COGS 300: Understanding and Designing
Cognitive Systems
Bob Woodham
Department of Computer Science
University of British Columbia
Lecture Notes 2013/2014 Term 2
13W2: January–April, 2014
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Today’s “Person” Example: Adrianne Haslet-Davis
Hugh Herr’s TED 2014 talk included a dance performance by Adrianne
Haslet-Davis. Adrianne is a professional dancer who lost her lower left
leg in the Boston Marathon bombings, April, 15, 2013. Click here to
read more about Adrianne’s journey
Hugh Herr (left), Adrianne Haslet-Davis (middle), Christian Lightner (right)
Photo credit: James Duncan Davidson
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This Week’s Learning Goals
1
Highlight (and complete) Bob’s “story” for COGS 300
2
Consider “chunking” knowledge (Minsky frames)
3
Re-examine the role the external environment plays in cognitive
systems
— Simon’s ant
— Brooks’ “intelligence without representation”
— Mason’s “kicking the sensing habit”
4
Collective intelligence (beyond individual humans)
5
Final words (on death) from Steve Jobs and George Wald
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Question Regarding Final Exam
For the final exam, I am in favour of allowing each student one
(standard) letter size (8.5 × 11) handwritten double-sided sheet of
notes
A) Yes
B) No
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Sine qua non of Intelligence
What are essential attributes of intelligence?
(Symbolic) language
(abstraction, composition)
Learning
Consciousness
..
.
Tool use
Embodiment
(act/interact directly with/in the world)
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Bob’s “Story” for COGS 300
Questions to ask about knowledge representation (KR):
— What language is used?
— What knowledge is represented explicitly?
— How to reason (i.e., make explicit that which is implicit)?
Distinguish actual knowledge of the world from an agent’s belief
about the world
— frequentist versus Bayesian intepretation of probability
— Chomsky versus Norvig
— GOFAIR (fully observable, deterministic, closed world) versus
Mackworth’s CBA framework (robot soccer)
Robots that interact with the world
— Situated/embodied cognition
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Problems with Google Flu Trends (GFT)
A paper published in Science, March 14, 2014, explores two issues
that contributed to GFT’s “mistakes”
1
“Big data hubris:” Most big data are not the output of instruments
designed to produce valid and reliable data amenable for scientific
analysis
2
“Algorithm dynamics:” Google’s search algorithm is not static over
time. Changes are regularly made that alter the results of Google
search and, consequently, alter users’ use of Google search
Lazer et al., “Big data. The parable of Google Flu: traps in big data
analysis,” Science 343(6176)1203–05, March 14, 2014
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Quiz 1
A Minksy frame
A) represents an atypical situation
B) is never linked to another frame
C) contains only fixed information
D) None of the above
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Quiz 1
A Minksy frame
A) represents an atypical situation
B) is never linked to another frame
C) contains only fixed information
D) None of the above
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Quiz 2
What’s TRUE about “top levels” of a Minsky frame?
A) They have many “slots” that must be filled by specific instances or
data
B) They’re fixed
C) They represent things that are always true about the supposed
situation
D) B & C
E) All of the above
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Quiz 2
What’s TRUE about “top levels” of a Minsky frame?
A) They have many “slots” that must be filled by specific instances or
data
B) They’re fixed
C) They represent things that are always true about the supposed
situation
D) B & C
E) All of the above
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Quiz 3
According to Minsky when an important condition cannot be satisfied
for a frame what accommodations should be made?
A) Matching
B) Excuse or explain
C) Advice
D) Summary
E) All of the above
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Quiz 3
According to Minsky when an important condition cannot be satisfied
for a frame what accommodations should be made?
A) Matching
B) Excuse or explain
C) Advice
D) Summary
E) All of the above
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Quiz 4
What are the steps in a first-order sketch of a GSF skeleton?
A) See it then draw it
B) Abstraction, sorting, transform
C) A & B
D) Seeing, frame-activation, instantiation
E) None of the above
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Quiz 4
What are the steps in a first-order sketch of a GSF skeleton?
