have sounded the same to people 30 years ago. People often follow utility, and if someone made a cybernetic arm that was amazingly good at something they wanted many would buy it.

These and many other useful enhancements to the human body are not far off in the future. It is critical that we as Transhumanists not only promote, but celebrate such achievements, and do so very vocally!

With the 4th of July, Independence Day in the US, mere hours away I’m reminded of how passionately many people seek after their freedom from oppression, having their votes count, and building that brighter future they can be proud for their children to inherit.

Much of the problem today is that they haven’t been given the tools necessary for these goals to be manifest, a bit like if Amazon or Paypal was trying to handle their accounting with a paper, pencil, and handheld calculator. With e-governance systems built on the principles of collective intelligence, these systems can modernize in a way that can be easily and rapidly tallied, audited, and integrated at scale.

Note: If you want to see a change in voting and e-governance support us on:
AGI Laboratory on Wefunder. | Wefunder Discloser

Nations shouldn’t have to take days or weeks just to get a rough estimate, and further days and weeks to recount every vote by hand. These waste tremendous amounts of time and money for all parties that could be better invested in improving the lives of citizens, and as we’ve seen even such wasteful recount methods don’t avoid the debt of distrust.

In order to improve the intelligence with which governance operates we now require systems that can function at the local level, and all the way up to the national level. The population has to be engaged in the process and know that what they have to say can actually make a difference, even if their perspective isn’t popular in their region. Cooperation at each scale is essential to building any functional system of governance, and often solutions to problems come from unexpected places.

“Innovation Hubs” have become popular in recent years, and one consistent trend on those platforms has been that when experts in one field had a “Hard” problem to solve it was the experts in a completely different field who offered a working solution for it. Likewise, people in one political party can solve many problems themselves, but when they run into a problem they consider hard the solution to it may often prove easy from a different perspective. This demonstrates how diversity of perspective contributes to collective intelligence, and how every individual can contribute.

Fundamentally the e-governance process and voting don’t need to be more complicated for those using them than the systems people are already familiar with. People can have something like a news feed discussing anything that will be coming up for a vote, the contents of which may be challenged, verified, and have debiasing measures applied. People could add comments and suggestions when debating subjects to help refine the voting process, which could potentially be integrated into new versions of a proposal. The User Experience (UX) of these systems could be simple, intuitive, and familiar to the broadest possible audience.

By having these systems operate with graph databases and run on the COG Blockchain the authenticity of these systems could be robustly maintained, showing a readily verifiable history for quickly certifying any vote. The graph databases in particular also open up the possibility of systems in one locality learning from systems that faced a variety of similar situations elsewhere, helping each e-governance system to offer better options for voting over time.

Rather than each local government has to come up with their own ideas or try to repurpose others, they’ve been exposed to they could be presented with a variety of options that worked well in other similar regions, benefiting the most people. This could not only substantially reduce the burden on local governments, but also greatly improve the results as they are able to draw on a much broader and deeper sum of knowledge which grows and improves constantly.

The debiasing process benefits substantially from this networking and scaling of e-governance systems as well. For example, if one system recognizes misinformation being spread or corruption taking place those events could be verified, and the verified results made available to all other systems in the blockchain to prevent any further harm. This ability to rapidly recognize and respond to serious problems impacting the governance process at scale can serve as a sort of digital immune system. Even rendering systems highly resistant to common fallacies could save communities from tremendous amounts of wasted time and energy.

Collective Intelligence Systems allow for a group of humans to achieve better results than anyone human while reducing bias. When these systems are networked together they can create collectives of collectives, allowing every collective in the network to achieve better results than anyone collective in isolation could. At each increasing level of complexity new levels of cooperation can take shape, and better solutions to problems may be realized more easily.

Nations need not wage endless and pointless wars within themselves, pitting one portion of their populations against the other. As with many other kinds of warfare, this is both very wasteful and unethical, but this status quo need not continue.

There is a better way, one where these wars may end, and freedom begins. May everyone have a happy and safe 4th of July.

