Early Artificial Intelligence

Our Working Definition of AI Artificial intelligence is the study of how to make computers do things that people are better at or would be better at if: they could extend what they do to a World Wide Web-sized amount of data and not make mistakes.

Why AI? "AI can have two purposes. One is to use the power of computers to augment human thinking, just as we use motors to augment human or horse power. Robotics and expert systems are major branches of that. The other is to use a computer's artificial intelligence to understand how humans think. In a humanoid way. If you test your programs not merely by what they can accomplish, but how they accomplish it, they you're really doing cognitive science; you're using AI to understand the human mind." - Herb Simon

The Dartmouth Conference and the Name Artificial Intelligence J. McCarthy, M. L. Minsky, N. Rochester, and C.E. Shannon. August 31, "We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the conjecture that 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."

Time Line – The Big Picture Dartmouth conference. 1981Japanese Fifth Generation project launched as the Expert Systems age blossoms in the US. 1988AI revenues peak at $1 billion. AI Winter begins. academic $ academic and routine

The Origins of AI Hype 1950 Turing predicted that in about fifty years "an average interrogator will not have more than a 70 percent chance of making the right identification after five minutes of questioning" Newell and Simon predicted that "Within ten years a computer will be the world's chess champion, unless the rules bar it from competition."

Evolution of the Main Ideas Wings or not? Games, mathematics, and other knowledge-poor tasks The silver bullet? Knowledge-based systems Hand-coded knowledge vs. machine learning Low-level (sensory and motor) processing and the resurgence of subsymbolic systems Robotics Natural language processing

Symbolic vs. Subsymbolic AI Subsymbolic AI: Model intelligence at a level similar to the neuron. Let such things as knowledge and planning emerge. Symbolic AI: Model such things as knowledge and planning in data structures that make sense to the programmers that build them. (blueberry (isa fruit) (shape round) (color purple) (size.4 inch))

The Origins of Subsymbolic AI 1943 McCulloch and Pitts A Logical Calculus of the Ideas Immanent in Nervous Activity “Because of the “all-or-none” character of nervous activity, neural events and the relations among them can be treated by means of propositional logic”

Interest in Subsymbolic AI

The Origins of Symbolic AI Games Theorem proving

Games 1950Claude Shannon published a paper describing how a computer could play chess Art Samuel built the first checkers program 1957Newell and Simon predicted that a computer will beat a human at chess within 10 years. 1967MacHack was good enough to achieve a class-C rating in tournament chess. 1994Chinook became the world checkers championChinook 1997Deep Blue beat Kasparpov 2007Checkers is solved AI in Role Playing Games – now we need knowledge

Logic Theorist Debuted at the 1956 summer Dartmouth conference, although it was hand-simulated then. Probably the first implemented A.I. program. LT did what mathematicians do: it proved theorems. It proved, for example, most of the theorems in Chapter 2 of Principia Mathematica [Whitehead and Russell 1910, 1912, 1913].

Logic Theorist LT used three rules of inference: Substitution (which allows any expression to be substituted, consistently, for any variable) : From: A  B  A, conclude: fuzzy  cute  fuzzy Replacement (which allows any logical connective to be replaced by its definition, and vice versa): From A  B, conclude  A  B Detachment (which allows, if A and A  B are theorems, to assert the new theorem B): From man and man  mortal, conclude: mortal

Logic Theorist In about 12 minutes LT produced, for theorem 2.45:  (p  q)   p (Theorem 2.45, to be proved.) 1. A  (A  B)(Theorem 2.2.) 2. p  (p  q)(Subst. p for A, q for B in 1.) 3. (A  B)  (  B   A)(Theorem 2.16.) 4. (p  (p  q))  (  (p  q)   p)(Subst. p for A, (p  q) for B in 3.) 5.  (p  q)   p(Detach right side of 4, using 2.) Q. E. D.

Logic Theorist The inference rules that LT used are not complete. The proofs it produced are trivial by modern standards. For example, given the axioms and the theorems prior to it, LT tried for 23 minutes but failed to prove theorem 2.31: [p  (q  r)] )  [(p  q)  r]. LT’s significance lies in the fact that it opened the door to the development of more powerful systems.

Mathematics 1956Logic Theorist (the first running AI program?) 1961SAINT solved calculus problems at the college freshman level 1967Macsyma Gradually theorem proving has become well enough understood that it is usually no longer considered AI.

The Silver Bullet? Is there an “intelligence algorithm”? 1957GPS (General Problem Solver) Start Goal

The Silver Bullet? Is there an “intelligence algorithm”? 1971STRIPS Precondition: ONTABLE(x) HANDEMPTY CLEAR(x) Delete list:ONTABLE(x) HANDEMPTY Add list:HOLDING(x) A planning system for Shakey. Shakey

The Silver Bullet? Is there an “intelligence algorithm”? 1971STRIPS Precondition: ONTABLE(x) HANDEMPTY CLEAR(x) Delete list:ONTABLE(x) HANDEMPTY Add list:HOLDING(x) Does x have to be on the table? Are there other constraints on x? Might something else also happen? Are we guaranteed that we’re holding x if we try to pick it up?

But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives.

But What About Knowledge? Why do we need it? How can we represent it and use it? How can we acquire it? Find me stuff about dogs who save people’s lives. Two beagles spot a fire. Their barking alerts neighbors, who call 911.