hello stephenreed
* Plural word form for any of these nouns ‘or’?
or (ProperNoun)
OR (Noun)
(y) The plural is ‘ors’.
(n) There is no plural.
(s) Skip this item.
(d) What are the definitions of this word?
Or provide the plural in quotes.
n

You are probably confused by this. My wife and sponsor, BethLynn Maxwell certainly was. Although I plan a help page to explain the dialog format to novice volunteer mentors, it needs to be clear and self-explanatory.

Here is what the workflow item is attempting to communicate.

Each item is a frequently occurring word from the defining words in WordNet or Wiktionary dictionary definitions. The items are restricted to the noun category in which the plural word form is currently missing, and are presented in order of usage frequency.

The first utterance, ‘hello stephenreed‘ was generated by Texai when I logged in. It was triggered by a hidden ‘hello’ utterance sent on my behalf by the chat client when the web page first appeared.

The next line ‘ * Plural word form for any of these nouns ‘or’?‘ is a prompt from Texai that was meant to communicate the non-inflected noun word form. It fails from a usability viewpoint for these reasons:

1. The word ‘or’ is not obviously a noun. It is the first workflow item because the word ‘or’ is the fourth most frequently occurring defining word, according to my tabulation. Although there are open-source part-of-speech taggers, available, I perhaps incorrectly assumed that simply tabulating words without regard to their part-of-speech usage would be OK. Consequently, Texai knows that it is a conjunction part of speech, but from the OpenCyc lexicon, Texai also believes that ‘in’ is a noun having the definition “a state in northwestern United States on the Pacific”. It would be somewhat more clear if ‘in’ were correctly presented as ‘IN’ but somehow in the transformation from the merged lexicons of WordNet, Wiktionary, OpenCyc and The CMU Pronouncing Dictionary, the upper case word form was incorrectly extracted in lower case.
2. The leading asterisk is supposed to indicate that Texai did not generate this utterance from logical propositions, as ultimately it should, but instead used an opaque character string assembled from an inflexible template.
3. The prompt fails to explain what else Texai is trying to say in the following lines.

The line ‘ or (ProperNoun)’ is supposed to indicate a non-inflected word form for ‘or’ that Texai believes is a proper noun. It fails for these reasons:

1. Because the English word ‘or’ is used predominately as a conjunction, it is very confusing for it to appear where one might expect a noun.
2. The word ‘or’ violates the capitalization rules for English proper nouns.

The line ‘OR (Noun)‘ is better because some mentors may realize that ‘OR’ is an abbreviation for the US State of Oregon.

The remaining lines suggest what the mentor can enter to teach Texai the requested noun plural word form. The mentor can type ‘y’ to confirm that Texai’s heuristic guess is correct, or type ‘n’ to say that there is no plural for the given word, type ‘d’ to request the noun definitions of the word, or finally the mentor can enter the correct plural word form as a quoted string.

Here are my current ideas to improve the usability of this dialog.

1. Using a large list of English noun non-inflected word forms available from Desi Quintans, I’ll filter the existing workflow list to requeue entries like ‘or’ into a separate exception workflow list for someone like me to process. The dialog script to handle the exception workflow list will require facilities to edit the existing Texai lexicon, for example to consolidate the two noun word form entries for ‘or’ into one: the proper noun ‘OR’. After this filtering, I expect the first worklist entry to be ‘person’, which is the 25th most frequently used defining word in the Texai lexicon.
2. I’ll automatically include the noun word sense definitions in the prompts, so mentors can immediately understand the word.
3. The dialog prompts will be re-written to more clearly communicate what is meant, even if the result is more verbosity. I plan for this version of the dialog prompts to be the novice mode, and in the near future have an available expert mode that can be requested by the mentor that features very concise prompts.

These remaining issues to be resolved are modest compared to the more technical issues already recently completed. The Texai dialog system has been migrated from my development computer to the production server and can be reached through my firewall from the Internet. I’ll email the URL when the dialog is ready for more testing by those of you who have already contacted me, and shortly thereafter I expect to put the URL on the Texai website, for your help and feedback.

Thanks in advance.

[update]

I’ve filtered the list of nouns to work on and there are 2220 that are on Quintans, list and are missing plural word forms. There are 17095 items in list of exceptions to work on. Most of these are WordNet words that do not appear on Quitans’s list such as biological, chemical, or geographic place names. Others are more difficult exceptions that will require new dialog scripts to edit the existing lexicon.

Furthermore, I’ve redesigned the prompts for teaching Texai missing noun plurals, as shown in this mock-up for the noun ‘person’.

