Transcript animated version

Course Overview
 What is AI?
 What are the Major Challenges?
 What are the Main Techniques?
Part I:
Introduce you to
what’s happening in
Artificial Intelligence
 Where are we failing, and why?
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Done
 Step back and look at the Science
 Step back and look at the History of AI
 What are the Major Schools of Thought?
 What of the Future?
Part II:
Give you an
appreciation for
the big picture
 Why it is a
grand challenge
Course Overview
 What is AI?
 What are the Major Challenges?
 What are the Main Techniques?
Part I:
Introduce you to
what’s happening in
Artificial Intelligence
 Where are we failing, and why?

Done
Step back and look at the Science
 Step back and look at the History of AI
 What are the Major Schools of Thought?
 What of the Future?
Part II:
Give you an
appreciation for
the big picture
 Why it is a
grand challenge
The Language of Thought
 What is the language we “think in”?
 Is it our natural language, e.g. English, or mentalese?
 Some introspective arguments against natural language
 Word is “on the tip of my tongue”, but can’t find it
 Difficult to define concepts in natural language, e.g. dog, anger
 We have a feeling of knowing something, but hard to translate to
language
 Some observable evidence against natural language
 Children reason with concepts before they can speak
 We often remember gist of what is said, not exact words
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Cognitive science experiment: (recall after 20 second delay)
He sent a letter about it to Galileo, the great Italian Scientist.
He sent Galileo, the great Italian Scientist, a letter about it.
A letter about it was sent to Galileo, the great Italian Scientist.
Galileo, the great Italian Scientist, sent him a letter about it.
Represent as Propositions
 Just like the logic we had for AI
isa
 likes(john,mary)
likes
a
apple
gives
mary
john
john
a
mary
Evidence for Propositions
 A cognitive Science experiment (Kintsch and Glass)
 Consider two different sentences,
but both with three “content words”
 The settler built the cabin by hand.
 The crowded passengers squirmed uncomfortably.
Evidence for Propositions
 A cognitive Science experiment (Kintsch and Glass)
 Consider two different sentences,
but both with three “content words”
 The settler built the cabin by hand.
 One 3-place relation
 The crowded passengers squirmed uncomfortably.
 Three 1-place relations
 Subjects recalled first sentence better
 Suggests it was simpler in the representation
 (Cognitive Science involves a fair bit of guessing!)
Associative Networks
 Idea: put together the bits of the propositions that are similar
Associative Networks
 Idea: put together the bits of the propositions that are similar
isa
likes
gives
a
mary
apple
john
john
a
mary
Associative Networks
 Idea: put together the bits of the propositions that are similar
likes
isa
mary
john
a
apple
gives
Associative Networks
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Idea: put together the bits of the propositions that are similar
Each node has some level of activation
Activation spreads in parallel to connecting nodes
Activation fades rapidly with time
A node’s total activation is divided among its links
Associative Networks
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Idea: put together the bits of the propositions that are similar
Each node has some level of activation
Activation spreads in parallel to connecting nodes
Activation fades rapidly with time
A node’s total activation is divided among its links
 These rules make sure it doesn’t spread everywhere
 Nodes and links can have different capacities
 Important ones are activated very often
 Have higher capacity
 These ideas seem to match our intuition from introspection
 One thought links to another connected one
Associative Networks
Cognitive Science experiment (McKoon and Ratcliff)
 Made short paragraphs of connected propositions
 Subjects viewed 2 paragraphs for a short time
 Subjects were shown 36 test words in sequence
and asked if those words occurred in one of the stories
 For some of the 36 words, they were preceded by a word from
same story
 For some of the 36 words, they were preceded by a word from
other story
 Word from same story helped them remember
 …Suggests it is because they were linked in a network
 They also showed recall was better if closer in the network
 …Suggests activation weakens as it spreads
Schemas
 Propositional networks can represent specific knowledge
 John gave the apple to Mary
 …but what about general knowledge, or commonsense?
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Apple is edible fruit
Grows on a tree
Roundish shape
Often red when ripe…
Could augment our proposition network
Add more propositions to the node for apple
Apple then becomes a concept
The connections to apple are a schema for the concept
 What about more advanced concepts/schemas like a trip to a
restaurant?...
Scripts
Elements of a script…
 Identifying name or theme
 Eating in a restaurant
 Visiting the doctor
 Typical roles
 Customer
 Waiter
 Cook
 Entry conditions
 Customer hungry, has money
Scripts
 Sequence of goal directed scenes
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Enter
Get a table
Order
Eat
Pay bill
Leave
 Sequence of actions within scene
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Get menu
Read menu
Decide order
Give order to waiter
Scripts
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How to represent a script?
Could use proposition network for all the parts
… but maybe whole script should be a unit
Introspection suggests that it is activated as a unit
without interference from associated propositions
 Experimental evidence (Bower, Black, Turner 1979)…
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Got subjects to read a short story
Story followed a script, but didn’t fill in all details
They were then presented various sentences
Some from story, and some not
Some trick sentences were included:
 Not from the story, but part of the script
 Subjects were asked to rate 1(sure I didn’t read it) -7(sure I did read it)
 Subjects had a tendency to think they read the trick sentences
 Suggests that they activate the script and fill in the blanks in memory
…Starting to get a Model of the Mind
 Propositional-schema representations stored in long-term
memory
 Associative activation used to retrieve relevant memories
 …but many details unspecified
 Need more machinery to account for
 Assess retrieved information, see does it relate to current goals
 Decompose goals into subgoals
 Draw conclusions, make decisions, solve problems
 More importantly:
 How to get new propositions and schemas into memory
 Schemas are often generalised from examples, not taught
 What about working memory?