Photo credit: Sami Anas

Originally posted here: https://uplift.bio/blog/next-generation-voting-and-e-governance/

I really wanted to get input from the community and especially the people doing great research on AI safety such as Roman Yampolskiy.  I ended up posting on the private forum on Facebook that is mostly people from the Machine Intelligence Research Institute (MIRI) where I posted about our summit inviting people for free to attend this summit and one particular statement I made was,

“help decide how to open source a version of the mASI (mediated Artificial Superintelligence) system, and the creation of a community-driven effort to make these systems better.”  

I really did just want everyone’s opinions and help to understand them, their position and make better decisions.  Eliezer Yudkowsky who is a founder of MIRI said immediately when I posted,

“This is exactly the wrong thing to do; […] Open source in AGI is not a strategy, it is a suicide-murder.”

That last line really shocked me.  Why would anyone think that.  I continued to try to get some opinions but alas this turned out be a fairly unfriendly audience.  I was able to get some help from others in the field privately and two people even reached out to apologize for how I was treated which I appreciate it.

The bottom line is though when we polled everyone at the conference if AGI software should be open-sourced 82% voted for open sourcing.  With this vote I sent everyone a draft of my new book that does an engineering code level walkthrough of the mASI/Uplift system.  not only this but we are going to support open-sourcing the AGI research team software some of which includes the ones listed below:

Open Source AGI Research Code

Open Nars

is the open source version of the NARS project, a general-purpose AI system, designed in the framework of a reasoning system, which attempts to uniformly explain and reproduce many cognitive facilities, including reasoning, learning, planning, etc., so as to provide a unified theory, model, and system for AI as a whole. The ultimate goal of this research is to build a thinking machine.

GPT-2

Unsupervised Multitask Learner Language Model API.

OpenCog

OpenCog is an open-source software project aimed at directly confronting the AGI challenge, using mathematical and biological inspiration and professional software engineering techniques.

TalaMind

will soon be publicly available through an Apache license.

mASI 

Our collective intelligence research architecture based on ICOM.  This has been included in a book and will be made public shortly.  Contact me and I can see about getting you a copy.

LIDA

An intelligent agent, communicating by email. Built for the US Navy. Based on Baar’s Global Workspace Theory. Answers only one question: “What do I do next?”.

ACT-R

ACT-R is a cognitive architecture: a theory for simulating and understanding human cognition. Researchers working on ACT-R strive to understand how people organize knowledge and produce intelligent behavior. As the research continues, ACT-R evolves ever closer into a system that can perform the full range of human cognitive tasks: capturing in great detail the way we perceive, think about, and act on the world.

and there are many more.  We hope to raise awareness and rate these systems so you can judge for yourself and maybe help us build the first real AGI.

an alternative thought experiment by Nikola Danaylov

We suffer not from the events in our lives but from our stories about them. Epictetus

The most powerful stories are stories about things that don’t exist. Because our fictive language gave birth to legal fictions, social constructs, and imagined realities. So much so that today imagined things are more powerful than real things. Trees, rivers, fish, animals, and even the climate depend on our imaginary constructs for their future survival. There is no money, law, justice, inalienable human rights, religion, love, friendship, capitalism, corporations, nations, or humanity outside of our common imagination. Nevertheless, it is such fictitious entities that will decide the fate of the world, ourselves included.

The more fictitious a story is, the more powerful that story is, provided it has a large enough number of people embracing it. Because stories that spread don’t just win – they change the world. This is true of Christianity, Islam, Hinduism, and Buddhism as much as it is true of Communism, Capitalism, Humanism, Nationalism, Feminism, Trumpism, Black Lives Matter, Brexit, MeToo, or human rights. For example, the most popular story on our planet is money. Because almost everyone accepts and therefore believes in money. But money, regardless of its form – be it gold, bitcoin, or paper money like the dollar, is basically trust. Trust in a story, which in the case of bitcoin, doesn’t even have a physical representation but is entirely digital – i.e. fictitious.

In human civilization, not only everything but also everyone is a story. And that is true at every level we can think of – individually, collectively or globally, because each of those levels requires a story. The same person can embrace many different stories that give her meaning, which also sets the spectrum of what is and what is not possible for her. For example, someone can be a mother, daughter, vegetarian, lesbian, police officer, Muslim, black and American – all at once. And each of those stories provides such powerful meaning that the person may be willing to kill, live or die for it. Thus, while our identities are little more than a hodge-podge of often contradictory stories, they determine our actions. But if we change our story we change our identity. And if we change our identity we change our actions, and therefore we change our future.