[texai] The definitions of the noun ‘person‘ are:

1. Human being; individual
2. Specific human being
3. The physical body of a specified individual
4. Any individual or formal organization with standing before the courts
5. A linguistic category used to distinguish between the speaker of an utterance and those to whom or about whom he is speaking. See grammatical person

I reason that the plural of ‘person’ is ‘persons‘. Is this correct for any of the above definitions? Enter y/yes to accept my choice, enter n/no to teach me that ‘person’ has no plural, enter s/skip to go on to the next item, or enter the correct plural for ‘person’ in quotes.

Note that the last sentence in particular will be challenging to generate from a structured set of propositions because I’ll need to write new grammatical construction rules for the phrasing. That will be a future task, for now Texai will create the sentence via a hack – template substitution.

Your comments appreciated!

Of the present day used systems, which products do you consider good business cases of applied AI? I’m looking for concrete product examples (not just wide technologies), in either consumer, or business everyday usage, with high profitability, and/or impact factor.

Having read the proceedings and indeed attended several of the annual conferences of the Association for the Advancement of Artificial Intelligence, I easily formed a relevant list of topics to search. Here are the pertinent results from those searches.

• voice recognition products from Dragon
• autonomous industrial robots
• machine vision
• automated scheduling
• constraint satisfaction from Microsoft
• route planning
• symbolic computation
• machine translation
• automatic landing systems
• recommendation systems
• logistics optimization
• stock trading
• pattern recognition
• spam filters

So where is AI currently used in a business context? Obvious answer: many places.

My arguments have always been top-down (complexity has to be there
because the overall features of the system don’t seem to be
implementable without it), so the way I interpret these low-level
results is that the brain has evolved to have all that self organized
criticality down at the bottom BECAUSE that is the easiest way to sort
through all of the different designs, to find ones that give optimum
high level behavior.

But to me, its content confirms an approach that I’ve taken with Texai to tackle AGI complexity.

Two points caught my attention, quoting from the article:
(1)

As it processes information, the brain often synchronises large groups of neurons to fire at the same frequency, a process called “phase-locking”. Like broadcasting different radio stations at different frequencies, this allows different “task forces” of neurons to communicate among themselves without interference from others.

This phenomenon suggests to me that a message-organized architecture is cognitively plausible, in that phase locking permits the transmission of information between sender and receiver without interfering in other such interactions.
(2)

Small-world networks lie somewhere between regular networks, where each node is connected to its nearest neighbours, and random networks, which have no regular structure but many long-distance connections between nodes at opposite sides of the network (see diagram). Small-world networks take the most useful aspects of both systems. In places, the nodes have many connections with their neighbours, but the network also contains random and often long links between nodes that are very far away from one another.

For the brain, it’s the perfect compromise. One of the characteristics of small-world networks is that you can communicate to any other part of the network through just a few nodes – the “six degrees of separation” reputed to link any two people in the world. In the brain, the number is 13.

To date, I’ve organized Texai physically as a regular network, where each agent node is directly connected to its nearest neighbors. Messaging however allows the easy creation of a virtual small world network by having intermediate nodes merely relay messages for which they are not the recipient. The experimenters noted that the human brain has 13 degrees of separation between network nodes.

What would be the average degree of separation in a vastly distributed hierarchical network of downloaded Texai instances? To simplify the discussion, I assume that each node has only one superior and 10 subordinate nodes. Thus each level of the network has 10x the number of nodes as the next higher level. Clearly, the great majority of nodes are located at the lowest level, and I will use these terminal nodes to represent the average node. A terminal node can reach its 9 direct siblings by messaging through its directly superior node, for 2 degrees of separation. It can reach 99 indirect sibling terminal nodes by messaging through the superior node of its directly superior node, for 4 degrees of separation. Clearly, a much greater number of indirect sibling nodes can be reached by routing the messages through higher level superior nodes in the hierarchy tree. The maximum degree of separation would therefore be about twice the number of levels in the hierarchy tree.

This discussion about comparative degrees of separation between the human brain and Texai’s future network is not meant to suggest that it will take a vast number of Texai nodes to perform human-equivalent intellectual symbolic tasks. Rather, I hypothesize that only a few nodes will be required to perform for example, the symbolic understanding of English text. The intent of the future, vastly distributed network of Texai instances is to model the intellectual capability of human organizations.

