Why Machine Intelligence is Very Hard

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Why Machine Intelligence is Very Hard

• Theo Pavlidis
• Distinguished Professor Emeritus
• Dept. of Computer Science
• t.pavlidis_at_ieee.org
• http//theopavlidis.com

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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?

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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.

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CAPTCHA

• CompletelyAutomatedPublicTuring test to
  tellComputers andHumansApart

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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.

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Example - 1
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Example - 2
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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).

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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.)

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Perceptual versus Computational Similarity
Perceptually close
Pixel-wise close
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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.

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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.

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Human Intelligence made simple
Input
Concept
Input
Output
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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.

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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)

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What Do You See?
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Reading Demo - 1
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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.
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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.

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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.

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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.)

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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.

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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)?

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A Note on Neural Nets
Is this a model of the brain?
As much as a table is a model of a dog.
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Simplified model of a small part of the brain
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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!

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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 FW
  0 for squares and FW

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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!!!

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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.

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Negator of Generality
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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.)

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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.

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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.

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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.

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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!)

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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
  and the Internet.
• Keep this in mind in your business plans!

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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.

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How these two faces differ?
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How about these two?
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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.

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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.

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Glimpses from the Face Detection Gallery - 1
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Glimpses from the Face Detection Gallery - 3
They got the wrong person
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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.

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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.

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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.

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The End

• Thats all folks