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Early History and Applications of AI: Exploring the Evolution and Future of Artificial Intelligence

Delve into the historical foundations, philosophical traditions, and application areas of artificial intelligence. Gain insights into the development of formal logic, the Turing Test, and biological/social models influencing AI research. Explore AI application domains like game playing, natural language understanding, and machine learning. Understand key features of AI, including heuristic problem-solving techniques, semantic reasoning, and domain-specific knowledge integration.

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Early History and Applications of AI: Exploring the Evolution and Future of Artificial Intelligence

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  1. CS 385 Fall 2006Chapter 1 AI: Early History and Applications

  2. Where are We Going? What is AI? What is intelligence? Our focus: • representation of knowledge • grades in a matrix • a sentence in a parse tree • exploitation of the representation (often via search) • find the averages • interpret what the sentence means Method • learn some important abstractions • logic programming in PROLOG to implement the ideas

  3. Working Definition of AI AI is the collection of problems and methodologies studied by artificial intelligence researchers • Recursive • Actually, not a bad definition • As such, a body of problems and techniques • Best way to understand AI is to study them

  4. Part 1 Early History and Applications 1.1 From Eden to Eniac: Attitudes towards Intelligence, Knowledge, and Human Artifice • a chance to put this course in the context of what else you know • recurring theme in our literature and mythology: intellectual ambition → disaster (Eve, Prometheus, Frankenstein) • does this relate to popular views of AI? 1.2 Overview of AI Application Areas • rest of the course: build the tools to attack some of these areas

  5. Back to 1.1 1.1.1 Historical Foundations. A good, but dense, discussion of the development of western thought. (Who were Aristotle, Copernicus, and Descartes?) 1.1.2 AI and the Rationalist and Empiricist Traditions • rationalism: the world can be described mathematically • early rationalist bias in AI lead to sterile, mechanical systems that couldn't "think." • empiricism: we only know what we see • how to represent knowledge here: association of related ideas synthesis of the two: stochastic modeling and associative theories

  6. 1.1 (cont.) 1.1.3 The Development of Formal Logic • 20th century: computers meant that formal reasoning as in predicate calculus could be mechanized on a computer and AI could develop 1.1.4 The Turing Test 1.1.5 Biological and Social Models • Criticisms of the rational/logical approach to AI (GOFAI) • New models of intelligence • biological inspired by genetic evolution (how we adapt and grow) • social inspired by social organizations (e.g. # of loaves of bread in NYC)

  7. The Turing Test Eliza

  8. 1.2: AI Application Areas 1.2.1 Game Playing 1.2.2 Automated Reasoning and Theorem Proving 1.2.3 Expert Systems 1.2.4 Natural Language Understanding and Semantic Modeling 1.2.5 Modeling Human Performance 1.2.6 Planning and Robotics 1.2.7 Languages and Environments for AI 1.2.8 Machine Learning 1.2.9 Alternative Representations: Neural Nets and Genetic Algorithms (very confusing section) 1.2.10 AI and Philosophy

  9. Important Features of Artificial Intelligence: • The use of computers to do reasoning, pattern recognition, learning, or some other form of inference. 2. A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem-solving technique. 3. A concern with problem solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems. 4. Reasoning about the significant qualitative features of a situation. 5. An attempt to deal with issues of semantic meaning as well as syntactic form. 6. Answers that are neither exact nor optimal, but are in some sense “sufficient.” This is a result of the essential reliance on heuristic problem-solving methods in situations where optimal or exact results are either too expensive or not possible.

  10. Important features of Artificial Intelligence: 7. The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems. 8. The use of meta-level knowledge to effect more sophisticated control of problem solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential area of research.

  11. Last Parag in 1.3: Some of the discussion suggests that straight rationalism is insufficient for AI and that • objects take on a meaning through their relationships with other objects. • This is equally true of the facts, theories, and techniques that constitute a field of scientific study. The facts/methods we will learn in this course will help us develop an understanding of the overall substance and directions of the field. Text in italics taken from Luger, p. 31.

  12. What You should Get from this Course Important models for abstraction • Many of these are creeping into "real" CS • AI identity crisis: once something can be done, it is not considered AI Introduction to the logic language PROLOG A sense of what the field is Interesting musings about intelligence and thinking How does this fit with the rest of your CS education?

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