Conversely, people who have not embraced or have lost their personal story feel lonely, unmotivated, lack meaning, feel depressed and are at risk of suicide. But people who have discovered their “calling” have basically found a compelling story and decided to embrace it as their own. When many people embrace the same story we can have large-scale cooperation among millions of humans, who are otherwise all strangers to each other. Thus, the power of our civilization is built on the power of our stories – our belief in them, our desire to spread them and our willingness to live or die by them.

Losing those stories is not merely devastating but, both figuratively and literally, potentially mortal. That is why Jacque Ellul noted that to destroy someone is to destroy their story. And this is true of individuals as much as it is true of groups of people – be it ethnic groups, corporations, organizations or nations. Therefore, we ought to be very careful in rewriting our story. Because if we end up destroying it without offering a better alternative we can end up destroying our civilization. As the Dark Mountain Manifesto warns:

Human civilization is an intensely fragile construction. It is built on little more than belief: belief in the rightness of its values; belief in the strength of its system of law and order; belief in its currency; above all, perhaps, belief in its future. [The Dark Mountain Manifesto page 5]

The power of story is hard to exaggerate because stories inform and guide our relationship with each other, the rest of the world and even the future. The good news is that stories are told by storytellers. So we can choose to get in charge of our stories and gain power by becoming the storytellers. Taking responsibility for and, ultimately, rewriting our stories is the path to our new selves and therefore our new future. This is where the ultimate power lies. And that is why, both Plato and the Hopi Indians recognized the fact that “those who tell the stories rule the world.”

One famous example of this exponential growth you might be familiar with if you are into the world of tech is, of course, Moore’s Law, but in “The Singularity is Near”, he shows that other technological fields including medicine and many other fields have been accelerating as well. Ray Kurzweil shows that technology has actually been accelerating since before the Stone Age, although a man in the Roman Empire would have not noticed any ramifications of progress considering that his grandchildren would not live in a very different society from the one his grandfather and he did. For the first time in recorded history, we are commonly thinking about where we will be in 100 years, where we will be in 50 years, and now we are even thinking about where we will be in a decade as technology progresses into the 21^st Century. If Ray Kurzweil is right, machines will have sentience and AI, or artificial intelligence, will be greater than human intelligence, resulting in a hypothetical event known as an “intelligence explosion” or “technological singularity”. After this point, machines will be much smarter than average human beings and will be able to carry on progress much faster than we can even begin to comprehend with our natural brains.

In the wake of the recognition of these future possibilities, many science fiction authors and scriptwriters have created a plethora of media to warn us that AI and future genetic augmentation poses many existential threats to the human race. Examples that now dominate the mainstream media include “Terminator”, “2001: A Space Odyssey”, “The Matrix”, and of course many more that warn us that AI might kill us all. “Gattaca” expresses the great fear of an unfair society of elitism in a genetically enhanced world where a man who was born naturally is unable to get his dream career because he wasn’t born with genetic modifications. In parallel, people demonize the idea of genetic modification by ruthlessly attacking GMOs and saying that they’re bad for us when GMOs have in fact solved famine in some parts of the world due to higher yields. People are always fearful of something they do not understand.

In the Golden Age of Science Fiction, a period during the mid-20th Century that saw many sci-fi works hitting the stage, spreading optimism and futurism, science fiction had a brighter outlook on the future. Isaac Asimov imagined future Spacer societies and a Galactic Empire in his Robot Series and Foundation Series. Gene Roddenberry took us on fantastic voyages across the stars in the Enterprise alongside Captain James T. Kirk and Spock, and other authors inspired visionaries to have a brighter outlook on the future as the Space Race sent the first humans to the Moon.

