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Cloud computing

2020-03-26T20:10:00.000+05:00

Cloud computing is the on-demand availability of computer system resources, especially data storage and computing power, without direct active management by the user. The term is generally used to describe data centers available to many users over the Internet.

top programming languages 2020

2020-03-17T22:30:00.000+05:00

Let us take a look at best Programming Languages to learn in 2020 for a job and for future prospects:

Python. Python undoubtedly tops the list. ...
Java.
C/C++ C/C++ is like the bread and butter of programming. ...
JavaScript. JavaScript is the “frontend” programming language. ...
Go programming language. ...
R. ...
Swift. ...
PHP.

object oriented programming

2020-03-16T22:48:00.000+05:00

Object-oriented programming (OOP) refers to a type of computer programming (software design) in which programmers define the data type of a data structure, and also the types of operations (functions) that can be applied to the data structure.

Object-oriented programming is a programming paradigm based on the concept of "objects", which can contain data, in the form of fields, and code, in the form of procedures. A feature of objects is an object's procedures that can access and often modify the data fields of the object with which they are associated

Which brand computer is best?

2020-03-15T18:15:00.000+05:00

Best & Worst Laptop Brands 2019
HP (86/100) HP has vaulted to No. ...
Asus (83/100) As they say, fortune favors the bold. ...
Dell (81/100) ...
Alienware (80/100) ...
Lenovo (76/100) ...
Razer (75/100) ...
Acer (75/100) ...
Microsoft (72/100)

Blockchain

2020-03-15T17:16:00.001+05:00

What is Blockchain?

If this technology is so complex, why call it “blockchain?” At its most basic level, blockchain is literally just a chain of blocks, but not in the traditional sense of those words. When we say the words “block” and “chain” in this context, we are actually talking about digital information (the “block”) stored in a public database (the “chain”).

“Blocks” on the blockchain are made up of digital pieces of information. Specifically, they have three parts:

Blocks store information about transactions like the date, time, and dollar amount of your most recent purchase from Amazon. (NOTE: This Amazon example is for illustrative purchases; Amazon retail does not work on a blockchain principle as of this writing)
Blocks store information about who is participating in transactions. A block for your splurge purchase from Amazon would record your name along with Amazon.com, Inc. (AMZN). Instead of using your actual name, your purchase is recorded without any identifying information using a unique “digital signature,” sort of like a username.
Blocks store information that distinguishes them from other blocks. Much like you and I have names to distinguish us from one another, each block stores a unique code called a “hash” that allows us to tell it apart from every other block. Hashes are cryptographic codes created by special algorithms. Let’s say you made your splurge purchase on Amazon, but while it’s in transit, you decide you just can’t resist and need a second one. Even though the details of your new transaction would look nearly identical to your earlier purchase, we can still tell the blocks apart because of their unique codes.

TOP TEN EMERGING TECHNOLOGY

2020-03-13T22:01:00.000+05:00

Emerging Technology

IoT. IoT is driving business changes by providing the data needed to improve marketing, increase sales, and decrease costs, the report found. ...
Artificial Intelligence (AI) ...
5G. ...
Serverless computing. ...
Blockchain. ...
Robotics. ...
Biometrics. ...
3D printing.

Hacking and its types

2020-03-07T03:28:00.001+05:00

What is Hacking?

Hacking is identifying weakness in computer systems or networks to exploit its weaknesses to gain access. Example of Hacking: Using password cracking algorithm to gain access to a system

Computers have become mandatory to run a successful businesses. It is not enough to have isolated computers systems; they need to be networked to facilitate communication with external businesses. This exposes them to the outside world and hacking. Hacking means using computers to commit fraudulent acts such as fraud, privacy invasion, stealing corporate/personal data, etc. Cyber crimes cost many organizations millions of dollars every year. Businesses need to protect themselves against such attacks.

In this tutorial, we will learn-

Before we go any further, let’s look at some of the most commonly used terminologies in the world of hacking.

Who is a Hacker? Types of Hackers

A Hacker is a person who finds and exploits the weakness in computer systems and/or networks to gain access. Hackers are usually skilled computer programmers with knowledge of computer security.

Hackers are classified according to the intent of their actions. The following list classifies hackers according to their intent.

Symbol	Description
	Ethical Hacker (White hat): A hacker who gains access to systems with a view to fix the identified weaknesses. They may also perform penetration Testing and vulnerability assessments.
	Cracker (Black hat): A hacker who gains unauthorized access to computer systems for personal gain. The intent is usually to steal corporate data, violate privacy rights, transfer funds from bank accounts etc.
	Grey hat: A hacker who is in between ethical and black hat hackers. He/she breaks into computer systems without authority with a view to identify weaknesses and reveal them to the system owner.
	Script kiddies: A non-skilled person who gains access to computer systems using already made tools.
	Hacktivist: A hacker who use hacking to send social, religious, and political, etc. messages. This is usually done by hijacking websites and leaving the message on the hijacked website.
	Phreaker: A hacker who identifies and exploits weaknesses in telephones instead of computers.

