1. Why General Artificial Intelligence (AI) is so Hard Theo Pavlidis Distinguished Professor Emeritus Dept. of Computer Science t.pavlidis@ieee.org http://theopavlidis.com

2. 10/9/2009Why General AI is so Hard - CS talk2 Definitions of Artificial Intelligence (AI) General or Strong AI: A machine that replicates the functionality of the human brain. “Around the Corner” since about 1945. Narrow or Weak AI: A machine that does a specific task that traditionally has been done by humans. Each specific application is treated as a separate engineering problem. Numerous successes.

3. 10/9/2009Why General AI is so Hard - CS talk3 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 house cleaner) 1D and 2D Bar Codes (in stores and in shipping)

4. 10/9/2009Why General AI is so Hard - CS talk4 Successes in Narrow AI (Not Seen Everyday) Chess Playing Machines Optical Character Recognition Industrial Inspection Biometrics (Fingerprints, Iris, etc) Medical Diagnosis

5. 10/9/2009Why General AI is so Hard - CS talk5 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.

6. 10/9/2009Why General AI is so Hard - CS talk6 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?

7. 10/9/2009Why General AI is so Hard - CS talk7 Humans may be machines, but they are very different from computers

8. 10/9/2009Why General AI is so Hard - CS talk8 Understanding the Difference between Humans and Computers We will start by looking at the problem of content-based image retrieval to obtain an understanding of the difference.

9. 10/9/2009Why General AI is so Hard - CS talk9 Content-based Image Retrieval (CBIR) Given an image find those that are similar to it from a data base of images. (If the images are labeled, the problem is reduced to text search.) Systems do not perform as advertised. For a collection of critical writings see –http://www.theopavlidis.com/technology/CBIR/index.htm The difficulty of image retrieval should be contrasted with the success of text retrieval, not only Google, but also earlier programs such as the Unix grep.

10. 10/9/2009Why General AI is so Hard - CS talk10 Example

11. 10/9/2009Why General AI is so Hard - CS talk11 Reasons for the Poor Results in Machine Vision and CBIR Images are represented by statistics of pixel values (e.g. color histogram, texture histogram, etc) Such statistics are unrelated to human perception. Papers describing CBIR methods use trivial queries (e.g. “show me all pictures with a lot of green”).

12. 10/9/2009Why General AI is so Hard - CS talk12 Perceptual versus Computational Similarity Two pictures may differ a lot in their pixel values but appear similar to a person. (“They have the same meaning”.) Two pictures may differ in very few pixels but they have different meaning. (Face portraits of two different people in front of the same background.)

13. 10/9/2009Why General AI is so Hard - CS talk13 Perceptual versus Computational Similarity Perceptually closePixel-wise close

14. 10/9/2009Why General AI is so Hard - CS talk14 Text versus Pictures In text files each byte (or two) is a numerical code for a character. Therefore strings of bytes correspond to words that carry semantic meaning. In pictures each byte (or group thereof) represents the color at a particular location (pixel). Pixels are quite far from the components that have a semantic meaning.

15. 10/9/2009Why General AI is so Hard - CS talk15 We do not do that well in text! If it is hard to search for concepts unless we can map concepts into words. Example 1: Find all articles critical of the government policy in dealing with the banking crisis. Example 2: Find all articles about a dog named Lucy. Amongst the Google returns was an article with the phrase: “ Lucy and I spent the weekend alone together. We have a dog named Kyler.”

16. 10/9/2009Why General AI is so Hard - CS talk16 Human Intelligence made simple Input Output Input Concept

17. 10/9/2009Why General AI is so Hard - CS talk17 The Big Difference The transformation of input to concept is a complex process (binding), barely understood by neuroscientists. (In spite of claims to the opposite by some computer scientists.) It is hard to develop algorithms for a barely understood process. Humans can transform concepts into formal entities (words in a language) and then code them in computer readable form. Computers can deal with such formal input.

18. 10/9/2009Why General AI is so Hard - CS talk18 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)

19. 10/9/2009Why General AI is so Hard - CS talk19 What Do You See?

20. 10/9/2009Why General AI is so Hard - CS talk20 Reading Demo - 1

21. 10/9/2009Why General AI is so Hard - CS talk21 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.

22. 10/9/2009Why General AI is so Hard - CS talk22 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.

23. 10/9/2009Why General AI is so Hard - CS talk23 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.

24. 10/9/2009Why General AI is so Hard - CS talk24 The Challenges We need to replicate complex transformations that the (human/animal) brain has evolved to do over millions of years. We have to deal with the fact the processing is not unidirectional and also affected by other factors than the input (context). (Such factors cause visual illusions.)

25. 10/9/2009Why General AI is so Hard - CS talk25 A time scale The human visual system has evolved from animal visual systems over a period of more than 100 million years. Speech is barely over 100 thousand years old. Written text is no more than 10 thousand years old.

26. 10/9/2009Why General AI is so Hard - CS talk26 A note on brain models There is a history for considering the latest technology to be a model of the human brain, for example in the 16 th century irrigations networks were considered to be models of the brain. If someone claims to have a machine modeling the human brain, ask how could the machine be modified to model the brain of a dog (since a dog cannot learn to write poetry, play chess, etc)?

