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_at_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.

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)

4
Successes in Narrow AI(Not Seen Everyday)

• Chess Playing Machines
• Optical Character Recognition
• Industrial Inspection
• Biometrics (Fingerprints, Iris, etc)
• Medical Diagnosis

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.

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?

7
Humans may be machines, but they are very
different from computers
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.

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.

10
Example
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).

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.)

13
Perceptual versus Computational Similarity
Perceptually close
Pixel-wise close
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.

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.

16
Human Intelligence made simple
Input
Concept
Input
Output
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.

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)

19
What Do You See?
20
Reading Demo - 1
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.
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.

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.

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.)

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.

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)?

27
A Note on Neural Nets
Is this a model of the brain?
As much as a table is a model of a dog.
28
Simplified model of a small part of the brain
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!

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 FW gt
  0 for squares and FW lt 0 for rectangles

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!!!

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.

33
Negator of Generality
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.)

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.

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.

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.

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!)

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!

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.

41
How these two faces differ?
42
How about these two?
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.

44
How About Deep Blue?

• In 1997 a chess machine (IBMs 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
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.

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.

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).

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.

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.

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.

51
The End

• Thats all folks