* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Introduction to Artificial Intelligence
Survey
Document related concepts
Visual Turing Test wikipedia , lookup
Turing test wikipedia , lookup
Human-Computer Interaction Institute wikipedia , lookup
Technological singularity wikipedia , lookup
Artificial intelligence in video games wikipedia , lookup
Expert system wikipedia , lookup
Computer Go wikipedia , lookup
Computer vision wikipedia , lookup
Knowledge representation and reasoning wikipedia , lookup
Wizard of Oz experiment wikipedia , lookup
Human–computer interaction wikipedia , lookup
Intelligence explosion wikipedia , lookup
Existential risk from artificial general intelligence wikipedia , lookup
Embodied cognitive science wikipedia , lookup
Ethics of artificial intelligence wikipedia , lookup
Transcript
Introduction to Artificial Intelligence What is intelligence? • Human brain’s information processing ability. • OR ability of humans to demonstrate their intelligence by communicating effectively and by learning. ▫ Ability to understand, analyze, synthesize, and transmit information ▫ Ability to learn or adapt behavior to a new situation • Else ▫ Ability to solve problems, to reason with incomplete information, to think of new or unique solutions to a problem (creative) What is Artificial Intelligence (AI)? It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. (John McCarthy, Stanford University) Generally, AI is a branch of CS that focuses on intelligent aspects of human beings. • Minsky, M. ▫ Science of making computers do things that require intelligence if they are done by humans (Engineering perspective). • Durkin, J. ▫ A field of study in CS that pursues the goal of making a computer reasons in a manner similar to humans (Cognitive Science perspective). 4 categories of AI definition (Rusell & Norvig, 2003): • Systems that think like humans ▫ The exciting new effort to make computers think … machines with minds, in the full and literal sense (Haugeland, 1985). ▫ The automation of activities that we associate with human thinking, such as decision making, problem solving, learning … (Bellman, 1978). • Systems that think rationally ▫ The study of mental faculties through the use of computational models (Charniak & McDermott, 1985). ▫ The study of the computations that make it possible to perceive, reason and act (Winston, 1992). • Systems that act like humans ▫ The art of creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990). ▫ The study of how to make computers do things at which, at the moment, people are better (Rich & Knight, 1991). • Systems that act rationally ▫ Is the study of the design of intelligent agents (Poole et al., 1998). ▫ AI … is concerned with intelligent behavior in artifacts (Nilsson, 1998). 8 Viewing AI in two dimensions Thought processes & reasoning (Cognitive SciencePsychology perspective) Behavior (Engineering perspective) Thinking humanly Thinking rationally Cognitive modelling ‘Laws of thought’ Acting humanly Acting rationally Turing Test Agent Measure success against human performance Measure success against ideal concept of intelligence History of AI Prior to 1956 • SNARC – the first neural network computer was built in 1950 by Marvin Minsky and Dean Edmonds. • The Turing Test – an articulation of a complete vision of AI by Alan Turing. 1956 • Dartmouth workshop – the formal recognition of the name ‘artificial intelligence’ (proposed by John McCarthy). • The workshop clearly distinguished AI from control theory, operations research and decision theory (fields with similar objectives with AI). ▫ AI embraced the idea of duplicating human faculties such as creativity, self-improvement and language use. ▫ AI methodology emphasis on building machines. Enthusiasm and expectation period (1952-1969) • Success in a limited way, partly was due to limitation of computer itself. • LISP was introduced in 1958 by McCarthy. • The idea of microworlds, i.e. a limited domain in which its problem requires intelligence to solve (famous microworlds – blocks world). A dose of reality (1966-1973) • The popular statement by Herbert Simon (1957): “…there are now in the world machines that can think, that learn and create …”. • Simon’s prediction of a computer to be a chess champion in 10 years time has only came true 40 years later. • Early AI systems failed miserably in attempt to solve more complex problems. • A period of criticism and difficulties. Expert Systems (1969-1979) • Recognition of domain-specific knowledge as key to power. • The first ES named DENDRAL (Buchanan et al., 1969) was developed. Commercial AI (1980-present) • R1 (later known as XCON) ▫ The first successful commercial AI system ▫ Is an ES, performing configuration task. ▫ Implemented at Digital Equipment Corporation (DEC) and proved successful in saving millions of dollars per year. • More ES began to gain footholds in organizations particularly in the U.S and Japan. • Boomed to highest point in 1988. • “AI Winter” or dawn era after 1988 – again, failure was due to oversell the technology • The return of Neural Networks (1986-present) ▫ More advance algorithms being developed. • AI becomes a science (1987-present) ▫ Introduction of more advance AI techniques. ▫ Application in wide range of domain/field. • The emergence of intelligent agents (1995present) ▫ Introduction of intelligent agent. Source: Turban & Aronson (2001) The Turing Test This machine is trying hard to manipulate the statement given by the woman. TURING MACHINE This woman is trying to tell the interrogator the truth about herself. This interrogator is guessing with whom/what he is communicating, based on the statement given by both woman and machine. This game ends when the interrogator made his guess Turing machine pass the test if the interrogator fails to recognize with whom (or with