Machine learning/AI

2020-03-04T23:42:00.002+05:00

Artificial Intelligence and Machine

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

Introduction

Artificial intelligence (AI) has received increased attention in recent years. Innovation, made possible through the Internet, has brought AI closer to our everyday lives. These advances, alongside interest in the technology’s potential socio-economic and ethical impacts, brings AI to the forefront of many contemporary debates. Industry investments in AI are rapidly increasing [3], and governments are trying to understand what the technology could mean for their citizens. [4]

The collection of “Big Data” and the expansion of the Internet of Things (IoT), has made a perfect environment for new AI applications and services to grow. Applications based on AI are already visible in healthcare diagnostics, targeted treatment, transportation, public safety, service robots, education and entertainment, but will be applied in more fields in the coming years. Together with the Internet, AI changes the way we experience the world and has the potential to be a new engine for economic growth.

Current Uses of AI:

Although artificial intelligence evokes thoughts of science fiction, artificial intelligence already has many uses today, for example:

Email filtering: Email services use artificial intelligence to filter incoming emails. Users can train their spam filters by marking emails as “spam”.
Personalization: Online services use artificial intelligence to personalize your experience. Services, like Amazon or Netflix, “learn” from your previous purchases and the purchases of other users in order to recommend relevant content for you.
Fraud detection: Banks use artificial intelligence to determine if there is strange activity on your account. Unexpected activity, such as foreign transactions, could be flagged by the algorithm.
Speech recognition: Applications use artificial intelligence to optimize speech recognition functions. Examples include intelligent personal assistants, e.g. Amazon’s “Alexa” or Apple’s “Siri”.

The Internet Society recognizes that understanding the opportunities and challenges associated with AI is critical to developing an Internet that people trust. This is particularly important as the Internet is key for the technology behind AI and is the main platform for its deployment; including significant new means of interacting with the network. This policy paper offers a look at the key things to think about when it comes to AI, including a set of guiding principles and recommendations to help make sound policy decisions. Of particular focus is machine learning, a specific approach to AI and the driving force behind recent developments.

Artificial Intelligence – What it’s all about

Artificial intelligence (AI) traditionally refers to an artificial creation of human-like intelligence that can learn, reason, plan, perceive, or process natural language. [5]

Artificial intelligence is further defined as “narrow AI” or “general AI”. Narrow AI, which we interact with today, is designed to perform specific tasks within a domain (e.g. language translation). General AI is hypothetical and not domain specific, but can learn and perform tasks anywhere. This is outside the scope of this paper. This paper focuses on advances in narrow AI, particularly on the development of new algorithms and models in a field of computer science referred to as machine learning.

Machine learning – Algorithms that generate Algorithms

Algorithms are a sequence of instructions used to solve a problem. Algorithms, developed by programmers to instruct computers in new tasks, are the building blocks of the advanced digital world we see today. Computer algorithms organize enormous amounts of data into information and services, based on certain instructions and rules. It’s an important concept to understand, because in machine learning, learning algorithms – not computer programmers – create the rules.

Instead of programming the computer every step of the way, this approach gives the computer instructions that allow it to learn from data without new step-by-step instructions by the programmer. This means computers can be used for new, complicated tasks that could not be manually programmed. Things like photo recognition applications for the visually impaired, or translating pictures into speech. [6]

The basic process of machine learning is to give training data to a learning algorithm. The learning algorithm then generates a new set of rules, based on inferences from the data. This is in essence generating a new algorithm, formally referred to as the machine learning model. By using different training data, the same learning algorithm could be used to generate different models. For example, the same type of learning algorithm could be used to teach the computer how to translate languages or predict the stock market.

Inferring new instructions from data is the core strength of machine learning. It also highlights the critical role of data: the more data available to train the algorithm, the more it learns. In fact, many recent advances in AI have not been due to radical innovations in learning algorithms, but rather by the enormous amount of data enabled by the Internet.

How machines learn:
Although a machine learning model may apply a mix of different techniques, the methods for learning can typically be categorized as three general types:

Supervised learning: The learning algorithm is given labeled data and the desired output. For example, pictures of dogs labeled “dog” will help the algorithm identify the rules to classify pictures of dogs.
Unsupervised learning: The data given to the learning algorithm is unlabeled, and the algorithm is asked to identify patterns in the input data. For example, the recommendation system of an e-commerce website where the learning algorithm discovers similar items often bought together.
Reinforcement learning: The algorithm interacts with a dynamic environment that provides feedback in terms of rewards and punishments. For example, self-driving cars being rewarded to stay on the road.1

Why now?

Machine learning is not new. Many of the learning algorithms that spurred new interest in the field, such as neural networks [7], are based on decades old research. [8] The current growth in AI and machine learning is tied to developments in three important areas:

Data availability: Just over 3 billion people are online with an estimated 17 billion connected devices or sensors. [9] That generates a large amount of data which, combined with decreasing costs of data storage, is easily available for use. Machine learning can use this as training data for learning algorithms, developing new rules to perform increasingly complex tasks.
Computing power: Powerful computers and the ability to connect remote processing power through the Internet make it possible for machine-learning techniques that process enormous amounts of data. [10]
Algorithmic innovation: New machine learning techniques, specifically in layered neural networks – also known as “deep learning” – have inspired new services, but is also spurring investments and research in other parts of the field. [11]

Key Considerations

As machine learning algorithms are used in more and more products and services, there are some serious factors must be considered when addressing AI, particularly in the context of people’s trust in the Internet:

