Artificial Intelligence (Part-III)- Developing Artificial Intelligence With Systematic Planning and Learning

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Systematic Knowledgebase with Common Sense are the Corner-Stone of Artificial Intelligence

Today we continue with the third part of our blog on artificial intelligence. Those who have missed our second blog can read it from Here. It will help to connect with this third part of the blog discussing the importance of proper and systematic use of knowledgebase and common sense to develop artificial intelligence.

Knowledge Engineering: The Corner-Stone to Classical Artificial Research

There are some expert systems to collect together accurate knowledge possessed by experts in some narrow domain. In addition, some projects attempt to collect the "fundamental knowledge" known to the average person into a database containing comprehensive knowledge about the world. Among the things a comprehensive fundamental knowledge base would contain are: objects, properties, categories and relations between objects; situations, events, states and time;  causes and effects; knowledge about knowledge and many other, less well researched domains. A depiction of "what exists" is an ontology: the set of objects, relations, approach, and properties formally described so that software agents can interpret them. The semantics of these are captured as description logic concepts, roles, and individuals, and typically implemented as classes, properties, and individuals in the Web Ontology Language. The most general ontologies are called upper ontologies, which attempt to provide a foundation for all other knowledge by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern). Such formal knowledge representations can be used in content-based indexing and retrieval, scene analysis, clinical judgement support, knowledge discovery,  and other areas.

Artificial Intelligence: Broad Combination of Common Sense and Knowledgebase

It’s within the grip of common people is to represent the knowledge as "facts" or "statements" that they could express verbally. For example, a chess master will avoid a particular chess position because it "feels too exposed" or an art authority can take one look at a statue and realize that it is a fictitious. These are non-conscious and sub-symbolic intuitions or tendencies in the human brain. Knowledge like this apprise, guide and provides a context for symbolic, cognizant knowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated AI, computational intelligence, or statistical AI will provide ways to perform this type of knowledge. The count of atomic facts that the moderate person knows is very huge. Research projects that pursuit to build a complete knowledge base of common-sense knowledge require huge amounts of laborious ontological engineering they must be develop, manually, one complicated concept at a time.

How Planning and Learning is done in Artificial Intelligence?

Multi-agent planning uses the assistance and counteraction of many agents to fulfil a given target. Appearing behaviour such as this is used by evolutionary algorithms and swarm intelligence. In humanistic planning problems, the agent can speculate that it is the only system acting in the world, allowing the agent to be certain of the countercoup of its actions. However, if the agent is not the only actor, then it requires that the agent can acumen under uncertainty. This calls for an agent that can not only assess its environment and make predictions, but also appraise its predictions and adapt based on its assessment. Computational learning theory can permit beginner by computational complexity, by sample complexity about the exact amount of data that is required, or by other notions of optimization. In brace learning the agent is rewarded for good feedback and punished for bad ones. The agent uses this sequence of rewards and punishments to form a strategy for running in its problem space. Unsupervised learning is the capability to search patterns in a torrent of input. Supervised learning includes both classification and numerical regression. Classification is used to determine what category something belongs in, after seeing a number of examples of things from several categories. Regression is the attempt to produce a function that describes the relationship between inputs and outputs and predicts how the outputs should change as the inputs change. Both classifiers and regression learners can be viewed as "function approximators" trying to learn an unknown (possibly implicit) function; for example, a spam classifier can be displayed as learning a function that maps from the written text of an email to one of two categories, "spam" or "not spam".

Intelligent agents must be capable to set targets and achieve them. They need a way to visualize the future a depiction of the state of the world and be able to make predictions about how their actions will change it and be able to make choices that maximize the utility (or "value") of available choices.

An Important Note to Always Keep in Mind 

Artificial Intelligence and the automated technology are one side of the life that always interest and wander us with the new ideas, topics, innovations, products …etc. AI is still not enforce as the films representing it(i.e. intelligent robots), however there are many important tries to reach the level and to challenge in market, like sometimes the robots that they show in TV. Nevertheless, the hidden projects and the advancements in industrial companies. 

Continued in the next blog...

Artificial Intelligence (Part-II)- Artificial Intelligence or Automated Machines?

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Artificial Intelligence or Automated Machines? Blessing or Curse to Our Mankind?

Now we continue with the second part of our blog on artificial intelligence. Those who have missed our first blog can read it from Here. It will help to connect with this second part of the blog discussing artificial intelligence or automated machines is what we are developing and is it a blessing or curse to mankind.

Artificial Intelligence can be a Good Problem Solver

In early days, researchers developed algorithms that burlesqued step-by-step reasoning that humans required when they solve puzzles or make logical deductions. In the late 1980s and 1990s AI research had built procedures for business with uncertain or incomplete data, employing concepts from probability and economics. It times of crisis, these algorithms proved wrong, since they are insufficient for solving larger critical and complicated problems because they experienced a combinatoric explosion. They became exponentially slower as the problems grew larger.  In fact, even humans barely use the step-by-step contemplation that early AI research was able to model. They solve most of their problems using fast, perceptive judgments.

Artificial Intelligence may act as a Social Intelligence

During the long terms and greater execution of process, social skills and an analyzing the  human emotion and game theory would be corners stone to a social agent. Being efficient to predict the actions of others by understanding their motives and emotional states would permit an agent to make better decisions. Some computer systems caricaturist human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise expedite human–computer interaction. Similarly, some virtual assistants are developed for doing conversations or even to chitchat amusingly; this tends to give ignorant users an impractical  or impossible conception of how knowledgeable existing computer agents actually are.  Medium successes related to effective computing include written text sentiment analysis and, more recently, multi-modal affect analysis (see multi-modal sentiment analysis), wherein AI classifies the affects advertised by a videotaped subject.

