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Transcript of Why Machine Intelligence is Very Hard Theo Pavlidis Distinguished Professor Emeritus Dept. of...
Why Machine Intelligence is Very Hard
Theo PavlidisDistinguished Professor Emeritus
Dept. of Computer Science
http://theopavlidis.com
2/29/2008 Machine Intelligence - CS talk 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.
• Some tasks that are quite easy for humans but very hard for computers.
• Examples: language translation, image analysis or understanding, speech recognition, game playing, etc. (Often grouped under Artificial Intelligence AI).
• Why are they hard?
2/29/2008 Machine Intelligence - CS talk 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.
2/29/2008 Machine Intelligence - CS talk 4
CAPTCHA
• CompletelyAutomatedPublicTuring test to tellComputers andHumansApart
2/29/2008 Machine Intelligence - CS talk 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.
2/29/2008 Machine Intelligence - CS talk 6
Example - 1
2/29/2008 Machine Intelligence - CS talk 7
Example - 2
2/29/2008 Machine Intelligence - CS talk 8
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”).
2/29/2008 Machine Intelligence - CS talk 9
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.)
2/29/2008 Machine Intelligence - CS talk 10
Perceptual versus Computational Similarity
Perceptually close Pixel-wise close
2/29/2008 Machine Intelligence - CS talk 11
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.
2/29/2008 Machine Intelligence - CS talk 12
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.”
2/29/2008 Machine Intelligence - CS talk 13
Human Intelligence made simple
Input
Output
InputConcept
2/29/2008 Machine Intelligence - CS talk 14
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.
2/29/2008 Machine Intelligence - CS talk 15
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)
2/29/2008 Machine Intelligence - CS talk 16
What Do You See?
2/29/2008 Machine Intelligence - CS talk 17
Reading Demo - 1
2/29/2008 Machine Intelligence - CS talk 18
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.
2/29/2008 Machine Intelligence - CS talk 19
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.
2/29/2008 Machine Intelligence - CS talk 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.
2/29/2008 Machine Intelligence - CS talk 21
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.)
2/29/2008 Machine Intelligence - CS talk 22
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.
2/29/2008 Machine Intelligence - CS talk 23
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)?
2/29/2008 Machine Intelligence - CS talk 24
A Note on Neural Nets
Is this a model of the brain?
As much as a table is a model of a dog.
2/29/2008 Machine Intelligence - CS talk 25
Simplified model of a small part of the brain
2/29/2008 Machine Intelligence - CS talk 26
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!
2/29/2008 Machine Intelligence - CS talk 27
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 andF’W < 0 for rectangles
2/29/2008 Machine Intelligence - CS talk 28
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!!!
2/29/2008 Machine Intelligence - CS talk 29
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.
2/29/2008 Machine Intelligence - CS talk 30
Negator of Generality
2/29/2008 Machine Intelligence - CS talk 31
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.)
2/29/2008 Machine Intelligence - CS talk 32
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.
2/29/2008 Machine Intelligence - CS talk 33
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.
2/29/2008 Machine Intelligence - CS talk 34
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.
2/29/2008 Machine Intelligence - CS talk 35
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!)
2/29/2008 Machine Intelligence - CS talk 36
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!
2/29/2008 Machine Intelligence - CS talk 37
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.
2/29/2008 Machine Intelligence - CS talk 38
How these two faces differ?
2/29/2008 Machine Intelligence - CS talk 39
How about these two?
2/29/2008 Machine Intelligence - CS talk 40
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.
2/29/2008 Machine Intelligence - CS talk 41
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.
2/29/2008 Machine Intelligence - CS talk 42
Glimpses from the Face Detection Gallery - 1
2/29/2008 Machine Intelligence - CS talk 43
Glimpses from the Face Detection Gallery - 3
They got the wrong person
2/29/2008 Machine Intelligence - CS talk 44
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.
2/29/2008 Machine Intelligence - CS talk 45
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.
2/29/2008 Machine Intelligence - CS talk 46
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.
2/29/2008 Machine Intelligence - CS talk 47
The End
That’s all folks