Socio-economic impacts. The new functions and services of AI are expected to have significant socio-economic impacts. The ability of machines to exhibit advanced cognitive skills to process natural language, to learn, to plan and to perceive, makes it possible for new tasks to be performed by intelligent systems, sometimes with more success than humans. [12] New applications of AI could open up exciting opportunities for more effective medical care, safer industries and services, and boost productivity on a massive scale.
Transparency, bias and accountability. AI-made decisions can have serious impacts in people’s lives. AI may discriminate against some individuals or make errors due to biased training data. How a decision is made by AI is often hard to understand, making problems of bias harder to solve and ensuring accountability much more difficult.
New uses for data. Machine learning algorithms have proved efficient in analyzing and identifying patterns in large amounts of data, commonly referred to as “Big Data”. Big Data is used to train learning algorithms to increase their performance. This generates an increasing demand for data, encouraging data collection and raising risks of oversharing of information at the expense of user privacy.
Security and safety. Advancements in AI and its use will also create new security and safety challenges. These include unpredictable and harmful behavior of the AI agent, but also adversarial learning by malicious actors.
Ethics. AI may make choices that could be deemed unethical, yet also be a logical outcome of the algorithm, emphasizing the importance to build in ethical considerations into AI systems and algorithms.
New ecosystems. Like the impact of mobile Internet, AI makes new applications, services, and new means of interacting with the network possible. For example, through speech and smart agents, which may create new challenges to how open or accessible the Internet becomes.

Challenges

Many factors contribute to the challenges faced by stakeholders with the development of AI, including:

Decision-making: transparency and “interpretability”. With artificial intelligence performing tasks ranging from self-driving cars to managing insurance payouts, it’s critical we understand decisions made by an AI agent. But transparency around algorithmic decisions is sometimes limited by things like corporate or state secrecy or technical literacy. Machine learning further complicates this since the internal decision logic of the model is not always understandable, even for the programmer. [13]

While the learning algorithm may be open and transparent, the model it produces may not be. This has implications for the development of machine learning systems, but more importantly for its safe deployment and accountability. There is a need to understand why a self-driving car chooses to take specific actions not only to make sure the technology works, but also to determine liability in the case of an accident.

Data Quality and Bias. In machine learning, the model’s algorithm will only be as good as the data it trains on – commonly described as “garbage in, garbage out”. This means biased data will result in biased decisions. For example, algorithms performing “risk assessments” are in use by some legal jurisdictions in the United States to determine an offenders risk of committing a crime in the future. If these algorithms are trained on racially biased data, they may assign greater risk to individuals of a certain race over others. [14] Reliable data is critical, but greater demand for training data encourages data collection. This, combined with AI’s ability to identify new patterns or re-identify anonymized information, may pose a risk to users’ fundamental rights as it makes it possible for new types of advanced profiling, possibly discriminating against particular individuals or groups.

The problem of minimizing bias is also complicated by the difficulty in understanding how a machine learning model solves a problem, particularly when combined with a vast number of inputs. As a result, it may be difficult to pinpoint the specific data causing the issue in order to adjust it. If people feel a system is biased, it undermines the confidence in the technology.

Safety and Security. As the AI agent learns and interacts with its environment, there are many challenges related to its safe deployment. They can stem from unpredictable and harmful behavior, including indifference to the impact of its actions. One example is the risk of “reward hacking” where the AI agent finds a way of doing something that might make it easier to reach the goal, but does not correspond with the designer’s intent, such as a cleaning robot sweeping dirt under a carpet. [15]

The safety of an AI agent may also be limited by how it learns from its environment. In reinforcement learning this stems from the so-called exploration/exploit dilemma. This means an AI agent may depart from a successful strategy of solving a problem in order to explore other options that could generate a higher payoff. [16] This could have devastating consequences, such as a self-driving car exploring the payoff from driving on the wrong side of the road.

There is also a risk that autonomous systems are exploited by malicious actors trying to manipulate the algorithm. The case of “Tay”, a chatbot deployed on Twitter to learn from interactions with other users, is a good example. It was manipulated through a coordinated attack by Twitter users, training it to engage in racist behavior. [17] Other examples of so-called “adversarial learning” include attacks that try to influence the training data of spam filters or systems for abnormal network traffic detection, so as to mislead the learning algorithm for subsequent exploitation. [18]

The ability to manipulate the training data, or exploit the behavior of an AI agent also highlights issues around transparency of the machine learning model. Disclosing detailed information about the training data and the techniques involved may make an AI agent vulnerable to adversarial learning. Safety and security considerations must be taken into account in the debate around transparency of algorithmic decisions.

Accountability. The strength and efficiency of learning algorithms is based on their ability to generate rules without step-by–step instructions. While the technique has proved efficient in accomplishing complex tasks such as facerecognition or interpreting natural language, it is also one of the sources of concern.

When a machine learns on its own, programmers have less control. While non-machine learning algorithms may reflect biases, the reasoning behind an algorithm’s specific output can often be explained. It is not so simple with machine learning.

Not being able to explain why a specific action was taken makes accountability an issue. Had “Tay”, the chatbot that engaged in racist behavior as mentioned in the prior section, broken a law (such as issuing criminal threats), would its programmers be held accountable? Or would the twitter users who engaged in adversarial training?

In most countries, programmers are not liable for the damages that flaws in their algorithms may produce. This is important, as programmers would likely be unwilling to innovate if they were. However, with the advancement of IoT technologies, such issues may become more immediate. As flaws in algorithms result in greater damages, there is a need for clarified liability on the part of the manufacturer, operator, and the programmer. With AI, the training data, rather than the algorithm itself, may be the problem. By obscuring the reasoning behind an algorithm’s actions, AI further complicates the already difficult questioon of software liability. And as with many fields, it may well be liability that drives change.

