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Why Machine Intelligence is Very Hard. Theo Pavlidis Distinguished Professor Emeritus Dept. of Computer Science t.pavlidis@ieee.org http://theopavlidis.com. Limitations of Computers.

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Why Machine Intelligence is Very Hard


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    1. Why Machine Intelligence is Very Hard Theo Pavlidis Distinguished Professor Emeritus Dept. of Computer Science t.pavlidis@ieee.org http://theopavlidis.com

    2. Limitations of Computers • Some tasks (e.g. number factorization) are very hard for computers (unless it is proven that NP = P), but they are also very hard for humans. We do not discuss such tasks in this talk. • But there are also tasks that are quite easy for humans but very hard for computers such as language translation, image understanding, speech recognition, game playing, etc. All these go often under the name of Artificial Intelligence (AI). Comparatively little progress has been in most of them even though there is no theoretical reason for their difficulty. Machine Intelligence - web version

    3. The State of Machine Vision • There have seen some successes, notably in industrial inspection and reading of printed text but a lot of problems remain open. • Reading distorted text (CAPTCHA) is so hard that it is used as a security device. • Content Based Image Retrieval (CBIR) is hopelessly behind content based text retrieval. • Face recognition programs are known mainly for their failure to perform outside the laboratory. Machine Intelligence - web version

    4. CAPTCHA • CompletelyAutomatedPublicTuring test to tellComputers andHumansApart Machine Intelligence - web version

    5. 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.) • Many systems have been advertised but they do well only on rather trivial queries. • This should be contrasted with the success of text retrieval, not only Google but earlier programs such as the Unix grep. Machine Intelligence - web version

    6. Example - 1 Machine Intelligence - web version

    7. Example - 2 Machine Intelligence - web version

    8. Why the Failures? • More specifically why computers have so much trouble with pictures (compared, say, to alphanumeric data)? Machine Intelligence - web version

    9. Human Intelligence made simple Input Concept Input Output Machine Intelligence - web version

    10. 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. Machine Intelligence - web version

    11. 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) Machine Intelligence - web version

    12. Reading Demo - 1 Machine Intelligence - web version

    13. 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. Machine Intelligence - web version

    14. 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. Machine Intelligence - web version

    15. 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”). Machine Intelligence - web version

    16. 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.) Machine Intelligence - web version

    17. Perceptual versus Computational Similarity Perceptually close Pixel-wise close Machine Intelligence - web version

    18. 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. Machine Intelligence - web version

    19. We do not 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.” Machine Intelligence - web version

    20. 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. Machine Intelligence - web version

    21. Example of Using Context - 1 Machine Intelligence - web version

    22. Example of Using Context - 2 Machine Intelligence - web version

    23. Example of Using Context - 3 • A human needs to detect only part of the contour of an object to recognize the object. • It is wasteful to look for algorithms that will produce complete contours without also producing noise. • The key to image analysis is to be able to make decisions from incomplete data. Machine Intelligence - web version

    24. Example of Using Context - 4 Machine Intelligence - web version

    25. 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.) Machine Intelligence - web version

    26. 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. Machine Intelligence - web version

    27. 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)? Machine Intelligence - web version

    28. A Note on Neural Nets Is this a model of the brain? As much as a table is a model of a dog. Machine Intelligence - web version

    29. Projections • When we map pictures of, say, 1000 by 1000 pixels into an array of, say, 100 numbers we perform, in effect, a projection from a high dimensional space to one of lower dimensions. • If the number of samples is small we are likely to be able to find structure in the projection. Machine Intelligence - web version

    30. Statistical Games Machine Intelligence - web version

    31. Statistical Games Machine Intelligence - web version

    32. Dead End Approaches • “Training” on large numbers of samples has been used as a way out of this problem. • 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! Machine Intelligence - web version

    33. 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 Machine Intelligence - web version

    34. 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!!! Machine Intelligence - web version

    35. 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. Machine Intelligence - web version

    36. 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.) Machine Intelligence - web version

    37. 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. Machine Intelligence - web version

    38. 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. Machine Intelligence - web version

    39. Optical Character Recognition (OCR) • Printed text characters have small shape variability and high contrast with the background. (CAPTCHA systems negate these properties) • Spelling checkers (or ZIP code directories in postal applications) introduce low level context. Machine Intelligence - web version

    40. 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 and the Internet. • Keep this in mind in your business plans! Machine Intelligence - web version

    41. Face Recognition • It took over forty years to built acceptable quality machines that recognize written 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. Machine Intelligence - web version

    42. Can you tell how these two pictures differ? Machine Intelligence - web version

    43. How about these two? Machine Intelligence - web version

    44. How these two faces differ? Machine Intelligence - web version

    45. How about these two? Machine Intelligence - web version

    46. How these two differ? Machine Intelligence - web version

    47. How about these two? Machine Intelligence - web version

    48. 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. Machine Intelligence - web version

    49. Results from the Field • Not surprisingly, the results of installed face recognition systems have been dismal. An ACLU press release of May 14, 2002 stated that "interim results of a test of face-recognition surveillance technology … from Palm Beach International Airport confirm previous results showing that the technology is ineffective." • See also The Economist, October 26, 2002 Machine Intelligence - web version

    50. Face Detection • Before proceeding with face recognition we need to find the faces in a picture (face detection) • CMU has a web site where the public may submit pictures and they get back results with a green square overlaid on faces facing front and green pentagons of profiles. • Results are not robust. Machine Intelligence - web version