27. 10/9/2009Why General AI is so Hard - CS talk27 A Note on Neural Nets Is this a model of the brain? As much as a table is a model of a dog.

28. 10/9/2009Why General AI is so Hard - CS talk28 Simplified model of a small part of the brain

29. 10/9/2009Why General AI is so Hard - CS talk29 A Dubious Approach “Training” on large numbers of samples has been used as a way out of finding a way to understand what is going on. But humans (and animals) do not need to be trained on large numbers of samples. Rats trained to distinguish between a square and a rectangle perform quite well when faced with skinnier rectangles. They have the concept of rectangle!

30. 10/9/2009Why General AI is so Hard - CS talk30 Distinguish Rectangles from Squares The Artificially Intelligent Approach Take a hundred (or more) pictures of rectangles and squares, compute several statistics on each picture and for each picture create a “feature” vector F. Then compute a vector W so that F’W > 0 for squares and F’W < 0 for rectangles

31. 10/9/2009Why General AI is so Hard - CS talk31 Distinguish Rectangles from Squares The Natural Approach Find the outline of a shape (if one exists in a picture) and fit a rectangle to it. Then compute the aspect ratio of the rectangle. If it is near 1 (for some given tolerance), then it is called a square, otherwise a rectangle. Criticism: Method lacks generality!!!

32. 10/9/2009Why General AI is so Hard - CS talk32 No Generality in Nature The animal visual systems has many special areas for visual tasks (about 30 in the human case). We have already seen examples where “high level” (context) recognition takes quickly over the low level data processing.

33. 10/9/2009Why General AI is so Hard - CS talk33 Negator of Generality

34. 10/9/2009Why General AI is so Hard - CS talk34 The Learning Machine (neural net) Approach It has the appeal of getting something for nothing, so it is kept alive. We can “solve” a problem without really understanding it. Give a learning machine “enough” samples and a classifier will be found!!! (Forget about the rat who only needs two samples.)

35. 10/9/2009Why General AI is so Hard - CS talk35 Criteria for Choosing a Problem to Work on Context should either be known or not important. Processing of the input should be relatively simple (it should be clear what kind of information we need to extract). For an example relying heavily on context see: technology/BoxDimensions/overview.htm on my web site. Comments on major areas in the next few slides.

36. 10/9/2009Why General AI is so Hard - CS talk36 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.

37. 10/9/2009Why General AI is so Hard - CS talk37 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.

38. 10/9/2009Why General AI is so Hard - CS talk38 An example of heavy use of context Reading of the checks sent for payment to American Express. Because payments are supposed to be in full and the amount due is known, the number written on a check is analyzed to confirm whether it matches the amount due or not. (But direct payment is used more and more!)

39. 10/9/2009Why General AI is so Hard - CS talk39 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 direct text entry through PCs and the Internet. Keep this in mind in your business plans!

40. 10/9/2009Why General AI is so Hard - CS talk40 Face Recognition It took over thirty years to built acceptable quality machines that recognize printed symbols. What makes us think that we can solve the much more complex problem of distinguishing human faces? Neuroscientists point out that humans have special neural circuitry for face recognition.

41. 10/9/2009Why General AI is so Hard - CS talk41 How these two faces differ?

42. 10/9/2009Why General AI is so Hard - CS talk42 How about these two?

43. 10/9/2009Why General AI is so Hard - CS talk43 Face Recognition and Scalability The population samples in published studies are relatively small and include men and women of different races with different hairstyles, etc. I have never seen a study where all the subjects are similar. For example, white blond men between the ages of 20 and 30 with long hair and beards. Subjects in published studies are cooperative.

44. 10/9/2009Why General AI is so Hard - CS talk44 How About Deep Blue? In 1997 a chess machine (IBM’s “Deep Blue”) beat the human world champion Garry Kasparov. This resulted in a lot of publicity on how computers had become smarter than humans.

45. 10/9/2009Why General AI is so Hard - CS talk45 However Chess is a deterministic game, so a computer could derive a winning solution analytically. On the other hand 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.

46. 10/9/2009Why General AI is so Hard - CS talk46 Chess Playing Machines 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.

47. 10/9/2009Why General AI is so Hard - CS talk47 More on Deep Blue 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).

48. 10/9/2009Why General AI is so Hard - CS talk48 Concluding Remarks Before we try to built a machine to achieve a goal we must ask ourselves whether that goal is compatible with the laws of nature. (Not because “people can do it”.) While such laws are clear in Physics and Chemistry, there are not in the field of Computation except in some extreme cases.

49. 10/9/2009Why General AI is so Hard - CS talk49 Human Credulity - 1 In spite of well understood laws of physics “inventors” persist in offering designs that violate them and they find takers. Therefore fundamental advances in Computer Science are likely to reduce but not to eliminate preposterous claims.

50. 10/9/2009Why General AI is so Hard - CS talk50 Human Credulity - 2 50 years ago Langmuir (in “Pathological Science”) debunked UFOs but also predicted that UFOs will be with us for a long time because it is too good a story for the news media to let go. The view of computers as giant brains that are able to out-think and replace humans is about as valid as visits by extraterrestrials, but it makes too good a story for the news media to let go.

51. 10/9/2009Why General AI is so Hard - CS talk51 The End That’s all folks