Social and Economic Impact. It is predicted that AI technologies will bring economic changes through increases in productivity. This includes machines being able to perform new tasks, such as self-driving cars, advanced robots or smart assistants to support people in their daily lives. [19] Yet how the benefits from the technology are distributed, along with the actions taken by stakeholders, will create vastly different outcomes for labor markets and society as a whole.

For consumers, automation could mean greater efficiency and cheaper products. Artificial intelligence will also create new jobs or increase demand for certain existing ones. But it also means some current jobs may be automated in one to two decades. Some predict it could be as high as 47% of jobs in the United States. [20] Unskilled and lowpaying jobs are more likely to be automated, but AI will also impact high-skilled jobs that rely extensively on routine cognitive tasks. Depending on the net-effect, this could lead to a higher degree of structural unemployment.

Automation may also impact the division of labor on a global scale. Over the past several decades, production and services in some economic sectors has shifted from developed economies to the emerging economies, largely as a result of comparatively lower labor or material costs. These shifts have helped propel some of the world’s fastest emerging economies and supports a growing global middle class. But, with the emergence of AI technologies, these incentives could lessen. Some companies, instead of offshoring, may choose to automate some of their operations locally.

The positive and negative impacts of AI and automation on the labor market and the geographical division of labor will not be without their own challenges. For instance, if AI becomes a concentrated industry among a small number of players or within a certain geography, it could lead to greater inequality within and between societies. Inequality may also lead to technological distrust, particularly of AI technologies and of the Internet, which may be blamed for this shift.

Governance. The institutions, processes and organizations involved in the governance of AI are still in the early stages. To a great extent, the ecosystem overlaps with subjects related to Internet governance and policy. Privacy and data laws are one example.

Existing efforts from public stakeholders include the UN Expert Group on Lethal Autonomous Weapons Systems (LAWS), as well as regulations like the EU’s recent General Data Protection Regulation (GDPR) and the “right to explanation” of algorithmic decisions. [21] How such processes develop, and how similar regulations are adopted or interpreted, will have a significant impact on the technology’s continued development. Ensuring a coherent approach in the regulatory space is important, to ensure the benefits of Internet-enabled technologies, like AI, are felt in all communities.

A central focus of the current governance efforts relates to the ethical dimensions of artificial intelligence and its implementation. For example, the Institute of Electrical and Electronics Engineers (IEEE) has released a new report on Ethically Aligned Design in artificial intelligence [22], part of a broader initiative to ensure ethical considerations are incorporated in the systems design. Similarly, OpenAI, a non-profit research company in California has received more than 1 billion USD in commitments to promote research and activities aimed at supporting the safe development of AI. Other initiatives from the private sector include the “Partnership on AI”, established by Amazon, Google, Facebook, IBM, Apple and Microsoft “to advance public understanding of artificial intelligence technologies (AI) and formulate best practices on the challenges and opportunities within the field”.

Despite the complexity of the field, all stakeholders, including governments, industry and users, should have a role to play to determine the best governance approaches to AI. From market-based approaches to regulation, all stakeholders should engage in the coming years to manage the technology’s economic and social impact. Furthermore, the social impact of AI cannot be fully mitigated by governing the technology, but will require efforts to govern the impact of the technology.

Guiding Principles and Recommendations

The Internet Society has developed the following principles and recommendations in reference to what we believe are the core “abilities” [23] that underpin the value the Internet provides. While the deployment of AI in Internet based services is not new, the current trend points to AI as an increasingly important factor in the Internet’s future development and use. As such, these guiding principles and recommendations are a first attempt to guide the debate going forward. Furthermore, while this paper is focused on the specific challenges surrounding AI, the strong interdependence between its development and the expansion of the Internet of Things (IoT) demands a closer look at interoperability and security of IoT devices. [24]

Ethical Considerations in Deployment and Design

Principle: AI system designers and builders need to apply a user-centric approach to the technology. They need to consider their collective responsibility [25] in building AI systems that will not pose security risks to the Internet and Internet users.

Recommendations:

Adopt ethical standards: Adherence to the principles and standards of ethical considerations in the design of artificial intelligence [26], should guide researchers and industry going forward.
Promote ethical considerations in innovation policies: Innovation policies should require adherence to ethical standards as a pre-requisite for things like funding.

Ensure “Interpretability” of AI systems

Principle: Decisions made by an AI agent should be possible to understand, especially if those decisions have implications for public safety, or result in discriminatory practices.

Recommendations:

Ensure Human Interpretability of Algorithmic Decisions: AI systems must be designed with the minimum requirement that the designer can account for an AI agent’s behaviors. Some systems with potentially severe implications for public safety should also have the functionality to provide information in the event of an accident.
Empower Users: Providers of services that utilize AI need to incorporate the ability for the user to request and receive basic explanations as to why a decision was made.

Public Empowerment

Principle: The public’s ability to understand AI-enabled services, and how they work, is key to ensuring trust in the technology.

Recommendations:

“Algorithmic Literacy” must be a basic skill: Whether it is the curating of information in social media platforms or self-driving cars, users need to be aware and have a basic understanding of the role of algorithms and autonomous decision-making. Such skills will also be important in shaping societal norms around the use of the technology. For example, identifying decisions that may not be suitable to delegate to an AI.
Provide the public with information: While full transparency around a service’s machine learning techniques and training data is generally not advisable due to the security risk, the public should be provided with enough information to make it possible for people to question its outcomes.