What has OpenCyc accomplished that qualifies as step to AGI? Nothing.
The consensus on this list is that OpenCyc should be thrown out: it is
a distraction

During my employment at Cycorp, John DeOliveira and I lobbied successfully for an open edition of the Cyc knowledge base. John and I are still listed as OpenCyc administrators at SourceForge and John not only authored the OpenCyc web site, but subsequently founded the independent Cyc Foundation after leaving Cycorp. Because I was an OpenCyc developer, because I am no longer working for Cycorp, and because I am taking a somewhat different approach with Texai, I believe that I’m well qualified to describe OpenCyc, and argue that its accomplishments are steps toward AGI.

In order of importance, OpenCyc’s accomplishments are:

1. Published a free, well organized ontology
2. Provides an opaque, but free to use runtime object store and inference engine
3. Provides tools to browse, edit and query the knowledge base
4. Serves as the basis for intelligent applications via its Java and socket APIs

Ontology

The OpenCyc ontology is the world’s most comprehensive ontology of commonsense information. If one believes, as do I, that organizing symbolic information is step towards AGI, then OpenCyc is a step in the right direction. Texai uses OpenCyc content as extracted into RDF, selecting only the atomic terms and binary assertions about them. RDF was created by a former Cycorp employee, R. Guha, who was obviously influenced by the previous Cyc frame representation. Cycorp is now targeting OpenCyc at the Semantic Web.

Over 10000 OpenCyc terms have links to equivalent synonym sets in WordNet, thus leveraging the simple OpenCyc lexicon with a much richer lexical resource.

Runtime

The OpenCyc runtime provides an object store and deductive inference engine, hosting millions of logical propositions. Performance is adequate for simple deductive queries. A large number of special purpose inference modules support a very expressive, i.e. higher order, form of predicate calculus logic. I speculate that a Java version of the Cyc runtime is forthcoming.

Tools

The OpenCyc browser is a fast way to navigate around the OpenCyc ontology, and it is a portal into the remainder of the OpenCyc tool set for editing and querying the knowledge base.

Intelligent Applications

For many years, the US government has sponsored Cycorp’s participation in research projects having the goal of creating and demonstrating intelligent applications. Cycorp used the majority of those funds to create what the sponsor wanted, but also used a substantial portion to improve Cyc generally – its fundamental commonsense knowledge, and to improve its symbolic, deductive inference engine.

For Texai, the most relevant sponsored research was the DARPA Rapid Knowledge Formation project, having the goal of dramatically reducing the effort required to create very large knowledge bases.  I was the project’s first manager at Cycorp, and learned important lessons about the utility of English dialog.

If Texai sucessfully creates AGI, then that will be proof that OpenCyc’s accomplishments are indeed steps towards AGI.

I previously posted The Current State of Affairs in Pursuit of a Commonsense AI which has more to say about Cycorp and Cyc.

OpenCyc has a sophisticated context inheritance mechanism is an example of how belief contexts can be represented and processed. My implementation is simpler and thus much faster. The application code, e.g. parsing, either specifies an override context directly, or by omission, specifies the default persistence context of the particular entity that is being found, loaded or persisted. I use semantic Java annotation to associate the name of the default persistence context on the Java class of each persistent entity.

Ordinarily, finding objects for a mentor from the knowledge base proceeds by looking in the mentor’s belief context first, and only if not found there, then looking in the default context for that class of object.  Sometimes it is necessary to know if an object is located exactly in  a particular context.  The API for finding KB objects includes a parameter to indicate whether to find the objects exactly in the specified context.

Instead of trying to produce a programme to simulate the adult mind, why not rather try to produce one which simulates the child’s? If this were then subjected to an appropriate course of education one would obtain the adult brain.

Turing then elaborated:

We may hope that machines will eventually compete with men in all purely intellectual fields. But which are the best ones to start with? Even this is a difficult decision. Many people think that a very abstract activity, like the playing of chess, would be best. It can also be maintained that it is best to provide the machine with the best sense organs that money can buy, and then teach it to understand and speak English.

The latter is the approach of the Texai project. Lexical knowledge and skills will be acquired so that the system can bootstrap itself into ever increasing capabilities, as taught to it by volunteer mentors. Turing’s requirement for sophisticated sense organs is reduced by (1) a focus on English text rather than English speech, and by (2) the hypothesis that the development of sub-symbolic processing can be postponed and that sufficiently intelligent behavior can be achieved almost entirely by symbolic processing plus spreading activation, which is much simpler and needs dramatically less computing power.

Celebrating this notable date, I expect to release before midnight a online, rudimentary version of the Texai bootstrap dialog, that will enable volunteers to register and to teach Texai plural noun forms.

