1946: ENIAC heralds the dawn of Computing

I propose to consider the question: “Can machines think?” --Alan Turing, 1950

1950: Turing asks the question….

1995: RALPH takes a trip from coast to coast

CMU’s RALPH program drove a van for all but 52 milesof a trip from D.C. to San Diego

1996: EQP proves that Robbin’s Algebras are all boolean

[An Argonne lab program] has come up with a major mathematical proof that would have been called creative if a human had thought of it. -New York Times, December, 1996

----- EQP 0.9, June 1996 -----

The job began on eyas09.mcs.anl.gov, Wed Oct 2 12:25:37 1996

UNIT CONFLICT from 17666 and 2 at 678232.20 seconds.

---------------- PROOF ----------------

2 (wt=7) [] -(n(x + y) = n(x)).

3 (wt=13) [] n(n(n(x) + y) + n(x + y)) = y.

5 (wt=18) [para(3,3)] n(n(n(x + y) + n(x) + y) + y) = n(x + y).

6 (wt=19) [para(3,3)] n(n(n(n(x) + y) + x + y) + y) = n(n(x) + y).

…….

17666 (wt=33) [para(24,16426),demod([17547])] n(n(n(x) + x) ….

Jan 12, 1997: HAL 9000 becomes operationalin fictional Urbana, Illinois

…by now, every intelligent person knew that H-A-L is derived from Heuristic ALgorithmic -Dr. Chandra, 2010: Odyssey Two

May, 1997: Deep Blue beats the World Chess Champion

I could feel human-level intelligence across the room -Gary Kasparov, World Chess Champion (human)

vs.

For two days in May, 1999, an AI Program called Remote Agent autonomously ran Deep Space 1 (some 60,000,000 miles from earth)

Real-time ExecutionAdaptive Control

HardwareS

cripted E

xecutive

GenerativePlanner &Scheduler

Generative Mode Identification

& Recovery

Scripts

Mission-levelactions &resources

component models

ESL

Monitors

GoalsGoals

May, 1999: Remote Agent takes Deep Space 1 on a galactic ride

May 2000: SCIFINANCEsynthesizes programsfor financial modeling

Develop pricing models for complex derivative structures

Involves the solution of a set of PDEs (partial differential equations)

Integration of object-oriented design, symbolic algebra, and plan-based scheduling

Sept. 2002: Cindy Smart

will be marketed Vision: can read, tell

the time Speech recognition:

can recognize 700 words and 77 phrases

Voice synthesis: speaks with a soft voice

What else? Real-time response robustness autonomous intelligent interaction with the

environment planning communication with natural language commonsense reasoning creativity learning ???

Administrivia Textbook: Luger’s Artificial Intelligence,

2002, Addison Wesley Grading:– Assignments 40%– Midterm Exam 1 20%– Midterm Exam 2 20%– Final Exam 20%

Academic honesty

Contents PART I: Artificial Intelligence: Its Roots and

Scope– Chapter 1: AI: History and Applications

PART II: Artificial Intelligence as Representation and Search– Chapter 2: The Predicate Calculus– Chapter 3: Structures and Strategies for

State Space Search– Chapter 4: Heuristic Search– Chapter 5: Control and Implementation of

State-Space Search

Contents (cont’d) Part III: Representation and Intelligence:

The AI Challenge– Chapter 6: Knowledge Representation– Chapter 7: Strong Method Problem

Solving– Chapter 8: Reasoning in Uncertain

Situations

Contents (cont’d) Part IV: Machine Learning– Chapter 9: Machine Learning: Symbol-

based– Chapter 10: Machine Learning:

Connectionist– Chapter 11: Machine Learning: Social

and Emergent

Contents (cont’d) Part V: Advanced Topics for AI Problem

Solving– Chapter 12: Automated Reasoning– Chapter 13: Understanding Natural

Language

Contents (cont’d) Part VI: Languages and Programming

Techniques for AI– Chapter 14: An Introduction to Prolog– Chapter 15: An Introduction to Lisp

Part VII: Epilolgue– Chapter 16: Artificial Intelligence as

Empirical Enquiry

What is AI?

Figure 1.1: The Turing test.

Definitions of AI Systems that think like humans Systems that act like humans Systems that think rationally Systems that act rationally

Question:

What would impress you as an

“intelligent system?”

Important Research and Application Areas

Game playing Automated Reasoning and Theorem Proving Expert Systems Natural Language Understanding and Semantic

Modeling Modeling Human Performance Planning and Robotics Languages and Environments for AI Machine Learning Alternative Representations: Neural Nets and Genetic

Algorithms AI and Philosophy

Important Features of AI The use of computers to do reasoning, pattern

recognition, learning, or some other form of inference.

A focus on problems that do not respond to algorithmic solutions. This underlies the reliance on heuristic search as an AI problem-solving technique.

A concern with problem solving using inexact, missing, or poorly defined information and the use of representational formalisms that enable the programmer to compensate for these problems.

Important Features of AI (cont’d)

Reasoning about the significant qualitative features of a situation.

An attempt to deal with issues of semantic meaning as well as syntactic form.

Answers that are neither exact nor optimal, but are in some sense “sufficient.” This is a result of the essential reliance on heuristic problem-solving methods in situations where optimal or exact results are either too expensive or not possible.

Important Features of AI (cont’d)

The use of large amounts of domain-specific knowledge in solving problems. This is the basis of expert systems.

The use of meta-level knowledge to effect more sophisticated control of problem solving strategies. Although this is a very difficult problem, addressed in relatively few current systems, it is emerging as an essential area of research.