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Foundations and Evolution of Artificial Intelligence: An Overview of Key Concepts and Historical Milestones

This document explores the foundations and evolution of artificial intelligence (AI) from its early theories and key contributors to its modern applications. It highlights milestones such as the Dartmouth Conference, the development of expert systems like DENDRAL and MYCIN, and advancements in neural networks and intelligent agents. Additionally, it outlines the various disciplines contributing to AI, including philosophy, computer engineering, and neuroscience, while discussing challenges like knowledge representation and the AI winter. This comprehensive overview emphasizes the journey of AI towards becoming a significant field of study and its practical importance.

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Foundations and Evolution of Artificial Intelligence: An Overview of Key Concepts and Historical Milestones

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  1. For Friday • Read chapter 2 • Homework: • Chapter 1, exercises 3, 11-13 • Send email tomecaliff@ilstu.edu from your preferred email address • Student information sheet

  2. What Do You Know? • Examples of artificial intelligence in your life? • Can you name any of the areas of AI?

  3. Homework • Intelligence • Artificial intelligence • Agent • Rationality • Logical reasoning • Evolution and rationality

  4. Foundations of AI • What disciplines have contributed to the development of artificial intelligence as a field?

  5. Foundations • Philosophy • Mathematics • Economics • Neuroscience • Psychology • Computer engineering • Control theory and cybernetics • Linguistics

  6. The Birth of AI • McCulloch and Pitts(1943) theory of neurons as competing circuits followed up by Hebb’s work on learning • Work in early 1950’s on game playing by Turing and Shannon and Minsky’s work on neural networks • Dartmouth Conference • Organizer: John McCarthy • Attendees: Minsky, Allen Newell, Herb Simon • Coined term artificial intelligence

  7. Early Years • What was the mood of the early years?

  8. Early Years • Development of the General Problem Solver by Newell and Simon in 1960s. • Arthur Samuel’s work on checkers in 1950s. • Frank Rosenblatt’s Perceptron (1962) for training simple networks

  9. At MIT • Marvin Minsky and John McCarthy • Development of LISP • SAINT: solved freshman calculus problems • ANALOGY: solved IQ test analogy problems • SIR: answered simple questions in English • STUDENT: solved algebra story problems • SHRDLU: obeyed simple English commands in the blocks world

  10. Early Limitations • Solved toy problems in ways that did not scale to realistic problems • Knowledge representation issues • Combinatorial explosion • Limitations of the perceptron were demonstrated by Minsky and Papert (1969)

  11. Knowledge Is Power: The Rise of Expert Systems • Discovery that detailed knowledge of the specific domain can help control search and lead to expert level performance for restricted tasks • First expert system was DENDRAL. It interpreted mass spectogram data to determine molecular structure. Developed by Buchanan, Feigenbaum and Lederberg (1969).

  12. Other Early Expert Systems • MYCIN: Diagnosis of bacterial infection (1975) • PROSPECTOR: Found molybdenum deposit based on geological data (1979) • R1: Configured computers for DEC (1982)

  13. AI Becomes an Industry • Numerous expert systems developed in 80s • Estimated $2 billion by 1988 • Japanese Fifth Generation project started in 1981. • MCC founded in 1984 to counter Japanese. • Limitations become apparent: prediction of AI Winter • Brittleness and domain specificity • Knowledge acquisition bottleneck

  14. Rebirth of Neural Networks • New algorithms (re)discovered for training more complex networks (1986) • Cognitive modeling • Industrial applications: • Character and hand-writing recognition • Speech recognition • Processing credit card applications • Financial prediction • Chemical process control

  15. AI Becomes a Science • Empirical experiments the norm • Theoretical underpinnings are important • The “See what I can do” approach is no longer an acceptable method for doing research • Some movement toward learning/statistical methods.

  16. Rise of Intelligent Agents • Why?

  17. Popular Tasks of Today • Data mining • Intelligent agents and internet applications • softbots • believable agents • intelligent information access • Scheduling applications • Configuration applications

  18. State of the Art • Deep Blue beats Kasparov • NASA’s Remote Agent program controls a spacecraft autonomously • High accuracy continuous speech recognition with fairly large vocabularies • Usable natural language interface to air travel system • No Hands Across America: Automated vehicle drives cross-country on freeways

  19. State of the Art • Medical diagnosis in specialized fields is sometimes assisted by AI programs • AI logistics programs were critically important in the Gulf War. • PROVERB can solve crossword puzzles faster than most humans.

  20. Views of AI • Weak vs. strong • Scruffy vs. neat • Engineering vs. cognitive

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