[UPDATE] I’m postponing the release for a few more days.  Today’s testing revealed that storing each volunteer mentor’s contributions in a separate belief context required small changes throughout the existing code.

I have another idea you might like less. What about giving the users
the possibility to modify the questions asked, early on? Even not
understood, someone could then try to reparse the questions-answers
later; but to have some mechanisms exposed in a “modify me” source of
the question-originating script.

Actually I like the idea of a volunteer mentor being able to modify the questions asked in a knowledge acquisition script.

I had been thinking of that rudimentary behavior modification as a form of skill acquisition to be postponed until further along in the bootstrapping process. I plan for Texai to record for later recollection and analysis, everything that it is told, even if it cannot understand what was said at the time. Accordingly, I may want the user to add a reason or two why their preferred form of a particular question is somehow contextually better.

Regarding the exposure of some scripting implementation mechanism to enable modification via dialog, I plan to expose a dialog interface that allows users to create and edit the TinyACT-R production rules that will govern Texai’s behavior. I hand-coded a few of these rules for the AGI-09 March demo. They are too tedious for the amount of scripting that I need to write in limited time, for even the noun plural acquisition scripts, but I speculate that there are many simple cases, where controlled dialog will be used to slightly modify a script, or to create a new script using analogy from some other script.

Ultimately, I hope that Texai can be taught to modify its behavior robustly without any hand-coding, by composing sophisticated, more capable skills, out of tediously created primitive skills.

This [spreading activation] does not provide reasons for any of the decisions made in any way that could be useful to the program. While it might seem reasonable to get the program to learn rudiments of language as a basis from which it would be able to comprehend (some) higher level reasons for decisions, the feasibility of this approach has never been demonstrated in any way.

A design guideline that I’ve followed for Texai is to have cognitively plausible processes when possible. Spreading activation is a well known cognitively plausible process that is described by John Anderson in his book “Atomic Components of Thought” and also at the ACT-R web site. I agree with a weakened version of your statement, namely that spreading activation does not provide deductive reasons for any of the rational decisions made by a Texai agent. Spreading activation is an efficient way to encode contextualized experience in a semantic network, and reinforcement learning can easily be applied to the the links.

I hypothesize that there are many decision making cases where symbolic deductive reasoning from ground facts and applicable rules is not required. In particular, the Texai project will experiment with parsing English utterances and making word sense disambiguation choices with spreading activation, not symbolic deductive reasoning – except as a fall back for certain harder-to-understand utterances. When I was employed at Cycorp, they experimented with deductive word sense disambiguation, and when it did work, it was very slow. Its clear to me that humans do not parse natural languages primarily using logical deduction.

I intend also to use spreading activation as a fact and rule selection heuristic during Texai deductive problem solving. On of the narrow-AI challenges facing Cycorp while I was there, was performing deep and robust deductive inference on the Cyc knowledge base, in a reasonable amount of time given a very large set of applicable facts and rules for certain queries. Again from my observations, its clear to me that humans do not perform deductive problem solving by applying a breadth-first, depth-first or iterative deepening search over a combinatorially explosive proof tree. I plan to filter the set of applicable rules and facts by spreading activation from the current proof state, as I suspect humans solve problems. This should be especially efficient when the problem solving activity has something in common with previously solved problems. John Anderson’s publications and those listed on his web site cover this issue to my satisfaction.

AI researchers complain about neural network solutions to classification or decision making, because the learned weights do not easily explain the decision process – its like a black box in that respect. Spreading activation is less opaque, because the highly activated links lead to explaining concepts. For example, when I think about feeding my cats, the link to the cat food storage location in my garage is activated, so therefore that place in my garage is important for a cat feeding event. If at some later point, Texai wants to deductively analyse its decisions and discover the principles behind them, then it can use the highly activated spreading activation links as a starting point in the analysis. A deductive analysis of cat feeding events, might be able to enumerate and to temporally order the sub-events by examining the concepts related by the spreading activation links.

http://wordnet.princeton.edu/glosstag

With a bit of work adapting this version to the WordNet 2.1 version that is the basis of the Texai lexicon, I should be able to add this tagging information to the Texai lexicon glosses, i.e. to the definitions of each word sense.  The tagging information will allow the Texai parser to unambiguously parse its own word sense definitions, when attempting to understand them.  Given this ability, it should then be possible to automatically gather potentially disambiguating information for each word sense from each discourse context in which it appears.  These automatically gathered contextualized relevance links would supplement, but not replace the need to gather additional ones from volunteer mentors.