Building Motions in Artificially Intelligent Robots

In the year 1988,  Moravec's paradox concluded that low-level sensory motor dexterity that humans take for granted are, counter intuitively, difficult to build and programmed into a robot; the paradox is named after Hans Moravec. It is easy to make computers exhibit adult level performing systems on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility. This is the parameters to the fact that, unlike checkers, physical dexterity has been a direct target of natural selection for millions of years. Motion planning is the method of breaking down a movement task into "anthropophagite" such as individual joint movements. Such movement often affects compliant motion, a method where movement requires maintaining physical contact with an object. Modern robotic arms and other industrial robots, widely used in modern  business, can learn from involvement how to move efficiently despite the presence of friction and gear slippage. A modern mobile robot, when given a small, static, and visible environment, can easily determine its location and map its environment; however, dynamic environments, such as (in endoscopy) the interior of a patient's breathing body, pose a greater challenge.

Artificial Intelligence can be Threatening

As per researchers they agreed that a super imaginative AI is unlikely to illustrate human emotions like loving someone hate someone or getting angry with someone, and that there is no reason to expect AI to become intentionally philanthropic or malignant. As per experts AI may become dangerous and there are two scenarios:

1. The AI is Programmed to do something Disastrous

Automatically operated tools are artificial intelligence systems that are developed to kill. In the hands of the terrorists, these weapons could easily cause mass destruction or even they can destruct a part of the nation. Moreover, an AI arms race could recklessly cause to an AI war that also outputs to destruction of human race. To avoid being circumvent by the enemy, these tools would be developed to be extremely difficult to simply “turn off,” so humans could apparently lose control of such a footing. This threat is one that’s present even with narrow Automated Systems or AI, but grows as levels of AI intelligence and self-determination increase.

2. The Artificial Intelligence is developed to do something Fruitful, but it develops a Destructive Method for achieving our target

This is strikingly difficult for us to achieve and it would be too much dangerous when we rely on AI to fulfill this type of goals. If we operate an obedient intelligent car to take you to the airport as early as possible, it might get you there drive away by helicopters and covered in regurgitate, doing not what you wanted but literally what you asked for. If a super brained and automated system is encumbered with an aggressive or determined geoengineering project, it might bring out catastrophe with our ecosystem as a side effect, and view human attempts to block it as a risk to be met.

Continued in the next blog...

Artificial Intelligence (Part-I)- The Driving Force of Modern Digitalization

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Introduce Yourself to the World of Artificial Intelligence, a Giant Leap Towards Modern Digitalization

Artificial Intelligence (AI) is the most popular and desirable tool for ground breaking development in almost every field of science and technology. This tool is continuously paving the way for modern digitalization thus providing better performance and greatly reducing human effort. We will present you with a series of blogs to know and explore about this interesting field.

Introducing Artificial Intelligence

Artificial intelligence is a kind of intelligence exhibit by machines in comparison to the natural intelligence (NI) spread out by humans and other animals. In computer science AI research is described as the study of "intelligent agents": any device that recognize its environment and takes actions that maximize its chance of successfully achieving its goals. Colloquially, the term "artificial intelligence" is enforced when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Once the President of Future of Life Institute, Mr. Max Tegmark  quoted that-

"Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as we manage to keep the technology beneficial".

Starting from the world best AI driven applications up to self-driven car, AI is progressing rapidly. Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car). However, the long-term targets of many researchers is to develop general AI. While narrow AI may perform whatever its specific task is, like solving equations, AI would outperform humans at nearly every mental task. The ambit of AI is disputed: as machines become increasingly proficient, tasks considered as requiring "intelligence" are often eliminated from the definition, a method known as the AI effect, initializing to the quip, "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from "artificial intelligence", having become a routine technology. Efficiency generally classified as AI as of 2017 include successfully understanding man’s speech, challenging at the greatest level in strategic game systems, autonomous cars, intelligent routing in content delivery network and military counterfeiting. The field started on the claim that human intelligence "can be so minutely demonstrated that a machine can be made to counterfeit it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by illusion, fiction and philosophy since the end. Some people also consider AI to be an emergency to humanity if it progresses unabatedly. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment. In the twenty-first century, AI techniques have accomplished a resurgence following circumstantial advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to clarify many confronting problems in computer science.

Phrase History

Artificial intelligence was developed as an academic discipline in 1956, and in the years since has accomplished several waves of anticipation, followed by disappointment and the loss of funding. The study of mechanical or "formal" reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing's theory of computation, which advised that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This acumen, that digital computers can counterfeit any process of formal reasoning, is known as the Church–Turing thesis. Along with concurrent discoveries in neurobiology, information theory and cybernetics, this led researchers to contemplate the possibility of building an electronic brain. Turing expected that "if a human could not distinguish between replies from a machine and a human, the machine could be considered “intelligent". The first work that is now generally recognized as AI was McCullouch and Pitts' 1943 formal design for Turing-complete "artificial neurons". According to Bloomberg's Jack Clark, in the year 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI within Google increased from a "sporadic usage" in 2012 to more than 2,700 projects. Clark also presents factual data indicating that error rates in image processing tasks have fallen significantly since 2011. He aspect this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and data sets. Other cited examples include Microsoft's development of a Skype system that can automatically translate from one language to another and Facebook's system that can describe images to blind people.

Continued in the next blog...