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Approaches to AI from Symbolic Systems to BacktoBasics Evolution

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The Chinese Gym. Searle believes that connectionism cannot bring anything ... Any meaning we confer to the symbols is parasitic on the meaning in our heads. ... – PowerPoint PPT presentation

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Title: Approaches to AI from Symbolic Systems to BacktoBasics Evolution


1
Approaches to AI from Symbolic Systems to
Back-to-Basics Evolution
  • Name Kim Thompson
  • Profession Transit Administration
  • Affiliation Washington Metropolitan Area
    Transit Authority
  • Relation I find AI fun to think about.
  • Question Can greater understanding of
    intelligence help us better relate to
    each other?

2
Local Representations
  • Symbolic representations are the lynch-pin of
    classical AI.
  • Units of information are shunted around and
    operated on by the model.
  • This type of representation is called local
    because information is kept together in a
    locatable package.

3
Distributed Representations
  • Neural Networks
  • A distributed representation is spread out across
    the whole network.
  • Neural networks are built up from atomic units
    artificial neurons but these units are rarely
    used to represent anything in themselves.

4
Complex Activity
  • Information is represented by a complex pattern
    of activity over a wide number of neurons.

5
Complementary Approaches
  • Connectionism is an approach inspired by the
    neural structure of human and animal brains.
  • Symbolic AI, an approach that relies on locatable
    symbols, was the accepted conceptual vocabulary
    of choice for a long time.
  • Historically, supporters of symbolic AI and
    connectionism were divided into rival camps.
    Today, most now agree that the two approaches
    complement each other.

6
Can Neural Networks Think?
  • Computers manipulate meaningless symbols.
    Computers do not understand the symbols.
  • Connectionism can offer some insights to this
    problem
  • 1) neural networks are physically different
  • 2) neural networks compute at a sub-symbolic
    level

1
2
7
The Chinese Gym
  • Searle believes that connectionism cannot bring
    anything new to the AI debate.
  • He describes a Chinese Gym filled with
    non-Chinese speakers each representing a neuron
    in a neural network.

8
The Symbol Grounding Problem
  • By themselves, symbols are meaningless shapes
    realized by in the case of a conventional
    computer a pattern of electrical activity.
  • Any meaning we confer to the symbols is parasitic
    on the meaning in our heads.

9
Breaking the Circle
  • Could you ever learn Chinese as a first language,
    with only the aid of a Chinese-Chinese
    dictionary?
  • How does one get out of a symbol-symbol
    merry-go-round?

10
Psychologist Stevan Harnard imagines
  • A classical symbolic system sitting on top of a
    sub-symbolic connectionist system.
  • The connectionist system has inputs that are
    grounded in the outside world through sensors.
  • In this way, symbolic representations are no
    longer defined in terms of other symbols, but are
    instead related to iconic representations with
    are directly linked to the sensory surfaces of
    the system.

11
The Demise of AI?
  • After a half century of research into AI
    expectations of building a machine that can match
    the cognitive abilities of humans are not being
    met.
  • the cognitivistic paradigms neglect of the
    fact that intelligent agents live in a real
    physical world leads to significant shortcomings
    in explaining intelligence.
  • --- Rolf Pfeifer and Christian Scheier

12
New AI
  • We use to argue whether a machine could think.
    The answer is No.
  • What thinks is a total circuit, including perhaps
    a computer, a man, and an environment.
  • Similarly, we may ask whether a brain can think,
    and again, the answer is No.
  • What thinks is a brain inside a man who is part
    of a system which includes an environment.
  • --- Gregory Bateson

13
The Problems of Conventional AI
  • Scalability

14
Another Problem ...
  • Robustness, or the inability of many systems to
    react well to unforeseen circumstances.

15
Final Problem
  • Operating in Real Time
  • The sense-model-plan-act cycle leads to massive
    amount of information processing requiring too
    much time.

16
The New Argument from Evolution
  • The problem of creating intelligent agents has
    already been solved over the course of 4.5
    billion years.
  • Biological evolution builds on existing designs
    by adding the occasional improvement.
  • MIT roboticist Rodney Brooks believes that our
    knowledge of evolution can inform AI
  • and argues that we should first aim to build
    basic mechanical creatures before we try to build
    mechanical humans.
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