What is Artificial Intelligence Today? Zenon Pylyshyn, Rutgers Center for Cognitive Science http://ruccs.rutgers.edu/faculty/pylyshyn.html

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What is Artificial Intelligence Today?Zenon
Pylyshyn, Rutgers Center for Cognitive
Sciencehttp//ruccs.rutgers.edu/faculty/pylyshyn.
html
CSCSI/SCEIO 15th Annual Meeting, Calgary, May
27, 2002

• Is it an engineering profession, like EE?
• Is it a branch of Computer Science, like software
  engineering?
• Is it a nascent science, like Genomics?
• Is it a methodology, like Statistics?
• Is it the home of lost causes, like
  Parapsychology?

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What is Artificial Intelligence Today?Zenon
Pylyshyn, Rutgers Center for Cognitive
Sciencehttp//ruccs.rutgers.edu/faculty/pylyshyn.
html
CSCSI/SCEIO 15th Annual Meeting, Calgary, May
27, 2002

• Is it an engineering profession, like EE?
• Is it a branch of Computer Science, like software
  engineering?
• Is it a nascent science, like Genomics?
• Is it a methodology, like Statistics?
• Is it the home of lost causes, like
  Parapsychology?
• Short answer All of the above.

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Where is Artificial Intelligence Today?a highly
personal view
CSCSI/SCEIO 15th Annual Meeting, Calgary, May
27, 2002
Zenon Pylyshyn, Rutgers Center for Cognitive
Sciencehttp//ruccs.rutgers.edu/faculty/pylyshyn.
html
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Where has Artificial Intelligence been and where
is it going?a highly personal view
Outline of talk

1. A lighthearted look at what has A.I. been doing
   these past 45 years in order to survive.
2. A more serious look at what I believe we have
   learned from work in A.I. and Cognitive Science.
3. Some speculations on whether current trends
   provide any grounds for optimism for the future
   of A.I.

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Where has A.I. been these past 45 years?

• It has been battling the forces of darkness and
  has survived

In the meantime it has split into many partsand
has assumed many different disguises.
It has been 45 years since the 1957 Dartmouth
conference.
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A.I. Has Survived Many Threats

• It has survived
• The illusions of innocence
• The foolishness of futurism
• The perils of popularity
• The dazzle of demos
• The shameless pursuit of short-term goals
• The corruptions of capitalism

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It has survived the illusions of innocence

• The age of innocence Cybernetics, Weiner,
  Turing, Newell Simon and the promise of
  mechanized intelligence (Thats what drew me and
  many others to it)

Photographs courtesy of Lord of the Rings
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It has survived foolhardy futurism

• Its hard to predict, especially the future
  (Japanese saying)

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Examples of foolhardy futurism

• A computer will be chess champion and will prove
  a significant mathematical theorem within ten
  years (Simon, 1957)
• Within a generation the problem of creating
  artificial intelligence will be substantially
  solved (Minsky, 1967).
• I believe that robots with human intelligence
  will be common within 50 years (Hans Moravec,
  1988)
• by the year 2020, neural net computers will
  have doubled about 23 times ... resulting in a
  speed of about 20 million billion neural
  connection calculations per second, which is
  equal to the human brain. The emergence of
  machines that exceed human intelligence in all
  its broad diversity is inevitable. (Ray
  Kurzweil, 1999)
• "Guys like Kurzweil and Moravec ... somehow think
  that they can take Moore's law and project things
  into the future and say that, by 2020, you'll
  have human-level intelligence. I don't believe
  that at all. I think new ideas are required."
  (John McCarthy, 2001).

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It has survived the perils of popularity

• For many years (in the late 1970s and 1980s)
  A.I. was the sexiest topic in computer science
  and psychology (which may be why many of us are
  here!)

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And that leads to certain distractionsthe
perils of popularity
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It has survived the dazzle of demos

• The Gee Whiz School of Science
• Toy systems (GPS, Eliza, Shrdlu, Shaky,
  Planner/Strips)
• Performance systems (Dendral, XCON, Prospector,
  DART)

Lothlorien

• But how do you know when you should be impressed
  by a demo?