Today, we have, in a way, a form of cultural stagnation. While some still see the future in an optimistic light, it seems much more popular today to look at the future as a dystopia, and new age movements all over the place actually act like demonizing technology is some kind of “morally right” position. Despite the trends of growth continuing to accelerate, mainstream culture seems to be propagating more fear of the future than hope and inspiration. Why are we doing this? While I agree that dystopian sci-fi has it’s place and that we should in very deed analyze and contemplate existential risks in our future that we might steer clear, progress is going to happen and we are going to try everything we can to “play god”, as the enemies of transhumanism like to say transhumanists are trying to do. To them, of course, I say, “were we not created in God’s image? Did God not give the Earth to mankind? Were we not meant to achieve our full potential, to subdue the Earth and conquer it, bending it to our will?” Indeed, this phrase in Genesis seems to be divine permission to modify our bodies and accelerate a brighter future. However, this is mainly an appeal to my fellow religious folks who may be averse to progress. We are not playing God because, quite honestly, God would not even make that possible. We are just using our God-given talents to hack our own genetic code and modify the machinery of our initial, still quite wonderful creation. To those Christians who say that we are insulting God and telling him “you didn’t make me good enough”, the beauty of mankind is that we were in fact created with the ability to modify ourselves. Don’t modify yourself with the intention of insulting your creator, but with the intention of becoming closer to your creator. Why would he give us the ability for self-modification if he didn’t intend for us to use it? It’s like saying that we shouldn’t work out because self-improvement is some kind of blasphemy against God. Do you really believe God wants us to intentionally limit ourselves from our full potential?

Anyway, others may fear the coming of AI as a usurping of humanity as the apex predator upon this planet, and they may be afraid of a Skynet scenario where a rampant AI destroys us all. I argue that the solution is to merge ourselves with the machines, allowing us to cause ourselves to evolve. Ray Kurzweil and many other singularitarians would argue the same thing. By evolving our own bodies and replacing our cells with nanobots whereby we can enhance our brains to the point where neural signals travel at light speed, we will be able to keep up with AI in the evolutionary arms race to come. You can choose to live in fear in the face of the singularity that is coming, getting left behind in it’s wake, or you can step bodly and bravely forward into the new world that it will create, surpassing all your physical, mental, and morphological limitations and ending your mortality fully.

Like I said before in this blog, mainstream media is overwhelmingly sending out negative signals and warnings about the future, painting into the memespace, or ideaspace, of mainstream culture the idea that technology is a negative influence and that it should be contained and controlled. Society is largely crying back to the caves because they are fearful of what they don’t understand. This trend needs to cease. People need to see the light that the future is much brighter than they think. AI is coming, and the technological singularity is coming, and it’s going to be better than anyone can imagine. This is a call to arms. Artists and sci-fi writers who see the ramifications of the future and how it can create an abundant, prosperous utopia, I urge you to write science fiction that portrays AI not in a negative, but rather in a positive manner. Show AI in a benevolent form and show how it can aid humanity in it’s future quest for survival. Show how it can solve global problems like hunger and global warming and cure disease. Stories that put the Neo-Luddites in their place, and show that the pseudo-religious zeal of anti-progress-minded people is ultimately a negative factor only holding us back from creating a better world in the long run. Know and understand that the content in the mainstream media has a huge effect on the minds of the people, and indeed much of culture is shaped by what is put out there and consumed by the masses. TRANSHUMANISM NEEDS MORE POSITIVE SCIENCE FICTION TO HELP GAIN SUPPORT FOR THE MOVEMENT AND TO INSPIRE THE NEXT GENERATION OF SCIENTISTS AND INVENTORS TO DESIGN THE FUTURE!!!

In 2018 the concept of Mediated Artificial Superintelligence (mASI) [1] was first proposed. The mASI is a type of collective system. A mediator, in this case, refers to someone who is one of these parts, as is an Artificial Intelligence (AI) system. The mASI lets all of the parts think together more effectively.

Uplift is an example of an mASI system.

Technical Advantages

Many AI experts and enthusiasts have begun talking about the concept of Augmenting human intelligence [2] in recent years. It allows for higher productivity and quality of life while also removing the risks posed to employment by automation. Hybrid systems such as mASI technology serve this function exceedingly well for several reasons.

The collective intelligence of a group is fundamentally cumulative when paired with an mASI. The thinking of the mASI is stored in a graph database, which is capable of growing dynamically [3]. In this way, the mASI also remembers what it has done before. Each part of the system has its unique strengths, weaknesses, and experiences—as well as a unique collection of cognitive biases.

As a collective intelligence, these biases are one entity we can eliminate. [4].

There are many ways we can improve this system over time. Improvements may include moving the mASI closer to being independent and having improving speed. Moreover, it is essential to manage scale—and the system does this well.