Responsible Deployment

Principle: The capacity of an AI agent to act autonomously, and to adapt its behavior over time without human direction, calls for significant safety checks before deployment, and ongoing monitoring.

Recommendations:

Humans must be in control: Any autonomous system must allow for a human to interrupt an activity or shutdown the system (an “off-switch”). There may also be a need to incorporate human checks on new decision-making strategies in AI system design, especially where the risk to human life and safety is great.
Make safety a priority: Any deployment of an autonomous system should be extensively tested beforehand to ensure the AI agent’s safe interaction with its environment (digital or physical) and that it functions as intended. Autonomous systems should be monitored while in operation, and updated or corrected as needed.
Privacy is key: AI systems must be data responsible. They should use only what they need and delete it when it is no longer needed (“data minimization”). They should encrypt data in transit and at rest, and restrict access to authorized persons (“access control”). AI systems should only collect, use, share and store data in accordance with privacy and personal data laws and best practices.
Think before you act: Careful thought should be given to the instructions and data provided to AI systems. AI systems should not be trained with data that is biased, inaccurate, incomplete or misleading.
If they are connected, they must be secured: AI systems that are connected to the Internet should be secured not only for their protection, but also to protect the Internet from malfunctioning or malware-infected AI systems that could become the next-generation of botnets. High standards of device, system and network security should be applied.
Responsible disclosure: Security researchers acting in good faith should be able to responsibly test the security of AI systems without fear of prosecution or other legal action. At the same time, researchers and others who discover security vulnerabilities or other design flaws should responsibly disclose their findings to those who are in the best position to fix the problem.

Ensuring Accountability

Principle: Legal accountability has to be ensured when human agency is replaced by decisions of AI agents.

Recommendations:

Ensure legal certainty: Governments should ensure legal certainty on how existing laws and policies apply to algorithmic decision-making and the use of autonomous systems to ensure a predictable legal environment. This includes working with experts from all disciplines to identify potential gaps and run legal scenarios. Similarly, those designing and using AI should be in compliance with existing legal frameworks.
Put users first: Policymakers need to ensure that any laws applicable to AI systems and their use put users’ interests at the center. This must include the ability for users to challenge autonomous decisions that adversely affect their interests.
Assign liability up-front: Governments working with all stakeholders need to make some difficult decisions now about who will be liable in the event that something goes wrong with an AI system, and how any harm suffered will be remedied.

Social and Economic Impacts

Principle: Stakeholders should shape an environment where AI provides socio-economic opportunities for all.

Recommendations:

All stakeholders should engage in an ongoing dialogue to determine the strategies needed to seize upon artificial intelligence’s vast socio-economic opportunities for all, while mitigating its potential negative impacts. A dialogue could address related issues such as educational reform, universal income, and a review of social services.

Open Governance

Principle: The ability of various stakeholders, whether civil society, government, private sector or academia and the technical community, to inform and participate in the governance of AI is crucial for its safe deployment.

Recommendations:

Promote Multistakeholder Governance: Organizations, institutions and processes related to the governance of AI need to adopt an open, transparent and inclusive approach. It should be based on four key attributes: Inclusiveness and transparency; Collective responsibility; Effective decision making and implementation and Collaboration through distributed and interoperable governance [27]

Acknowledgments

The Internet Society acknowledges the contributions of staff members, external reviewers, and Internet Society community members in developing this paper. Special acknowledgements are due to the Internet Society’s Carl Gahnberg and Ryan Polk who conducted the primary research and preparation for the paper, and Steve Olshansky who helped develop the document’s strategic direction and provided valuable input throughout the writing process.

The paper benefitted from the reviews, comments and support of a set of Internet Society staff: Constance Bommelaer, Olaf Kolkman, Konstantinos Komaitis, Ted Mooney, Andrei Robachevsky, Christine Runnegar, Nicolas Seidler, Sally Wentworth and Robin Wilton. Thanks to the Internet Society Communications team for shaping the visual aspect of this paper and promoting its release: Allesandra Desantillana, Beth Gombala, Lia Kiessling, James Wood and Dan York.

Special thanks to Walter Pienciak from the Institute of Electrical and Electronics Engineers (IEEE) for his significant contributions in his early review of the paper.

Finally, the document was immensely improved by the input of a variety of Internet Society community members. Their wide areas of expertise and fresh perspectives served to greatly strengthen the final paper.