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• Its not easy to tellwhether you should be
  impressed by a demo.
• How impressive the demo is depends on what else
  you assume the system can do.
• How impressive the demo is may depend on what
  tools are available.

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It has survived the shameless pursuit of short
term goals

• Artificial intelligence used to mean robots that
  think like people now it means software for
  rejecting junk e-mail. Low expectations could
  yield better applications, sooner." Doug Lenat.
  MIT Tech Review, 2001

• So, whats wrong with that?

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• Short-term solutions rarely scale up.
• It is possible to attain spectacular short-term
  achievements without any fundamental new
  understanding of A.I. problems (e.g., Deep Blue).
• Short term criteria dont tell you when your
  assumptions are hopelessly wrong and you should
  start over.
• Short term problem-solving detracts from the
  bigger AI goal.

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A.I. has even survived the corruption of
capitalism

• Teknowledge, Tekmoney, CAIP, Cognicom, Gomi AI, ..

• What we tell venture capitalists vs. what we
  really believe

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Some of the commercial claims make one wonder who
our colleagues are working for!
Sometimes I wonder if we havet carried
ecumenism a bit too far
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AIs dilemma

• In the end, we in AI get high marks on the
  applications side, to which we have been pushed
  by sponsor pressure and seduced the lure of
  billions. And certainly, it is hard to quarrel
  with the good done. But from the perspective of
  answering big questions and realizing big dreams,
  the disappearance of AI into computer science
  retards progress by shifting the way AI people
  are judged and rewarded.
• Patrick Winston, keynote address to the American
  Association for Artificial Intelligence, July 20,
  1999

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Is it time to return to the original goal?Can we
keep our eye on the prize

• Is it time for looking back on what weve learned
  and for refocusing on the scientific puzzles

From Nils Nilssons 1995 AAAI paper title
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What have we learned in the past 45 years? Some
current criticisms of AI not my own views
Lessons I have learned from Cognitive Science

1. Representing and manipulating knowledge is not
   everything KR has been overemphasized in AI
2. Logical formalisms are hopeless for representing
   knowledge
3. Human cognition has a lot in common with that of
   other organisms, so AI should start by simulating
   the simple ones first (e.g., insects), and
   letting complex ones evolve
4. Intelligence is distributed among minds, bodies,
   and environments, and A.I. has not recognized
   these enough in its pursuit of KR.

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There is more to intelligence than processing
knowledge.

• No matter how little of the system deals with
  knowledge, it will always remain the core of
  intelligence because Intelligent behavior is
  essentially knowledge-dependent or cognitively
  penetrable. This follows from the following
  important facts about (human) cognition
• The equivalence classes of events under which
  intelligent agents behavior is predictable are
  semantically defined inputs with the same
  meaning have the same effect.
• Its how the world is represented, rather than
  how it actually is, that determines behaviour.
• There is no rule or principal of intelligent
  behavior that cannot be overturned by changing a
  persons goals and beliefs. In other words, some
  part of every intelligent behaviour is cognitive
  penetrable.

For details, see my Computation and Cognition,
MIT Press, 1984
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What about the Intelligence in the I/O?

• Its true that a lot of intelligence occurs at
  the I/O, where the processes are encapsulated in
  modules.
• Its also true that the most progress (in AI and
  CogSci) has been made in understanding the
  modular systems of cognition, such as language,
  vision, and motor control.
• Exploring I/O systems and new computational
  architectures is important, but the basic engines
  of intelligence are semantically-driven processes
  like inference. The intentionality of the mental
  is what distinguishes human-level intelligence
  from merely clever or impressive behavior.
• For more on this, see Pylyshyn, Z. (1984).
  Computation and Cognition. MIT Press.

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(2) Logical formalisms are hopelessly inadequate
for representing knowledge

• As Winston Churchill said about democracy It is
  the worst form of government, except for all the
  rest.
• So also logic is the worst form of knowledge
  representation, except for all the rest.

