Theo Pavlidis
Distinguished Professor Emeritus
Stony Brook University t.pavlidis@ieee.org
http://theopavlidis.com

• 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.
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(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 )
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(Not Seen Everyday)
• Chess Playing Machines
• Optical Character Recognition
• Industrial Inspection
• Biometrics
(Fingerprints, Iris, etc)
• Medical Diagnosis
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• 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.
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Sept. 2008 http://store.irobot.com/home/index.jsp
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• 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.
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Examples of Two-Dimensional Symbologies
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Maxicode (UPS)
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PDF417 (Fed Ex, DMV)
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• Chess is a deterministic game, so a computer could derive a winning solution analytically.
However the number of all possible positions is so large (10 120 ) 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.
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• 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.
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(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).
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• 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
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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!
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• 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.
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• 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?
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Humans may be machines, but they are very different from computers
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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.
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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.
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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.
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• “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)
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• “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.
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• Humans (and animals) use prior knowledge to deal with sensory input. The process involves a complex of bottom-up and topdown 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.
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• 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.
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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.
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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.
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• 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.
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