Introduction - Artificial Intelligence - Lecture Slides, Slides of Artificial Intelligence

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Artificial Intelligence

Intelligence is in the eye of the beholder

• We perceive intelligent behaviors (or at least that’s what

we think)

• But what is intelligent behavior?
  • It deals with doing things that only humans do
  • It deals with something difficult
• Yet once it is achieved by a machine, we tend to disregard it

and set a new goal for intelligence.

• For example, an ATM dispenser

Intelligence: our current perspective

• Doing things that are difficult
• Mostly we deal with Learning and Adaptation
• We have let other areas of research to deal with AI
  • Robotics
  • Pattern Recognition
  • Expert and Tutoring Systems
  • Intelligent Agents
  • Knowledge Management
• Learning and adaptation deals with changing a behavior from past experiences
• Yet in order to learn we must choose among various alternatives, this is where knowledge management became an important goal in AI

Conception of Knowledge

5

Information Equality p = c p if and only if SI c

Austin = Capital of Texas

Knowledge example: Which is the best restaurant in Austin?

p => r (p IMPLIES r) judgment if City = Austin then the best restaurant is

An implication or a sound judgment (justified belief)

Product of experience or from a committed selection with the available information

A working hypothesis to be corroborated by the scientific method

A fact, true data (previously corroborated) o given as a postulate

Austin the capital of Texas ¿Is this Information or knowledge?

The Range of AI

7

Feedback and Cybernetics

Algorithms, Functions and Relations (Truth Processing)

Symbolical and Reasoning IF antecedent THEN consequence

Perceptua l

Learning and Adaptation

Common Sense and Concept Generation

AI Paradigms

for Knowledge Acquisition

• Preprogrammed Intelligence
  • Game playing and Planning
  • Representation and Heuristic Search
  • Symbolic processing
  • Logic and Knowledge
• Learning
  • Feedback and Adaptation
  • Connectionism
  • Neural Networks
  • Learning Automata
• Selection
  • Genetic Algorithms
  • Evolutionary Programming
  • Classifier Systems
  • Genetic programming (^8)

Inflection Point in BRETAM

10

B R E T A M

• While a breakthrough is being corroborated by researchers, a new breakthrough is found thus linking growth at the Inflection point (before d 2 fn/dt = 0 )
• In this fashion breakthrough and research are combined
• BRETAM phases occur horizontally (a new breakthrough) and vertically (at the same time)
• Innovation requires that new products create new routines in work habits

B

B

B

B R

T

A

E

B R E T A M

A breakthrough reflects

a new form not seen before

R E T A M

AI history using the BRETAM model adapted from Gaines

11

Digital^ B^ R^ T circuits

E A M

Hardware B R E T A M

Autonomy B R E

Acquisition B R E T

Cooperation (^) B R E

M

A M

T A M

T A M

R E T A M

1940 1948 1956 1964 1972 1980 1988 1996 2004 2012 2020 2028 2036 2044 Generations^0 1 2

1988^ throw away

research

2004

Interaction B R E T A M

knowledge B R E T A

Software B R E T A M

Collaboration B

1972^ invention

1980^ innovative

low cost

(^1996) line 1972

Cognitive Sciences

Computing Sciences

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