SLIDE 1
Ancient History
The intellectual roots of AI and intelligent machines
(human-like artifacts) in mythology
Mechanical
devices behaving with some degree
- f
intelligence.
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AI History CE417: Introduction to Artificial Intelligence Sharif - - PowerPoint PPT Presentation
AI History CE417: Introduction to Artificial Intelligence Sharif - - PowerPoint PPT Presentation
AI History CE417: Introduction to Artificial Intelligence Sharif University of Technology Spring 2017 Soleymani Ancient History The intellectual roots of AI and intelligent machines (human-like artifacts) in mythology Mechanical
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SLIDE 3
Modern History
By emerging modern computers, it became possible to
create programs performing difficult intellectual tasks.
From these programs, general tools are constructed which
have applications in a wide variety of everyday problems.
Emerging computing programmable devices (electronic
computers) was a major breakthrough to make intelligent systems.
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SLIDE 4
AI Timeline
1943
McCulloch & Pitts: Boolean circuit model of brain
1950
Turing's "Computing Machinery and Intelligence“ paper
1956
Dartmouth meeting: "Artificial Intelligence" term coined
1952-69
Early AI progress, great expectations
1965
Robinson's complete algorithm for logical reasoning
1966-73
AI discovers computational complexity Neural network research almost disappears
1969-79
Early development of knowledge-based systems
1980--
AI becomes an industry
1986--
Neural networks return to popularity
1987--
AI becomes a scientific method
1995--
The emergence of intelligent agents
2001--
AI on very large datasets
Early Successes Predictions that AI would eventually do almost anything Dark Age Crawl back Industrial & Scientific Age
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Periods in AI (briefly)
Early period - 1950’s & 60’s (mostly based on search)
Game playing (brute force), theorem proving (symbol manipulation),
biological models (neural networks)
Symbolic application period - 70’s
Early expert systems, use of knowledge
Commercial period - 80’s
knowledge/ rule bases
Scientific & Industrial period - 90’s and early 21st Century
Rapid advance due to greater use of solid mathematical methods and
rigorous scientific standards
Real-world applications
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SLIDE 6
The Gestation of AI (1943-1956) Neural Network
The first AI work: Modeling of Neurons
Warren McCulloch & Walter Pitts, 1943
Any computable function could be computed by some network of connected neurons
Learning neural network (Hebbian rule): updating rule for modifying
the weights of connection between neurons
Donald Hebb, 1949
First neural network computer (SNARC)
Marvin Minsky & Dean Edmonds (undergraduate students at Harvard), 1950
Minsky studied universal computation in neural networks during his
PhD at Princeton
Later, Minsky proved theorems showing limitations of NN 6
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The Gestation of AI (1943-1956) Turing
Alan Turing (1950)
“Computing Machinery and Intelligence” (1950) paper includes
a complete vision of AI
Turing introduced the Turing test, machine learning, genetic
algorithms, and reinforcement learning fields
First Chess Player Program
Claude Shannon & Alan Turing, 1950s
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The birth of AI (1956)
John McCarthy organized a 2 month workshop at Dartmouth College
McCarthy (Stanford), Minsky (MIT), Simon & Newell (CMU), Samuel (IBM) “every aspect of learning or any other feature of intelligence can in principle be so
precisely described that a machine can be made to simulate it.”
Achieved no new breakthroughs but AI was dominated by these people and their
students and colleagues for the next 20 years
“Artificial Intelligence” name was chosen by McCarthy during workshop
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SLIDE 9
The birth of AI (1956)
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Why AI becomes a separate field:
AI duplicates human faculties like creativity, self-improvement, and language use
Methodology: a branch of computer science and the only filed trying to build machines functioning autonomously in complex, changing environments
Newell and Simon from CMU presented the most general
program
Logic Theorist (LT) as a reasoning program (proved many mathematical
theorems)
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Early enthusiasm, great expectations (1952- 1969) - “Look, Ma, no hands!”
Many successes (in a limited way) in early years of AI
In few years computers from doing just arithmetic to machines did
anything remotely clever
General Problem Solver (GPS) – CMU (Simon & Newell, 1960)
Imitated human thinking
Geometry Theorem Prover – IBM (Gelenter, 1959)
proved theorems that many students of mathematics would find tricky
Checkers Player Machines (Arthur Samuel, 1952)
Using game tree search & Reinforcement Learning
McCarthy, MIT, 1958
LISP
,Time Sharing,Advice Taker (the first complete AI system)
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Early enthusiasm, great expectations (1952- 1969) - “Look, Ma, no hands!”
