The Two (Computational) Faces of AI
David Davenport
Computer Engineering Dept.,
Bilkent University
Ankara 06800 – TURKEY
PT-AI talk
Thessaloniki
Oct. 2011
Email: david@bilkent.edu.tr
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Explaining Cognition / AI
• Scientific endeavor
& Engineering discipline
“...an engineering discipline built
on an unfinished science”
Matt Ginsberg, 1995
• Philosophers only
complicated matters
▫ confusing us about words
we thought we understood.
• This is my naïve attempt to understand…
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In the beginning…
• was the classical “symbolic” paradigm
▫ cognition seen as computation
▫ logical, rule-governed
manipulation of formal symbols…
▫ but meaning & biological plausibility?
• enter the “connectionist” paradigm
▫ brain inspired, flexible, subsymbolic,
able to learn its own “symbols”, but opaque
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The Architecture of Cognition
• Is it symbolic & connectionist?
▫ Is one wrong?
Are they genuine alternatives?
Or a hybrid of both?
Or neither…
• Newer contenders:
▫ Dynamical systems, embodied, embedded,
radical embedded, situated, extended,
interactivist, enactivist, …
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Engineering Cognition / AI
• Requirements: concerned with function,
what is the problem that needs solving?
• Design: an abstract solution to problem.
• Implementation: concrete, physical
mechanism corresponding to the design.
• Test, distribution, maintenance
▫ handled by environment & evolution!
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Functional Requirements
• Agents are small part of physical world
▫ so have limited knowledge & subject to error
• World has some regularities
“The unpredictability of the world makes intelligence
necessary, the predictability makes it possible.”
• Agents make use of regularities
▫ detect, predict & select “best” action.
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Use Cases
• Example task types
1) Maintain body temperature, control engine
speed, flower facing sun…
2) Track predator/prey even when occluded,
walk/climb towards goal despite obstacles...
3) Converse in English, do math, tell fictional
stories, socialise, …
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Different mechanisms…
• Example 1 type systems
▫ require only simple feedback control
• Example 3 type systems
▫ require… a full symbol system?
“A physical symbol system has the necessary and sufficient
means for [human-level] intelligent action.”
Newell & Simon, 1976
• Note: PSS could do all types, but type 1 systems
couldn’t ~~ c.f. newcomers?
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Design
• Constrained by functional requirements
& by properties of available materials
• Claim designs will be computational
• Take a broad view of computation
“computation as prediction/modeling”
why?
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Prediction / Modeling
• Target system & model
• Map states & seq.
▫ Find existing system
▫ Construct one anew
▫ Use digital computer
• Rely on causation
• Causal structure is all that matters
Photo by Flickr user charamelody
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Design (cont.)
• Constrained by functional requirements
& by properties of available materials
• Claim designs will be computational
• Take a broad view of computation
“computation as prediction/modeling”
• Program/algorithm/computation is
“…an abstract specification for a causal system.”
Chalmers, 1997
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Design for example type 1
• Type 1 (e.g. engine speed governor)
▫ only two actions (increase/decrease steam)
▫ predictable from current engine speed
▫ any mechanism that
provides such control is fine:
 Watt’s Centrifugal Governor (mechanical)
 Embedded microprocessor-based controller.
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Design for example type 3
• Type 3 ( human-level behaviour)
▫ with no a priori knowledge of world
an agent can only store what it senses
& detect similar situations in the future.
▫ combined with record of temporal
sequence & of its actions
▫ it has info to make “intelligent” actions!
But how? back to basics…
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Communication....
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Storage... (copy)
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Recognition... (copy)
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Storage... (link)
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Recognition... (link)
- exact/partial match
- flat/hierarchical structure
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Internal & External symbols...
internal
symbols
C
A
T
external
symbols
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Relating word to object
Situation in which
word “CAT” is heard
and cat is seen
audio
senses
“CAT”
visual
senses
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z
Logically
• Conventional
“if a & b & c then z”
• Alternative, Inscriptors
a
b
c
“if z then a & b & c”
▫ causal, rule-following, “not”, but uses abduction
fill-in expectations (top-down/bottom-up) so flexible
▫ storing what seen, so syntax & semantics match
▫ decouple from input (state retaining)
▫ Model-like (simple incomplete or combine…~PSC)
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The Architecture of Cognition
• Is Cognition Symbolic or Connectionist?
▫ Differ based on “copy” or “link” storage
▫ Shown both can do the job, so
▫ they are genuine design alternatives.
(as are analog/digital & serial/parallel)
• Is the PSSH wrong then?
▫ No, it is setting functional requirements.
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To Conclude
• Presented a principled distinction between
classical symbolic & connectionist approaches,
showing them to be genuine design alternatives.
• Distinguished (Newell’s) PSS from the symbolic
paradigm, per se.
• Hopefully in an understandable way
(… so avoiding Bonini’s paradox)!
The End
(… of the beginning?)
Thank you.
PT-AI 2011