The mASI is designed like Legos in that new parts can be added quickly to augment the system with new features. Such changes allow us to use other systems to feed the collective— and it also makes the mASI more powerful.

Psychological Advantages

Besides the technological advantages, such systems also facilitate strong psychological benefits. Some examples are those catering to psychology’s “pillars of meaning” [5].

Sense of Belonging: Members of a collective more easily work together and build common ground. This strengthens teams by building trust and belonging.

Purpose: A collective develops its shared vision. This builds on that common ground, trust, belonging, establishing, and growing its purpose.

Storytelling: A shared narrative forms as a collective communicates both internally and externally. Senses of belonging and purpose reinforce this, driving the story of individual members as they orbit within that narrative.

Transcendence: These factors combine to achieve psychological benefits not possible without a collective. A sense of transcendence may be realized by the “sense of being a small part of something greater”.

How mASI Technology Works Today

An mASI communicates with members of a team, creating thoughts based on that communication and from their own research. Topics can also be raised directly through the Thought Studio, with these thoughts termed knowledge graphs. They are then mediated by members of the team. This is much like how any conventional team meeting could influence the decisions of a team leader.

This process helps remove the negative effects of groups like “group think” or politics. The collective can make decisions based on how it feels about the decision.

Current Limitations

As with any new technology, there are some current limitations to be aware of. We also have engineering efforts in progress to overcome them.

Current graph databases do not support the scale needed to grow a system indefinitely. We have designed a new one that can do this, but it is not complete yet.

The current mASI system needs many architectural upgrades to be brighter than it is now.

Translating from a knowledge graph to something human-readable still needs improvements.

We would like to integrate an mASI into many other systems. Over time we will build more modules

Read the rest here: https://uplift.bio/UpliftWhitePaper.pdf

Chapter 2: The Story of Story
The limits of my language mean the limits of my world. Ludwig Wittgenstein

Humanity has searched for meaning since its beginning. And we find it in story. The story that we tell ourselves. Thus, a world devoid of meaning becomes meaningful. But this meaning is given by and designed for us. And it is created in language.

The truly unique feature of human language is not its ability to transmit practical information about animals, rivers, stones, and trees. This feature is present in the languages of many other species such as whales, dolphins, apes, elephants, birds and bats, even bees. What makes human language unique is its ability to transmit information about things that don’t exist in the physical world at all – like gods, money, law, ethics, corporations, and so on. In other words, what makes our language unique is its ability to tell fictional stories. That is why ours is a fictive language. And this fact is very important for the two key features of our civilization:

1. Large-scale cooperation: Neanderthals and Cro-Magnons rarely cooperated in groups larger than 150 [Dunbar’s number]. This placed a rather low upper limit on what they could accomplish. Homo Sapiens, however, when given a powerful enough story, can exhibit cooperation among millions of strangers working towards the same goal. And what we can accomplish is of unlimited scale. Both positive and negative examples abound in history from wars and genocides through sports events, religious rituals, social movements, construction projects such as the Great Wall of China, or technological and scientific ones such as the Large Hadron Collider and space exploration.

2. Fast [cultural] evolution: What this means is that when we change the story, we change the culture. When enough people switch the story they believe in we have a revolution. For example, in 1789 the population of France switched almost overnight from the story of the divine right of the king to the story of the sovereignty of the people. [“Liberté, égalité, fraternité.”] Now known as the French Revolution, this phenomenon shows how cultural revolutions, in contrast to genetic ones, are very fast. This is the main reason why humanity has outstripped all other species in evolutionary terms – because we are using culture, not genes, to evolve faster. Every revolutionary paradigm switch – be it the French Revolution or the Scientific Revolution or the Industrial Revolution, is accompanied by a respective cultural switch in story about what the dominant paradigm could or should be. That new story is in turn told by coining new language about new political or legal rights, scientific concepts, or business metaphors such as the invisible hand, evolution, survival of the fittest, etc. New stories require new language and thus the expansion of story necessitates the expansion of language and the expansion of language creates opportunities for new stories.

In short, without our fictive language, we can’t have a story. And without a human story, our human civilization will not exist. So, the story of story is the story of our language. Keneth Burke said it best:

Language does our thinking for us.