Notes

[1] See: https://en.wikipedia.org/wiki/Artificial_intelligence
[2] For a full list of the abilities and principles that guide our work, see http://www.internetsociety.org/who-we-are/mission/values-and-principles
[3] See https://www.weforum.org/agenda/2016/06/investors-are-backing-more-AI-startups-than-ever-before
[4] See for example: UK House of Commons Science and Technology Committee’s “Robotics and artificial intelligence”, or White House Reports “Preparing for the Future of Artificial Intelligence” or “Artificial Intelligence, Automation, and the Economy”.
[5] See: https://en.wikipedia.org/wiki/Artificial_intelligence
[6] See the example of Aipoly, a smartphone app designed to assist people with visual impairment to navigate or identify objects by taking a photo and getting an audio description in return, https://techcrunch.com/2015/08/17/aipoly-putsmachine-vision-in-the-hands-of-the-visually-impaired/
[7] Neural networks is a computational approach modeled on the human brain.
[8] The history of neural networks is often described as starting with a seminal paper in 1943 by Warren McCulloch and Walter Pitts on how neurons might work, and where they modeled a simple neural network with electrical circuits.
[9] See https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of-internet-of-things-forecasts-and-marketestimates-2016/#67bdbe2e292d [10] Google’s ground breaking experiment “AlphaGo”, the first AI to beat the human champion at the board game Go used approximately 280 GPU cards and 1,920 standard processors. See http://www.economist.com/news/science-andtechnology/21694540-win-or-lose-best-five-battle-contest-another-milestone [11] A good example of such progress is the program Libratus, the first AI to beat several of the top human players in no-limit Texas Hold ‘Em poker- a game that has been notoriously difficult for an AI to win due to incomplete information about the game state. See https://www.wired.com/2017/02/libratus/[12] For example, the use of machine learning for medical image analysis, https://www.technologyreview.com/s/602958/an-aiophthalmologist-shows-how-machine-learning-may-transform-medicine/[13] Burrell, J. “How the machine ‘thinks’: Understanding opacity in machine learning algorithms”. (2016), http://journals.sagepub.com/doi/abs/10.1177/2053951715622512 [14] See, https://www.themarshallproject.org/2015/08/04/the-new-science-of-sentencing#.bwuhXcwqn [15] Amodei, Dario, et al. “Concrete problems in AI safety.” (2016), https://arxiv.org/abs/1606.06565 [16] Ibid
[17] See https://techcrunch.com/2016/03/24/microsoft-silences-its-new-a-i-bot-tay-after-twitter-users-teach-it-racism/ ; https://blogs.microsoft.com/blog/2016/03/25/learning-tays-introduction/#sm.0000u2hwzvo38fdew261y8vofjdej [18] Huang, Ling, et al. “Adversarial machine learning.” Proceedings of the 4th ACM workshop on Security and artificial intelligence. ACM, 2011. (2011), http://dl.acm.org/citation.cfm?id=2046692 [19] White House Report 2016: Artificial Intelligence, Automation and the Economy, https://obamawhitehouse.archives.gov/blog/2016/12/20/artificial-intelligence-automation-and-economy [20] Frey, C. B. and Osborne, M. A. “The Future of Employment: How Susceptible are Jobs to Computerization?” (2013), http://www.oxfordmartin.ox.ac.uk/publications/view/1314 [21] Goodman, B. and Flaxman, S. “European Union regulations on algorithmic decision-making and a” right to explanation” (2016), https://arxiv.org/abs/1606.08813 [22] See http://standards.ieee.org/develop/indconn/ec/autonomous_systems.html [23] For a full list of the abilities and principles that guide our work, see http://www.internetsociety.org/who-we-are/mission/values-and-principles [24] For more information about IoT and ISOC’s guiding principles in this area, please see our previous publications: – https://www.internetsociety.org/policybriefs/iot – https://www.internetsociety.org/doc/iot-overview [25] ISOC, “Collaborative Security: An approach to tackling Internet Security issues” (2015), http://www.internetsociety.org/collaborativesecurity [26] For an example, see the principles and standards under development by the IEEE, http://standards.ieee.org/develop/indconn/ec/autonomous_systems.html [27] For more information about the key attributes of multistakeholder governance, please see ISOC’s “Internet Governance – Why the Multistakeholder Approach Works”, https://www.internetsociety.org/doc/internet-governance-why-multistakeholder-approach-works

Artificial Intelligence, Technology

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software design

2020-03-04T01:34:00.003+05:00

Software design is the process by which an agent creates a specification of a software artifact, intended to accomplish goals, using a set of primitive components and subject to constraints. ... Software design usually involves problem solving and planning a software solution.

Wireshark

2020-03-02T18:41:00.003+05:00

If you find yourself troubleshooting network issues, and you have to inspect individual packets, you need to use Wireshark. Wireshark is the de facto, go-to, you-need-to-know-how-to-use, application to capture and investigate network traffic.

Since Wireshark is the be-all-end-all tool for this job, let’s go over some basics – like where to download, how to capture network packets, how to use the Wireshark filters, and more.

What is wireshark

Wireshark is an open-source network protocol analysis software program started by Gerald Combs in 1998. A global organization of network specialists and software developers support Wireshark and continue to make updates for new network technologies and encryption methods.

Wireshark is absolutely safe to use. Government agencies, corporations, non-profits, and educational institutions use Wireshark for troubleshooting and teaching purposes. There isn’t a better way to learn networking than to look at the traffic under the Wireshark microscope.

Software Quality Engineering

2020-03-01T00:30:00.001+05:00

Abstract

An integrated approach to software quality, reliability, and safety is described that is termed ‘software quality engineering’. It encompasses the three levels of quality assurance technology generally recognised in manufacturing, namely, product inspection, process control, and design improvement. Most software organizations today operate at the product-inspection level of technology (or lower). Achieving higher levels of quality assurance technology depends on establishing effective measurement, control, and process improvement mechanisms within the software enterprise.

RAM(random access memory

2020-02-27T01:01:00.000+05:00

RAM (pronounced ramm) is an acronym for random access memory, a type of computer memory that can be accessed randomly; that is, any byte of memory can be accessed without touching the preceding bytes. RAM is found in servers, PCs, tablets, smartphones and other devices, such as printers.

what is ram used for?

