Why Artificial Intelligence is Very Hard

1. Why Artificial Intelligence is Very Hard Theo Pavlidis Distinguished Professor Emeritus Stony Brook University t.pavlidis@ieee.org http://theopavlidis.com

2. What is Artificial Intelligence? • A machine that replicates the functionality of the human brain. (General or Strong AI) “Around the Corner” since about 1945. • A machine that does a specific task that traditionally has been done by humans. (Narrow or Weak AI). Each specific application is treated as an engineering problem. Numerous successes. T. Pavlidis

3. Successes in Narrow AI(Seen in daily life) • Restricted Speech Recognition(in Banking and Airline reservation systems, etc) • Credit Card Fraud Detection • Web Tools (Shopping Suggestions, Mechanical Translation, etc) • Simple Robots (Roomba) • 1D and 2D Bar Codes (in stores and in shipping) T. Pavlidis

4. Successes in Narrow AI(Not Seen Everyday) • Chess Playing Machines • Optical Character Recognition • Industrial Inspection • Biometrics (Fingerprints, Iris, etc) • Medical Diagnosis T. Pavlidis

5. Restricted Speech Recognition • Grammar driven models (using low level context) have been quite successful. • High level context is even better. For example, matching a speech fragment to a name on a list. • Successful applications include Airline reservation systems and Call Center monitoring. • See a demonstration of using voice for web search in http://www.youtube.com/watch?v=npRtTdGeWQA . The system is a product of Nuance Open Voice Search and it relies on personalization. T. Pavlidis

6. Web Shopping:Learning User Preferences T. Pavlidis

7. Household Robot http://store.irobot.com/home/index.jsp T. Pavlidis

8. Making Reading Easy for Computers • Bar codes and two-dimensional symbologies are much easier to read than text because: • They are formally defined. • They include well-defined error detection or, in some cases, error correction codes thus providing their own context. T. Pavlidis

9. Examples of Two-Dimensional Symbologies Maxicode (UPS) PDF417 (Fed Ex, DMV) T. Pavlidis

10. Chess Playing Machines - 1 • Chess is a deterministic game, so a computer could derive a winning solution analytically. However the number of all possible positions is so large (10120) that using even the fastest available computer it will take billions of years to consider all possible moves. • Skilled players may look at 20 moves ahead by pruning, i.e. ignoring non-promising moves. T. Pavlidis

11. Chess Playing Machines - 2 • Around 1980 Ken Thompson developed a chess playing program called Belle based on a minicomputer with a hardware attachment used to generate moves very fast. • Belle defeated all other computer programs and became the world champion. • The use of special chess knowledge and special purpose hardware became the preferred approach since then. T. Pavlidis

12. Deep Blue(The IBM machine that beat the human world champion) • A major focus of the effort was the development of special purpose hardware. • An expert chess player (Murray Campbell ) contributed the evaluation functions of the moves generated by the hardware. • The project had as a consultant an international grandmaster (Joel Benjamin who had played Kasparov to a draw in 1994). T. Pavlidis

13. Optical Character Recognition (OCR) • Printed text characters have small shape variability and high contrast with the background. • Spelling checkers (or ZIP code directories in postal applications) introduce low level context. • Reading of the checks sent for payment to American Express relies heavily on context. • Payments are supposed to be in full and the amount due is known, so the number written on a check is analyzed to confirm whether it matches the amount due or not T. Pavlidis

14. An Aside: Why did OCR mature when the need for it was diminished? • The algorithms used in the products of the 1990s were known earlier but they were too complex to be implemented effectively with the digital technology of earlier times. • When computer hardware became cheap enough for good OCR, it also became cheap enough for PCs, the Internet, and direct bank transfers. • Keep this in mind in your business plans! T. Pavlidis

15. Features of Narrow AI • Each Problem is Solved Separately even though certain common mathematical tools may be used (statistics, graph theory, signal processing, etc). • Each Solution Relies Heavily on Specific Environment Constraints and performance (compared to that of humans) drops when these constraints are relaxed. T. Pavlidis

16. Why Not General AI? • Why “waste” time with all the special cases and not solve the general problem once for all? • Why not use a “brain model” to solve all these problems? • Are advances in general computer technology (hardware, systems) likely to help? Why not wait for them rather than solving problems piecemeal? T. Pavlidis

17. Humans may be machines, but they are very differentfrom computers T. Pavlidis

18. Some Experiments T. Pavlidis

19. Can you read these words? T. Pavlidis

20. Can you read these words? T. Pavlidis

21. Reading Demo - 1 T. Pavlidis

22. Reading Demo - 1 Tentative binding on the letter shapes (bottom up) is finalized once a word is recognized (top down). Word shape and meaning over-ride early cues. T. Pavlidis

23. Reading Demo -2 New York State lacks proper facilities for the mentally III. The New York Jets won Superbowl III. • Human readers may ignore entirely the shape of individual letters if they can infer the meaning through context. T. Pavlidis

24. Reading dot-matrix print and fine laser print From: T. Pavlidis ``Context Dependent Shape Perception,''in Aspects of Visual Form Processing, (C. Arcelli, L. P. Cordella, and G. Sanniti di Baja, eds.) World Scientific, 1994, pp. 440-454. T. Pavlidis

25. What Neuroscientist Say • “Perceptions emerge as a result of reverberations of signals between different levels of the sensory hierarchy, indeed across different senses”. The author then goes on to criticize the view that “sensory processing involves a one-way cascade of information (processing)” • Source: V.S. Ramachandran and S. Blakeslee Phantoms in the Brain, William Morrow and Company Inc., New York, 1998 (p. 56) T. Pavlidis

26. The Importance of Context • “Human intelligence almost always thrives on context while computers work on abstract numbers alone. … Independence from context is in fact a great strength of mathematics.” • Source: Arno Penzias Ideas and Information, Norton, 1989, p. 49. T. Pavlidis

27. The Big Difference Between Humans and Machines • Humans (and animals) use prior knowledge to deal with sensory input. The process involves a complex of bottom-up and top-down processes. • It is hard to develop algorithms for a barely understood process. • Certainly, we cannot match human behavior by a machine, unless the machine has prior knowledge of its environment. T. Pavlidis

28. The Big Obstacle to General AI • We have too little knowledge of how the brain works, especially how context is inferred and brought into play. • Adding more CPU power helps only if we understand the problem (as in the case of chess), so general advances in computing are not likely to help. T. Pavlidis

29. Brain Models maybe Counter-productive • Once we accept that humans and computers are fundamentally different machines we should not try to imitate the way humans solve a problem. • We should attack problems in their own right given the nature of digital computers. Chess playing machines are a prime example. T. Pavlidis

30. How to Choose a Problem to Work On • Problem should be well defined in an algorithmic sense and context should be available. • For an example relying heavily on context see: http://www.theopavlidis.com/technology/BoxDimensions/overview.htm • In processing the input, it should be clear what kind of information we need to extract. (Mathematical model of the physical world must exist.) • Do not be too concerned about limitations in present day computer power. T. Pavlidis

31. Acknowledgements • I want to thank Prof. Paul Pavlidis of the University of British Columbia for several constructive comments on an earlier draft of this presentation. • The link to the speech recognition system of Nuance was provided by Prof. Amanda Stent of Stony Brook University. T. Pavlidis