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• Much has been said about why logic is
  inadequate e.g., it has trouble representing
  knowledge that is procedural, quantitative,
  pictorial (or sensory), higher-order (meta
  knowledge) and indexical although excellent
  work is being done on all of these problems. A
  reasonable assumption is that there may have to
  be more than one form of representation.
  Unfortunately, none of the non-symbolic ones
  proposed so far (e.g., neural nets, images) are
  adequate for reasoning.
• Logic or Logical Calculus is just another name
  for symbol system except that logic usually
  provides a formal semantics (i.e., a theory of
  how the meaning of expressions is composed from
  the meanings of its parts) and a system of
  inference (at least for deductive inference, if
  not for inductive, abductive and practical
  reasoning). Nothing like that is available for
  any of the other forms of representation.

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So logic is a special form of symbol system But
why do we need symbol systems at all?

• What makes it possible to represent and process
  knowledge in a physical system, and to connect
  this knowledge with both perception and action,
  is what Newell Simon called a Physical Symbol
  System. This is perhaps the single most important
  idea of the 20th century because it made possible
  computing.

• But in order to be adequate for encoding
  human-level knowledge, physical symbol systems
  must meet some stringent conditions on format.

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ASIDE on symbol systems

• To be adequate for encoding human-level
  knowledge, physical symbol systems must meet some
  stringent conditions on format.

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Conditions on the format of representations

1. The format must have the capacity to distinguish
   an unlimited number of distinct representations.
   This is called the criterion of productivity
   which von Humboldt characterized as the Infinite
   use of finite means. This means it must be
   combinatorial.
2. The capacity for representation and inference
   must be systematic In intelligent agents the
   capacity to represent or infer a certain
   situation is always accompanied by the capacity
   to represent or infer other related situations.
   This means it must be compositional.

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Conditions on the format of representations 2

• The capacity for representation and inference is
  systematic In intelligent agents the capacity to
  represent or infer a certain situation is always
  accompanied by the capacity to represent or infer
  other related situations.
• If an intelligent system can represent some
  situations, say Q51 and Q27, then it will also
  have the capacity to represent some other
  situations related to Q51 and Q27.
• If an intelligent system can infer, say, Q36 from
  Q75 then it will also have the capacity to infer
  other things related to Q36 and Q75.
• Systematicity follows naturally from the use of
  an encoding system that has constituents (or
  parts) and builds up complexes by recursively
  embedding parts in wholes, as is done with all
  numerals, algebraic expressions, language or
  sentences in a logical calculus.

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Conditions on the format of representations 2a

• If you encode representations in terms of
  expressions that have a constituent structure,
  then systematicity holds in virtue of the format
  alone.
• E.g., if you can represent John gave the book to
  Mary and Mary gave the pen to Fred then you
  already have the capacity to represent many other
  situations e.g., John gave the pen to Mary,
  Mary gave the book to John, Fred gave the pen
  to Fred, and all other combinations.
• E.g., if you can infer from It is cloudy and
  rainy and cold that it is cold then you must
  also be able to infer from It is rainy and cold
  that it is cold. Such a principle (called
  conjunction elimination) is an inference schema
  that can be applied to representations that have
  constituents (or parts), and once the rule is
  part of the system it will apply to arbitrarily
  many other representations, resulting in
  inferential systematicity.

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Conditions on the format of an adequate system of
representation

• The conditions of productivity and systematicity
  entail the compositionality of representations
  complex representations are built out of simpler
  representations by rules of composition.
• Any system of representation meeting these
  constraints is a logical calculus or language of
  thought (lingua mentis).
• The invention of systems of logic that can encode
  wide range of beliefs are among the greatest
  achievements of the 20th century.
• For a more detailed argument see
• Fodor, J. A., Pylyshyn, Z. W. (1988).
  Connectionism and cognitive architecture A
  critical analysis. Cognition, 28, 3-71.

