946: ENIAC heralds the dawn of Computi
Dec 19, 2015
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
Definitions of AI Systems that think like humans Systems that act like humans Systems that think rationally Systems that act rationally
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.