Why General Artificial Intelligence (AI) is so Hard

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. 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. Why General AI is so Hard - CS talk

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 house cleaner) • 1D and 2D Bar Codes (in stores and in shipping) Why General AI is so Hard - CS talk

4. Successes in Narrow AI(Not Seen Everyday) • Chess Playing Machines • Optical Character Recognition • Industrial Inspection • Biometrics (Fingerprints, Iris, etc) • Detection of Forest Fires • Etc, etc Why General AI is so Hard - CS talk

5. 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. Why General AI is so Hard - CS talk

6. 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? Why General AI is so Hard - CS talk

7. Humans may be machines, but they are very differentfrom computers Why General AI is so Hard - CS talk

8. Understanding the Difference betweenHumans and Computers • We will start by looking at the problem of content-based image retrieval to obtain an understanding of the difference. Why General AI is so Hard - CS talk

9. 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. Why General AI is so Hard - CS talk

10. Example Why General AI is so Hard - CS talk

11. 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”). Why General AI is so Hard - CS talk

12. 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.) Why General AI is so Hard - CS talk

13. Perceptual versus Computational Similarity Perceptually close Pixel-wise close Why General AI is so Hard - CS talk

14. 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. Why General AI is so Hard - CS talk

15. 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.” Why General AI is so Hard - CS talk

16. Human Intelligence made simple Input Concept Input Output Why General AI is so Hard - CS talk

17. 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. Why General AI is so Hard - CS talk

18. 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) Why General AI is so Hard - CS talk

19. What Do You See? Why General AI is so Hard - CS talk

20. Reading Demo - 1 Why General AI is so Hard - CS talk

21. 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. Why General AI is so Hard - CS talk

22. 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. Why General AI is so Hard - CS talk

23. 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. Why General AI is so Hard - CS talk

24. 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.) Why General AI is so Hard - CS talk

25. 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. Why General AI is so Hard - CS talk

26. 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 16th 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)? Why General AI is so Hard - CS talk

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

28. Simplified model of a small part of the brain Why General AI is so Hard - CS talk

29. 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! Why General AI is so Hard - CS talk

30. Distinguish Rectangles from SquaresThe 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 Why General AI is so Hard - CS talk

31. Distinguish Rectangles from SquaresThe 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!!! Why General AI is so Hard - CS talk

32. 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. Why General AI is so Hard - CS talk

33. Negator of Generality Why General AI is so Hard - CS talk

34. 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.) Why General AI is so Hard - CS talk

35. 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. Why General AI is so Hard - CS talk

36. 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. Why General AI is so Hard - CS talk

37. 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. Why General AI is so Hard - CS talk

38. 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!) Why General AI is so Hard - CS talk

39. 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! Why General AI is so Hard - CS talk

40. 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. Why General AI is so Hard - CS talk

41. How these two faces differ? Why General AI is so Hard - CS talk

42. How about these two? Why General AI is so Hard - CS talk

43. 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. Why General AI is so Hard - CS talk

44. 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. Why General AI is so Hard - CS talk

45. 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 (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. Why General AI is so Hard - CS talk

46. 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. Why General AI is so Hard - CS talk

47. 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). Why General AI is so Hard - CS talk

48. 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. Why General AI is so Hard - CS talk

49. 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. Why General AI is so Hard - CS talk

50. 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. Why General AI is so Hard - CS talk