what) he is communicating. Characteristics of AI system • Knowledge is key element ▫ Knowledge processing instead of data processing. ▫ Knowledge is represented in various forms E.g. logic, semantic network, rules, and trees on(a,b) A B C on(b,c) on(c,table) clear(a) S NP The birds fly VP D N V the birds fly s(np(det(the),noun(birds)),vp(v(fly))) • Focus on heuristics ▫ Heuristics is an informal, judgmental knowledge of an application area that constitutes the “rules of good judgment” in the field. ▫ It encompasses the knowledge of how to solve problems efficiently and effectively, how to plan steps in solving a complex problem, how to improve performance, and so forth. • Symbolic processing ▫ A string of characters that stands for some real-world concepts • Possess inference ability ▫ Inference from facts and rules using heuristics or other search techniques. ▫ Makes inference by employing pattern-matching approach. ▫ Reflects human inference process, i.e. relate current information with what is known (knowledge) when attempt to solve a problem. AI vs. Natural Intelligence Artificial Intelligence Natural Intelligence Consistent Not consistent Can be copied and transfer Cannot be copied and transfer Cost low High Can be documented Difficult to document Required steps of execution Creative Symbolic Input Observation Focus – Limited Focus – Wider AI vs. Conventional Program AI Program Based on the knowledge representation – dynamic Conventional Program Based on the steps defined – difficult to change/update Symbolic manipulation More to numeric manipulation Qualitative Quantitative Can perform reasoning and produce conclusion Cannot! AI systems attempt to solve 3 major types of tasks: • Mundane tasks ▫ Tasks that we do everyday Commonsense reasoning perception natural language understanding ▫ Related application – computer vision, speech recognition, pattern recognition, natural language processing, planning, neural networks, genetic algorithm and machine learning. • Formal tasks ▫ Much of the early works in AI focused on formal tasks such as game playing and theorem proving. Share the property that people who do them well are considered to be displaying intelligence. Computers could perform well at those tasks because they are fast at exploring a large number of solution paths and then selecting the best one. • Expert tasks ▫ Task that requires high-level intelligence of human experts (expertise) Diagnosis, interpretation, configuration, credit authorization. Related AI application is expert systems. Roots of AI Ideas, viewpoints and techniques of AI today come from various disciplines: • • • • • • • • Philosophy Mathematics Economics Neuroscience Psychology Computer engineering Control theory and cybernetics Linguistics Source: Turban & Aronson (2001) AI Tree.. Affective computing Machine Learning Speech Understanding Automatic Robotic Programming Natural Language Processing Expert System Intelligent Tutor Computer Vision Linguistics Game Playing Neural Network Fuzzy Logic Genetic Algorithm Data Mining Computer Science Psychology Management & Philosophy Management Science Electrical Engineering Applications of AI • Expert system (ES) A computer system that applies reasoning methodologies to knowledge in a specific domain to render advice or recommendations, much like a human expert. A computer system that achieves a high level of performance in task areas that, for human beings, require years of special education and training Medical diagnosis • Robotics and sensory systems ▫ Robots Machines that have the capability of performing manual functions without human intervention An “intelligent” robot has some kind of sensory apparatus, such as a camera, that collects information about the robot’s operation and its environment NeCoRo the robot cat – responds to human movement/emotions, has feelings and desires, remembers its name and acknowledges its name when called • Speech (voice) understanding ▫ Translation of the human voice into individual words and sentences understandable by a computer • Natural language processing (NLP) ▫ Using a natural language processor to interface with a computer-based system ▫ Two subfields of NLP ▫ Natural language understanding Natural language generation • Visual recognition / vision system ▫ The addition of some form of computer intelligence and decision-making to digitized visual information, received from a machine sensor such as a camera ▫ The basic objective of computer vision is to interpret scenarios rather than generate pictures KISMET • Intelligent computer-aided instruction (ICAI) ▫ The use of AI techniques for training or teaching with a computer ▫ Intelligent tutoring system (ITS) - selftutoring systems that can guide learners in how best to proceed with the learning process • Neural network ▫ An experimental computer design aimed at building intelligent computers that operate in a manner modeled on the functioning of the human brain. • Fuzzy logic ▫ Logically consistent ways of reasoning that can cope with uncertain or partial information; characteristic of human thinking and many expert systems • Genetic algorithms ▫ Intelligent methods that use computers to simulate the process of natural evolution to find patterns from a set of data GA steps • Intelligent agent (IA) ▫ An expert or knowledge-based system embedded in computer-based information systems (or their components) to make them smarter • Game playing ▫ One of the first areas that AI researchers studied ▫ It is a perfect area for investigating new strategies and heuristics because the results are easy to measure • Automotive • POD: emotion car.. • Toyota & Sony. • Snow driver emotion and learned from driver experiences. • measure your sweat, pulse • Affective Computing Others.. Microsoft Clippers Face Recognition NASA’s spacecraft scheduling operation controller