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Not every smart system is a physical symbol
system

• There are many very complex and intriguing
  systems in the mind/body that are not PSSs, and
  intelligent action depends on them as well. Much
  of early vision, motor control and even
  visual-motor coordination is like that. But no
  matter how much complexity is built into such
  systems they will sooner or later run up against
  the inference wall they will have to interface
  seamlessly with knowledge-based processes in a
  way that does not require some hidden
  intelligence in the interface (e.g., without a
  homunculus).
• Thats why layered intelligences (e.g., Brooks)
  will never scale up. Intelligent systems are
  not layers of architecture all the way up.
  Sooner or later the process depends on
  representations rather than on fixed structures.

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End of ASIDE and summary

• The conditions of productivity and systematicity
  entail the compositionality of representations
  complex representations are built out of simpler
  representations by rules of composition.
• Any system of representation meeting the above
  constraints is a logical calculus or language of
  thought (lingua mentis).
• For a more detailed argument see
• Fodor, J. A., Pylyshyn, Z. W. (1988).
  Connectionism and cognitive architecture A
  critical analysis. Cognition, 28, 3-71.

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(3) It has been suggested that since human
intelligence is continuous with animal
intelligence, we should study lower organisms
(e.g., insects) in which cognition is simpler,
and then move up to human intelligence later.

• Comparative psychology does indeed show that many
  aspects of vision, visual-motor coordination, as
  well as ontological categories and conceptual
  systems (e.g., concepts such as physical object,
  animate, cause, conspecific) and even parts of
  arithmetic are shared by most organisms,
  including human infants.
• But the research strategy of working up from
  lower organisms will not work in general. Even
  though we may share a lot with lower organisms,
  what we do not share is critical it is
  constitutive of intelligence.

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• Although many human cognitive capacities are
  found in animals, many other capacities appear
  suddenly as we go up the philogenetic scale
  (evolution and punctate equilibrium Steven
  Jay Gould).
• All humans, in contrast with members of other
  species, possess the capacity for language, the
  instinctive attribution of beliefs and desires to
  others, the capacity for counterfactual
  reasoning, and the need to provide a theoretical
  explanation for events around them (i.e., to
  practice inductive and abductive reasoning).
• Human vision, language, and action are at the
  service of goals and beliefs in a way that makes
  human behavior, unlike other animal behavior,
  largely stimulus-free
• Because all intelligent behaviour is cognitive
  penetrable by goals and beliefs.

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(4) AI has traditionally downplayed the role of
the environment in intelligent action But
intelligence must be situated and embodied.

• The first recognition of the importance of the
  environment-agent relation in shaping behavior
  may be in Simons ant example, but it now makes
  an appearance in many areas of contemporary AI.
• There is considerable truth in the observation
  that intelligence is situated and embodied, but
  the deep intellectual puzzles still concern how
  the organism represents the world, because (as
  discussed earlier)
• Its how the world is represented, rather than
  how it actually is, that determines intelligent
  behavior.

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But also recent A.I. trends have been in the
direction of taking the environment into account.
Recent examples that recognize the importance of
taking the agents environment into account
include

• Emphases on reactive planning, where plans allow
  for unexpected sensory input,
• Emphasis on active vision, in which vision
  interrogates the environment for relevant clues,
• Allowing for nonsymbolic aspects of reasoning
  through closer links with perception (e.g.,
  visual inference Jon Barwise),
• The use of indexicals in representations.(See
  Lespérance Levesque, 1995 Pylyshyn, 2000,
  2001)

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Forms of representation for a robot using
indexicals
From Pylyshyn, Z. W. (2000). Situating vision in
the world. Trends in Cognitive Sciences, 4(5),
197-207.
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Finally, after all this, are there grounds for
optimism about the future of Artificial
Intelligence?

• The simple answer is Yes, of course, otherwise
  we would not be here!
• But what are some promising trends that offset
  the enormous problems still to be solved?