RAM (Random Access Memory) is short-term digital storage. Computers (and yes, your phone is a computer) use RAM mostly to hold data that active applications — along with the CPU and operating system's kernel — are using because RAM is very fast when it comes to reading and writing.

Computer types

2020-02-25T18:48:00.000+05:00

The four basic types of computers are as under:

1Supercomputer.
2Mainframe Computer.
3Minicomputer.
4Microcomputer.

Supercomputer and Mainframe

Supercomputer is a broad term for one of the fastest computers currently available. Supercomputers are very expensive and are employed for specialized applications that require immense amounts of mathematical calculations (number crunching). For example, weather forecasting requires a supercomputer. Other uses of supercomputers scientific simulations, (animated) graphics, fluid dynamic calculations, nuclear energy research, electronic design, and analysis of geological data (e.g. in petrochemical prospecting). Perhaps the best known supercomputer manufacturer is Cray Research.

Mainframe was a term originally referring to the cabinet containing the central processor unit or "main frame" of a room-filling Stone Age batch machine. After the emergence of smaller "minicomputer" designs in the early 1970s, the traditional big iron machines were described as "mainframe computers" and eventually just as mainframes. Nowadays a Mainframe is a very large and expensive computer capable of supporting hundreds, or even thousands, of users simultaneously. The chief difference between a supercomputer and a mainframe is that a supercomputer channels all its power into executing a few programs as fast as possible, whereas a mainframe uses its power to execute many programs concurrently. In some ways, mainframes are more powerful than supercomputers because they support more simultaneous programs. But supercomputers can execute a single program faster than a mainframe. The distinction between small mainframes and minicomputers is vague, depending really on how the manufacturer wants to market its machines.

Minicomputer

It is a midsize computer. In the past decade, the distinction between large minicomputers and small mainframes has blurred, however, as has the distinction between small minicomputers and workstations. But in general, a minicomputer is a multiprocessing system capable of supporting from up to 200 users simultaneously.

Workstation

It is a type of computer used for engineering applications (CAD/CAM), desktop publishing, software development, and other types of applications that require a moderate amount of computing power and relatively high quality graphics capabilities. Workstations generally come with a large, high-resolution graphics screen, at large amount of RAM, built-in network support, and a graphical user interface. Most workstations also have a mass storage device such as a disk drive, but a special type of workstation, called a diskless workstation, comes without a disk drive. The most common operating systems for workstations are UNIX and Windows NT. Like personal computers, most workstations are single-user computers. However, workstations are typically linked together to form a local-area network, although they can also be used as stand-alone systems.

N.B.: In networking, workstation refers to any computer connected to a local-area network. It could be a workstation or a personal computer.

Personal computer:

It can be defined as a small, relatively inexpensive computer designed for an individual user. In price, personal computers range anywhere from a few hundred pounds to over five thousand pounds. All are based on the microprocessor technology that enables manufacturers to put an entire CPU on one chip. Businesses use personal computers for word processing, accounting, desktop publishing, and for running spreadsheet and database management applications. At home, the most popular use for personal computers is for playing games and recently for surfing the Internet.

Personal computers first appeared in the late 1970s. One of the first and most popular personal computers was the Apple II, introduced in 1977 by Apple Computer. During the late 1970s and early 1980s, new models and competing operating systems seemed to appear daily. Then, in 1981, IBM entered the fray with its first personal computer, known as the IBM PC. The IBM PC quickly became the personal computer of choice, and most other personal computer manufacturers fell by the wayside. P.C. is short for personal computer or IBM PC. One of the few companies to survive IBM's onslaught was Apple Computer, which remains a major player in the personal computer marketplace. Other companies adjusted to IBM's dominance by building IBM clones, computers that were internally almost the same as the IBM PC, but that cost less. Because IBM clones used the same microprocessors as IBM PCs, they were capable of running the same software. Over the years, IBM has lost much of its influence in directing the evolution of PCs. Therefore after the release of the first PC by IBM the term PC increasingly came to mean IBM or IBM-compatible personal computers, to the exclusion of other types of personal computers, such as Macintoshes. In recent years, the term PC has become more and more difficult to pin down. In general, though, it applies to any personal computer based on an Intel microprocessor, or on an Intel-compatible microprocessor. For nearly every other component, including the operating system, there are several options, all of which fall under the rubric of PC

Today, the world of personal computers is basically divided between Apple Macintoshes and PCs. The principal characteristics of personal computers are that they are single-user systems and are based on microprocessors. However, although personal computers are designed as single-user systems, it is common to link them together to form a network. In terms of power, there is great variety. At the high end, the distinction between personal computers and workstations has faded. High-end models of the Macintosh and PC offer the same computing power and graphics capability as low-end workstations by Sun Microsystems, Hewlett-Packard, and DEC.

SQL(structure query language)

2020-02-20T18:34:00.001+05:00

SQL (pronounced "ess-que-el") stands for Structured Query Language. SQL is used to communicate with a database. According to ANSI (American National Standards Institute), it is the standard language for relational database management systems.

SQL (Structured Query Language) is a database management language for relational databases. SQL itself is not a programming language, but its standard allows creating procedural extensions for it, which extend it to functionality of a mature programming language.

python

2020-02-17T22:37:00.000+05:00

Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace.