McCarthy (logic) vs. Minsky (anti-logical outlook) Minsky’s group chose limited problems known as microworlds
appeared to require intelligence to solve.
e.g. closed form calculus integration problems, geometric analogy
problems that appear in IQ tests, blocks world
NN of McCulloch-Pitts flourished
Enhancing learning byWidrow (1960, 1962) rules Perceptron by Rosenblatt (1962) and convergence theorem
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SLIDE 12
A dose of realty (1966-1973)
Herbert Simon, 1957
The power of AI will increase so rapidly that in a visible future, the range
- f problems they can handle will be coextensive to that of human.
Predictions did not come true
Problems (Early systems turned out to fail on wider selections or more difficult problems)
Most of early programs contained little or no knowledge of subject
matter
1966, “There is no Machine Translation for general scientific text and there
would be no in immediate prospect.”
Intractability of problems (“Combinatorial Explosion”)
Failed to prove theorems involving more than a dozen of facts Lighthill report, 1973 Cancellation of almost all AI research in G.B.
Fundamental limitations on basic structures used to generate intelligent
behavior
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Knowledge based systems: The key to power (1969-1979)
First decade of AI research
General purpose search mechanisms (weak methods – general but
cannot scale up)
Alternative – more powerful, domain specific knowledge
DENDRAL, 1969 - Inferring molecular structure MYCIN, 1971 - Diagnosis of blood infections with 450 rules Natural language understanding
Shrdlu – Blocks world Schank,Yale
Demands for workable knowledge representation schemes
(Prolog, PLANNER, Minsky’s idea of frames)
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AI becomes industry 1980-present
R1 Expert System at DEC, 1982
Configure orders for new computer systems
Saving $40 million per year
The Fifth Generation Project, 1981 (Japanese)
10 year plan to build intelligent computers running Prolog Counter attacks in U.S. and G.B.
From a few million dollars in 1980 to billions of dollars in
1988
Expert systems, vision systems, robots, software and hardware
specialized for these purposes
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SLIDE 15
The return of neural networks 1986-present
Reinvention of BACK-PROPAGATION
First in 1969, then in 1986.
Connectionist
Connectionist vs. Symbolic
Symbolism: manipulating knowledge of the world as explicit symbols
(e.g., words), where these symbols have clear relationships to entities in the world
Connectionism:
embodying knowledge by assigning numerical conductivities or weights to connections inside a network of nodes
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AI adopts the scientific method 1987- present
It is more common to build on existing theories than to
propose brand-new ones
To base claims on rigorous theorems (rather than intuition) and hard
experimental evidence (real applications rather than toy examples)
Early isolation of AI from the rest of computer science has been
abandoned (Neats defeated Scruffies)
Samples of revolutions
HMM for speech recognition and machine translation Baysian
network for uncertain knowledge representation and reasoning
NN became comparable to corresponding techniques (e.g. statistics)
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Emergence of Intelligent Agents 1995-present
“Whole Agent”
Reorganizing previously isolated subfields of AI
Influential founders of AI have expressed discontent with
the progress of AI
AI should put less emphasis on creating ever-improved version
- f applications that are good at a specific task
AI should return to its roots “machines that think, that learn,
and that create” (Human-level AI or HLAI)
Artificial General Intelligence (AGI), 2007
Universal algorithm for learning and acting in any environment
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SLIDE 18
Large Data Sets 2001-present
Data became more important than algorithm
Word-sense disambiguation
Performance increasing yield from using more data exceeds any
difference in algorithm choice
Filling in holes of a photograph
Poor when 10000 photos available while excellent when 2000000
photos in collection Knowledge bottleneck
Learning with enough data instead of hand-coded knowledge
engineering
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Game playing:
Deep Blue defeated Kasparov (1997) Problem solving
A mathematical conjecture (Robbins conjecture) unsolved for decades was proved (1996) Planning
NASA's autonomous planning program controlled the scheduling of operations for a spacecraft
US forces deployed an AI logistics planning and scheduling program DART that involved up to 50,000 vehicles, cargo, and people transportation (During 1991 GulfWar) Robotics & robot vehicles
NASA AI agent ran a satellite beyond Mars for over a day, without ground control (1999)
Sojourner, Spirit, and Opportunity explore Mars
NASA Remote Agent in Deep Space I probe explores solar system
DARPA grand challenge:Autonomous vehicle navigates across desert and urban
iRobot Roomba automated vacuum cleaner, and PackBot used in Afghanistan and Iraq wars Speech understanding systems for airline Spam filters using machine learning Machine translation by Google Question answering systems automatically answer factoid questions
Some samples of AI Successes
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SLIDE 20
Which of the following can be done at present?
Play a decent game of table tennis Drive in the center of Tehran Play a decent game of bridge Discover and prove a new mathematical theorem Write an intentionally funny story Give competent legal advice in a specialized area of law Translate spoken English into spoken Swedish in real time
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