A) See it then draw it
B) Abstraction, sorting, transform
C) A & B
D) Seeing, frame-activation, instantiation
E) None of the above
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Quiz 5
According to Minsky, someone is “clever” when they are. . .
A) Good at solving difficult problems
B) Unusually good at quickly locating highly appropriate frames
C) Able to retrieve long-term memory
D) None of the above
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Quiz 5
According to Minsky, someone is “clever” when they are. . .
A) Good at solving difficult problems
B) Unusually good at quickly locating highly appropriate frames
C) Able to retrieve long-term memory
D) None of the above
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Two Motivating Examples
1. Remembering positions of pieces on a chess board:
Chess masters remember the positions of pieces on a chessboard
much better than novices when the positions come from an actual
game
Chess masters remember the positions of pieces on a chessboard
no better than novices when the positions are random
2. Imagining a beach ball:
When asked to imagine a beach ball, people imagine an object
with a specific shape and function. But, they also assign a specific
colour and pattern to the ball they have imagined
The claim is that “imagination” assigns all of an object’s essential
properties, using default values for properties not otherwise
known or given
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Minsky’s Frames
The essence of Minsky’s frame theory is contained in the quote:
“When one encounters a new situation (or makes a
substantial change in one’s view of the present problem) one
selects from memory a substantial structure called a frame”
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Minsky’s Frames: Observations/Conjectures
1
Although presented as a broad theory of AI, many of Minsky’s
ideas arise from problems in perception, especially vision
2
“Visual experiences seems continuous. . . . . . continuity depends
on confirmation of expectations which in turn depends on rapid
access to remembered knowledge about the visual world”
3
Expertise means having more frames that are relevant to a given
task, not in having frames that are more complex. “Where a
layman uses 10 frames for some job, an expert might use 1,000
and thus get the appearance of a different order of performance”
4
“A theory of seeing should also be a theory of imagining”
5
“. . . language understanding. . . . . . somewhat parallel to seeing”
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Minsky’s Frames: Observations/Conjectures (cont’d)
6
Frames connect factual and procedural content. “A frame is a
collection of questions to be asked about a hypothetical situation;
it specifies issues to be raised and methods to be used in dealing
with them”
7
Frame systems are organized as similarity networks, not as a strict
hierarchy of classes. “. . . ‘concepts’ are interrelated in different
ways when in different contexts, and so no single hierarchical
ordering is generally satisfactory for all goals.” Note: This is similar
to how concepts (i.e., articles) are organized in Wikipedia
8
“The primary purpose in problem solving should be better to
understand the problem space, to find representations within
which the problems are easier to solve”
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Minsky’s Frames: Observations/Conjectures (cont’d)
9
Minsky suggests an evolutionary primacy to vision stating, “. . . the
requirements of three-dimensional vision may have helped the
evolution of frame-like representations in general”
10
Minsky contrasts “discrete symbolic descriptions” with “entities
with the properties of continua” and states, “I am convinced that
the symbolic models are the more profound ones and that,
perhaps paradoxically to some readers, continuous structures are
restrictive and confining”
11
“. . . only a process that can reflect on what it has done – that can
examine a record of what has happened – can have any
consequences”
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Minsky’s Frames (concluded)
Minsky summarizes his argument against quantitative models in the
following general principle:
“Thesis: the output of a quantitative mechanism, be it
numerical, statistical, analogue, or physical (non-symbolic), is
too structureless and uniformative to permit further analysis.
Number-like magnitudes can form the basis of decisions for
immediate action, for muscular superpositions, for filtering
and summing of stimulus features, and so forth. But each is a
‘dead-end’ so far as further understanding and planning is
concerned, for each is an evaluation – and not a summary.
A number cannot reflect the considerations that formed it.1
Thus, although quantitative results are useful for immediate
purposes, they impose a large cost on further and deeper
development”
1
Emphasis in original
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