Abridged history of AI

1943 McCulloch & Pitts: Boolean circuit model of brain based on artificial neurons
1950 Turing's "Computing Machinery and Intelligence“ – Turing test, subareas, machine learning, genetic algorithms

1952—69 Early enthusiasm, great expectations
1956 Dartmouth meeting: Phrase "Artificial Intelligence" adopted (John McCarthy, Minsky, Shannon, Newell/Simon…) Logic Theorist program
Next 20 years: dominated by MIT, CMU, Stanford, IBM
1950s Early AI programs, including Samuel's checkers program (learned to play better than its inventor), Newell & Simon's General Problem Solver, Gelernter's Geometry Engine
1958 Lisp McCarthy at MIT
1965 Robinson's complete algorithm for logical reasoning


Focus on Strong AI – solving math and logic problems, engaging in dialogues
Focus on Top-down approach – simulate concepts of human brain (planning, reasoning, language understanding, …)
Bottom-up approaches emerge – model low-level concepts (neurons, learning at much lower level, …)

Traditional top-down approach = Good-Old-Fashioned –AI (GOFAI)
Neat / Scruffy approaches

Neat: formal, pure, provable approach
Scruffy, messy: less provable but still yielding useful and significant results


AI’s Winter (a dose of reality) 1966-1973-mid 1980’s-now?
Predictions didn’t materialize
Simon 1957: in 10 years chess champion, major theorem proven. Actually, in 40 years.
Domain knowledge needed (eg automatic translation: ‘the spirit is willing but the flesh is weak’ <> ‘the vodka is good but the meat is rotten’ – US government funding on automatic translation projects cancelled.
AI discovers computational complexity – intractable problems.
Neural network research almost disappears (small mutation of programs …)
Negative results, eg. 1969 Minsky/Papert‘s Perceptrons


1969—79 Early development of knowledge-based systems
Early AI based on general-purpose search mechanisms. Weak methods – do not scale up. Alternative – use powerful domain-specific knowledge base
DENDRAL (first knowledge-intensive system)– infer molecular structure from info from mass spectrometer
Knowledge-based systems, expert systems; intensive work on knowledge representation

1973 Prolog (in France)


1980-present Results-oriented applications
Expert systems
NL systems
…

1981 Fifth Generation project (Japan), MCC (US) – never met their ambitious goals but lots of useful results, chips, …


1986– AI re-emerges
Strong AI – goal to emulate the full range of human cognitive capabilities
Weak AI – solve specific problems
Now focus on Weak AI, more realistic goals
Neural networks return to popularity
Connectionist models
Fuzzy logic, fuzzy controllers
Speech recognition and generation
Numerous applications
Artificial Life systems
Artificial Immune systems
Algorithms transition from AI algorithms to standard algorithms once they become practically useful


1987-- AI becomes a science
Build on existing theories rather than invent new
Real-world applications
A proper scientific methods – hypotheses are subjected to rigorous experiments, statistical analysis of results

Data mining
Gentle revolutions in robotics, computer vision, knowledge representation


1995-- The emergence of intelligent agents
Complete agent architecture
Web bots
1999 NASA – Deep Space I spacecraft – an agent to provide autonomy of spacecraft for limited durations of time