Early Artificial Intelligence

1
Early Artificial Intelligence
2
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.

3
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
4
The Dartmouth Conference and the Name Artificial
Intelligence
J. McCarthy, M. L. Minsky, N. Rochester, and C.E.
Shannon. August 31, 1955. "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."
5
Time Line The Big Picture
academic academic and routine
50 60 70 80
90 00 10
1956 Dartmouth conference. 1981 Japanese Fifth
Generation project launched as the Expert
Systems age blossoms in the US. 1988 AI revenues
peak at 1 billion. AI Winter begins.
6
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". 1957 Newell and Simon predicted
that "Within ten years a computer will be the
world's chess champion, unless the rules bar it
from competition."
7
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

8
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))
9
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
10
Interest in Subsymbolic AI
40 50 60 70 80 90
00 10
11
The Origins of Symbolic AI

• Games
• Theorem proving

12
Games

• 1950 Claude Shannon published a paper describing
  how
• a computer could play chess.
• 1952-1962 Art Samuel built the first checkers
  program
• 1957 Newell and Simon predicted that a computer
  will
• beat a human at chess within 10 years.
• 1967 MacHack was good enough to achieve a
  class-C
• rating in tournament chess.
• 1994 Chinook became the world checkers champion
• 1997 Deep Blue beat Kasparpov
• 2007 Checkers is solved
• AI in Role Playing Games now we need knowledge

13
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.

14
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

15
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.
16
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. LTs
significance lies in the fact that it opened the
door to the development of more powerful systems.
17
Mathematics
1956 Logic Theorist (the first running AI
program?) 1961 SAINT solved calculus problems at
the college freshman level 1967 Macsyma Gradu
ally theorem proving has become well enough
understood that it is usually no longer
considered AI.
18
The Silver Bullet?
Is there an intelligence algorithm? 1957 GPS
(General Problem Solver)
Start
Goal
19
The Silver Bullet?
Is there an intelligence algorithm? 1971 STRIPS
A planning system for Shakey.
Precondition ONTABLE(x) HANDEMPTY CLEAR(x)
Delete list ONTABLE(x) HANDEMPTY Add
list HOLDING(x)
20
The Silver Bullet?
Is there an intelligence algorithm? 1971 STRIPS
Precondition ONTABLE(x) HANDEMPTY CLEAR(x) D
elete 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 were holding x if
we try to pick it up?
21
But What About Knowledge?

• Why do we need it?

Find me stuff about dogs who save peoples lives.

• How can we represent it and use it?
• How can we acquire it?

22
But What About Knowledge?

• Why do we need it?

Find me stuff about dogs who save peoples lives.
Two beagles spot a fire. Their barking alerts
neighbors, who call 911.

• How can we represent it and use it?
• How can we acquire it?