What is Python used for?

Python is a general purpose and high level programming language. You can use Python for developing desktop GUI applications, websites and web applications. Also, Python, as a high level programming language, allows you to focus on core functionality of the application by taking care of common programming tasks.

javascript

2020-02-17T19:14:00.000+05:00

JavaScript, often abbreviated as JS, is an interpreted programming language that conforms to the ECMAScript specification. JavaScript is high-level, often just-in-time compiled, and multi-paradigm. It has curly-bracket syntax, dynamic typing, prototype-based object-orientation, and first-class functions

uses of javascript

JavaScript is a client scripting language which is used for creating web pages. It is a standalone language developed in Netscape. It is used when a webpage is to be made dynamic and add special effects on pages like rollover, roll out and many types of graphics.

CSS(cascading style sheet)

2020-02-16T21:11:00.000+05:00

Cascading Style Sheets is a style sheet language used for describing the presentation of a document written in a markup language like HTML. CSS is a cornerstone technology of the World Wide Web, alongside HTML and JavaScript.

HTML(hypertext markup language)

2020-02-16T21:06:00.000+05:00

Hypertext Markup Language is the standard markup language for documents designed to be displayed in a web browser. It can be assisted by technologies such as Cascading Style Sheets and scripting languages such as JavaScript
Example of a simple HTML page. Hypertext Markup Language (HTML) is the most common language used to create documents on the World Wide Web. HTML uses hundreds of different tags to define a layout for web pages. Most tags require an opening <tag> and a closing </tag>

How to Make Blog.

2020-02-15T21:58:00.001+05:00

Pick a blog name. Choose something descriptive.
Get your blog online. Register your blog and get hosting.
Customize your blog. Choose a free template and tweak it.
Write & publish your first post. The fun part!
Promote your blog. Get more people to read your blog.
Make money blogging.

Blogging

2020-02-15T21:56:00.001+05:00

A blog is a discussion or informational website published on the World Wide Web consisting of discrete, often informal diary-style text entries. Posts are typically displayed in reverse chronological order, so that the most recent post appears first, at the top of the web page.

Web development

2020-02-15T21:52:00.000+05:00

Web development

Web development is the work involved in developing a website for the Internet or an intranet. Web development can range from developing a simple single static page of plain text to complex web-based internet applications, electronic businesses, and social network services

Web languages.

HTML
CSS
JAVASCRIPT
PHP
PYHTON

Information and Computer Science

2020-02-14T23:38:00.000+05:00

Information and computer science (ICS) or computer and information science (CIS) (plural forms, i.e., sciences, may also be used) is a field that emphasizes both computing and informatics, upholding the strong association between the fields of information sciences and computer sciences and treating computers as a tool rather than a field.

Information science is one with a long history, unlike the relatively very young field of computer science, and is primarily concerned with gathering, storing, disseminating, sharing and protecting any and all forms of information. It is a broad field, covering a myriad of different areas but is often referenced alongside computer science because of the incredibly useful nature of computers and computer programs in helping those studying and doing research in the field – particularly in helping to analyse data^[1] and in spotting patterns too broad for a human to intuitively perceive. While information science is sometimes confused with information theory, the two have vastly different subject matter. Information theory focuses on one particular mathematical concept of information while information science is focused on all aspects of the processes and techniques of information

What is the best Engineering Degree

2020-02-13T10:32:00.001+05:00

CIVIL ENGINEERING ,
PETROLEUM ENGINEERING,
SOFTWARE ENGINEERING,
ELECTRICAL ENGINEERING,
MECHANICAL ENGINEERING,
AEROSPACE ENGINEERING,

Microprocessor

2020-02-12T16:08:00.001+05:00

A microprocessor is an electronic component that is used by a computer to do its work. It is a central processing unit on a single integrated circuit chip containing millions of very small components including transistors, resistors, and diodes that work together.

Microprocessor is a controlling unit of a micro-computer, fabricated on a small chip capable of performing ALU (Arithmetic Logical Unit) operations and communicating with the other devices connected to it. Microprocessor consists of an ALU, register array, and a control unit.

Microcontroller

2020-02-12T16:07:00.000+05:00

A microcontroller is a small computer on a single metal-oxide-semiconductor integrated circuit chip. In modern terminology, it is similar to, but less sophisticated than, a system on a chip; a SoC may include a microcontroller as one of its components.

Computer Experts reviews from computer expert in all over the world||Computer Experts

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Blockchain

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TOP TEN EMERGING TECHNOLOGY

Hacking and its types

What is Hacking? Introduction & Types

What is Hacking?

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Machine learning/AI

Artificial Intelligence and Machine

Introduction

Artificial Intelligence – What it’s all about

Machine learning – Algorithms that generate Algorithms

Why now?

Key Considerations

Challenges

Guiding Principles and Recommendations

Ethical Considerations in Deployment and Design

Ensure “Interpretability” of AI systems

Public Empowerment

Responsible Deployment

Ensuring Accountability

Social and Economic Impacts

Open Governance

Acknowledgments

software design

Wireshark

Software Quality Engineering

Abstract

RAM(random access memory

Computer types

SQL(structure query language)

python

javascript

CSS(cascading style sheet)

HTML(hypertext markup language)

How to Make Blog.

Blogging

Web development

Information and Computer Science

What is the best Engineering Degree

Microprocessor

Microcontroller