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Some grounds for optimism

1. There are a very many accomplishments for which
   AI can take credit, despite the AI winter we
   have come through. The military, the airlines,
   traditional Operations Research domains, robotics
   and the games world have all seen major AI
   achievements (see Nilsson, 1995). There are also
   many small accomplishments, like Google, seen
   daily on the web and in nearly every walk of
   life, that have widespread effects. Many have
   not used the term artificial intelligence but
   they nonetheless owe their technology to AI which
   has infiltrated much of conventional CS.
2. Unlike the early days of AI, many more approaches
   are being entertained. While most of the grand
   polemical claims made by their advocates are
   almost certainly wrong, these approaches are
   leading to different lines of inquiry, which is
   healthy. The diversity is also producing
   powerful niche results in areas within
   computational vision, robotics, computational
   linguistics, and speech understanding.

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There is strength in diversity(also distractions)
Neats
Scruffies
Brooks
Minsky
Newell
Logic
Schank
McCarthy
Analogues
Fuzzy sets
Planning
Robots
Neural nets
Expert ystems
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Some grounds for optimism

• 3. One of the consequences of de-emphasizing pure
  reasoning has been more progress in areas where
  human cognitive skill is modular (and may not
  involve any reasoning).
• Vision (especially early vision).
• Computational Linguistics (a large part of
  grammatical analysis is modular),
• Speech recognition (much of phonetic analysis is
  modular),
• Visual-motor coordination (much of that is
  modular in humans and animals). Lends itself to
  Rod Brooks approach.
• Hybrid systems, in which the developments of
  modular smart subsystems (especially vision and
  control) are combined with knowledge-based
  systems (e.g., UBCs hybrid controllers and U of
  Ts cognitive robotics).

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Some grounds for optimism

• (4) Finally, there has been a large (and
  unexpected) resurgence of interest in the very
  broad questions of the nature of intelligence,
  and it relation to consciousness, to biology, to
  evolution and to technology. Books and articles
  by Kurzweil, Moravec, Joy, Wolfram, Dennett and
  the critical writings of Searle, Fodor, Lanier
  and others have once again returned the bigger
  questions of human and machine intelligence to
  centre stage.
• While these may get people thinking about where
  we stand in history, they probably only move AI
  ahead by focusing public debate and perhaps
  awakening funding agencies. But some problems
  are just not ready for scientific scrutiny they
  are the mysteries as opposed to the puzzles. The
  trouble with mysteries, such as the question What
  is consciousness? and What are the limits of AI?
  is that they are inherently ill-posed they are
  not stated in a way that could connect to a
  recognizable answer.

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Yet despite the possibility that this new
interest will let in misguided views and give us
bad press, a multitude of approaches have to be
tolerated because nobody knows where the key
discoveries will come from. So we shouldnt put
them down, even when they are wrong (and even
when they have larger grants than we do)!
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.because then we could all lose ..
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References cited

• Pylyshyn, Z. W. (2001). Visual indexes,
  preconceptual objects, and situated vision.
  Cognition, 80(1/2), 127-158.
• Pylyshyn, Z. W. (1999). Is vision continuous with
  cognition? The case for cognitive impenetrability
  of visual perception. Behavioral and Brain
  Sciences, 22(3), 341-423.
• Reiter, R. (2001). Knowledge in Action Logical
  Foundations for Specifying and Implementing
  Dynamical Systems. Cambridge, MA MIT Press.
• Winston, P. H. (1999, July, 1999). Why I am
  optimistic. Paper presented at the AAAI Annual
  Conference.

• Brooks, R. A. (1991). Intelligence without
  representation. Artificial Intelligence, 47,
  139-159.
• Fodor, J. A., Pylyshyn, Z. W. (1988).
  Connectionism and cognitive architecture A
  critical analysis. Cognition, 28, 3-71.
• Lespérance, Y., Levesque, H. J. (1995).
  Indexical knowledge and robot action - a logical
  account. Artificial Intelligence, 73, 69-115.
• Levesque, H. J., Lakemeyer, G. (2001). The
  Logic of Knowledge Bases. Cambridge, MA MIT
  Press.
• Nilsson, N. J. (1995). Eye on the prize. AI
  Magazine (summer), 9-17.
• Pylyshyn, Z. W. (2000). Situating vision in the
  world. Trends in Cognitive Sciences, 4(5),
  197-207.