artificial intelligence

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Basic to AI, coignitive science. A subject to Purbanchal university,Nepal


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    Objectives of Course (Artificial Intelligence) Understand the definition of artificial intelligence Machine Learning Natural Language Expert Systems Neural Network Have a fair idea of the types of problems that can be currently solved by computers and those that are as yet beyond its ability.

    Introduction to AI Types of AI tasks:

    One possible classification of AI task is into 3 classes: Mundane tasks, Formal tasks and Experts tasks.

    1. Mundane tasks: by mundane tasks, all those tasks which nearly all of us can do routinely in order to act and interact in the world. This includes: perception, vision, speech, natural language

    (understanding, generation and translation), common sense reasoning, and robot control.

    2. Formal tasks: a. Games: Chess, backgammon, GO etc. To solve these problems we must explore a large

    number of solutions quickly and choose the best one.

    b. Mathematics: i. Geometry and logic theory: it proved mathematical theorems. It actually proved several

    theorems form classical math textbooks.

    ii. Integral calculus: programs such as Mathematica and Mathcad and perform complicated symbolic integration and differentiation.

    c. Proving properties of programs. E.g. correctness, manipulate symbols and reduce problem. 3. Expert task: by expert tasks means things that only some of people are good and which acquire

    extensive training. This includes:

    a. Engineering: Design, Fault finding, Manufacturing b. Planning c. Scientific analysis d. Medical diagnosis e. Financial analysis

    What is AI?

    AI is one of the newest disciplines, formally initiated in 1956 by McCarthy when the name was coined. The

    advent of computers made it possible for the first time for people to test models they proposed for learning,

    reasoning, perceiving etc.

    Definition may be organized into four categories:

    1. Systems that thinks like humans 2. Systems that act like humans 3. Systems that think rationally 4. Systems that act rationally

    1. Systems that thinks like humans: This requires getting inside of the human mind to see how it works and then comparing our computer programs to this. This is what cognitive science attempts to do. Another way to do this is to observe a human

    problem solving and argue that ones programs go about problem solving in a similar way.

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    For example, General Problem Solver (GPS) was an early computer program that attempted to model human

    thinking. The developers were not so interested in whether or not GPS solved problems correctly. They were

    more interested in showing that it solved problems like people, going through the same steps and taking

    around the same amount of time to perform those steps.

    2. Systems that act like humans: The first proposal for success in building a program and acts humanly was the Turing Test. To be considered

    intelligent a program must be able to act sufficiently like a human to fool an interrogator. The machine and

    the human are isolated from the person carrying out the test and messages are exchanged via a keyboard and

    screen. If the person cannot distinguish between the computer and the human being, then the computer must

    be intelligent. To pass this test requires: NLP (natural language processing), knowledge representation,

    automated reasoning, machine learning. A total Turing test also requires computer vision and robotics.

    3. Systems that think rationally: Aristotle was one of the first to attempt to codify thinking. For example, all computers use energy. Using energy always generates heat. Therefore, all computers generate heat.

    This initiates the field of logic. Formal logic was developed in the late nineteenth century. This was the first

    step toward enabling computer programs to reason logically. By 1965, programs existed that could, given

    enough time and memory, take a description of the problem in logical notation and find the solution, if one


    4. Systems that act rationally: Acting rationally means acting so as to achieve ones goals, given ones beliefs. An agent is just something that perceives and acts.

    In the logical approach to AI, the emphasis is on correct inferences. This is often part of being a rational agent

    because one way to act rationally is to reason logically and then act on ones conclusions.

    Foundation of AI:

    Philosophy: Logic, methods of reasoning, mind as physical system foundations of learning, language, rationality.

    Mathematics: Formal representation and proof algorithms, computation, (un) decidability, (in) tractability, probability. Philosophers staked out most of the important ideas of AI, but to move to a

    formal science requires a level of mathematical formalism in three main areas: computation, logic and

    probability. Mathematicians proved that there exist5s an algorithm to prove any true statement in first-

    order logic. Analogously, Turing showed that there are some functions that no Turing machine can

    compute. Although un-decidability and non-computability are important in the understanding of

    computation, the notion of intractability has had much greater impact on computer science and AI. A

    class of problems in called intractable if the time required to solve instances of the class grows at least

    exponentially with the size of the instances.

    Economics: utility, decision theory.

    Neuroscience: physical substrate for mental activity.

    Psychology: phenomena of perception and motor control, experimental techniques. The principle characteristic of cognitive psychology is that the brain processes and processes information. The claim

    is that beliefs, goals, and reasoning steps can be useful components of a theory of human behavior.

    The knowledge-based agent has three key steps:

    o Stimulus is translated into an internal representation. o The representation is manipulated by cognitive processes to derive new internal representations o These are translated into actions.

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    Computer engineering: building fast computers.

    Control theory: design systems that maximize an objective function over time.

    Linguistics: knowledge representation, grammar. Having a theory of how humans successfully process natural language is an AI-complete problem- if we could solve this problem then we would have

    created a model of intelligence.

    AI History:

    Intellectual roots of AI date back to the early studies of the nature of knowledge and reasoning. The dream of

    making a computer imitate humans also has a very early history.

    The concept of intelligent machines is found in Greek mythology. There is a story in the 8th

    century A.D

    about Pygmalion Olio, the legendary king of Cyprus.

    Aristotle (384-322 BC) developed an informal system of syllogistic logic, which is the basis of the first formal

    deductive reasoning system.

    Early in the 17th

    century, Descartes proposed that bodies of animals are nothing more than complex machines.

    Pascal in 1642 made the first mechanical digital calculating machine.

    In 1943: McCulloch and Pits propose modeling neurons using on/off devices.

    In 1950s: Claude Shannon and Alan Turing try to write chess playing programs.

    In 57: John McCarthy thinks of the name Artificial Intelligence.

    In 1960s: Logic Theorist, GPS (General Problem Solver), micro worlds, neural networks.

    In 1971: NP-Completeness theory casts doubt on general applicability of AI methods.

    In 1970s: Knowledge based system and Expert systems were developed.

    In 1980s: AI techniques in widespread use, neural networks rediscovered.

    The early AI systems used general systems, little knowledge. AI researchers realized that specialized

    knowledge is required for rich tasks to focus reasoning.

    The 1990's saw major advances in all areas of AI including the following:

    Machine learning, data mining

    Intelligent tutoring,

    Case-based reasoning,

    Multi-agent planning, scheduling,

    Uncertain reasoning,

    Natural language understanding and translation,

    Vision, virtual reality, games, and other topics.

    In 2000, the Nomad robot explores remote regions of Antarctica looking for meteorite samples.

    Limits of AI Today

    Todays successful AI systems operate in well-defined domains and employ narrow, specialized knowledge. Common sense knowledge is needed to function in complex, open-ended worlds. Such a system also needs to

    understand unconstrained natural language. However these capabilities are not yet fully present in todays intelligent systems.

    What can AI systems do?

    Todays AI systems have been able to achieve limited success in some of these tasks.

    In Computer vision, the systems are capable of face recognition

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    In Robotics, we have been able to make vehicles that are mostly autonomous. In Natural language processing, we have systems that are capable of simple machine translation. Todays Expert systems can carry out medical diagnosis in a narrow domain Speech understanding systems are capable of recognizing several thousand words continuous speech Planning and scheduling systems had been employed in scheduling experiments with the Hubble Telescope. The Learning systems are capable of doing text categorization into about a 1000 topics In Games, AI systems can play at the Grand Master level in chess (world champion), checkers, etc.

    What can AI systems NOT do yet?

    Understand natural language robustly (e.g., read and understand