Working Memory
 Most long-term memory not “active” most of the time
 Just keep a few things in working memory for current
processing
 Very limited: try multiplying 3-digit numbers without paper
 Working memory holds 3-4 chunks at a time
 Why so limited? (it seems useful to have more nowadays)
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Maybe complex circuitry required
Maybe costly in energy
Maybe tasks were less complex in environment of early humans
Or maybe more working memory would cause too many clashes,
or be too hard to manage
 However limits can be overcome by skill formation
 Note also: limit of 3-4 does not mean other “propositions” inactive
 Could be a lot more going on subconsciously
Skill Acquisition
 With a lot of practice we can “automate” many tasks
 We distinguish this from “controlled processing” – using working memory
 Once automated:
 Takes little attention or working memory
(these are “freed up”)
 Hard not to perform the task – cannot control it well
 Most advanced skills use a combination
 Automatic processes under direction of controlled processes, to meet goals
 Examples: martial arts expert, or musician
Is Skill Acquisition Separate?
 Evidence from Neuropsychology:
 People with severe “anterograde amnesia”
 Cannot learn new facts
 i.e. can’t get them into long-term propositional memory
 …but can learn new skills
 Example:
 Can learn to solve towers of Hanoi with practice
 But cannot remember any occasion when they practised it
 Suggests that a different part of the brain handles each
 Skill may reside in visual and motor systems, rather than central systems
 Maybe because of evolution:
 Animals often have good skill acquisition
 Maybe humans evolved a specific new module for high level functions
Mental Images
 Sometimes we seem to evoke visual images in “mind’s eye”
 Subjective experience suggests visual image is separate from propositions
 …but need experimental evidence
 In imagining a scene:
 Example: search a box of blocks for 3cm cube with two adjacent blue sides
 Properties are added to a description
 But not so many properties as would be present in a real visual scene
 Support, illumination, shading, shadows on near surfaces
 Image does not include properties not available to visual perception
 Other side of cube
 Intuition suggests that “mind’s eye” mimics visual perception
 Maybe it uses the same hardware?
 Would mean that “central system” sends information to vision system
Mental Images
Hypothesis: there is a human “visual buffer”
 Short-term memory structure
 Used in both visual perception and “mind’s eye”
 Special features/procedures:
 Can load it, refresh it, perform transformations
 Has a centre with high resolution
 Focus of attention can be moved around
Assuming it exists… what good is it?
 Allows you to pull things out of your visual long term memory
 Use it to build a scene, with all spatial details filled in
 Useful to plan a route, or a rearrangement of objects
 Experiment: how many edges on a cube?
 (Assuming answer is not in long term memory)
Experiments to show Mental Images
Test a special procedure: mental rotation
Experiments to show Mental Images
Time taken depended on how much rotation was needed
 Suggests that we really rotate in the “visual buffer”
Experiments to show Mental Images
Experiments to show Mental Images
 However… just because we rotate stuff doesn’t necessarily mean that we do
it in the “visual buffer”
 …Need more evidence
 PET brain scans have shown that the “occipital cortex” is used
 “occipital cortex” is known to be involved in visual processing
So far…
The “Symbolic” Approach to
explaining cognition
an alternative…
the “Connectionist” approach…
So far…
The “Symbolic” Approach to
explaining cognition
an alternative…
the “Connectionist” approach…
Connectionist Approach
 What is connectionism?
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Concepts are not stored as clean “propositions”
They are spread throughout a large network
“Apple” activates thousands of microfeatures
Activation of apple depends on context, no single dedicated unit
 Neural plausibility
 Graceful degradation, unlike logical representations
 Cognitive plausibility
 Could explain entire system, rather than some task in central system
(symbolic accounts can be quite fragmented)
 Could explain the “pattern matching” that seems to happen everywhere
(for example in retrieval of memories)
 Explain how human concepts/categories do not have clear cut definitions
 Certain attributes increase likelihood (ANN handles this well)
 But not hard and fast rules
 Explains how concepts are learned
 Adjust weights with experience
Another Perspective on Cognitive Science / AI
 We have seen multiple models for the mind,
and each has an “AI version” too
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Propositions  AI’s logic statements
Scripts  AI’s case based reasoning
Mental images  AI: some work, but not much
Connectionist models  AI’s neural networks
 This gives us another perspective on Cognitive Science / AI
 Both are working in different directions
 AI person starts with a computer and says
 How can I make this do something that a mind does?
 May take some inspiration from what/how a mind does it
 Cognitive Science person starts with a mind and says
 How can I explain something this does, using the “computer metaphor”?
 May take some inspiration from how computers can do it
 Especially from how AI people have shown certain things can be done
Another Perspective on Cognitive Science / AI
 We have seen multiple models for the mind,
Which
modeltoo
is correct?
and each has an “AI
version”
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Propositions  AI’s logic statements
…possibly… all of them
Scripts  AI’s case based reasoning
Mental images  AI: some work, but not much
i.e. all working together
Connectionist models  AI’s neural networks
 This gives us another
perspective
on Cognitive
e.g. we have
seen that logic
could be Science / AI
 Both are workingimplemented
in different directions
on top of Neurons
(need
in “clean” and
symbolic
 AI person starts
withnota be
computer
saysway)
 How can I make this do something that a mind does?
This
would give
opportunity
logical
 May take some
inspiration
from
what/howfor
a mind
does it
 Cognitive Science personreasoning,
starts with a mind and says
while still having “scruffy” intuitions
 How can I explain going
something
does, using the “computer metaphor”?
on in this
the background.
 May take some inspiration from how computers can do it
 Especially from how AI people have shown certain things can be done