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A New Artificial Intelligence 7
Kevin Warwick
Embodiment & Questions
Issues of Modern AI
• We will look here at some of the important
questions facing AI today
• We will open up some of the directions
being taken
• We will attempt to move away from the
restrictions imposed by Classical AI
Brains
• A brain has different neuronal structures
each with a specialised role – sensory,
motor, inter
• Neurons communicate through BINARY
(not analogue) codes
• We know something about the physical chemical aspects of the brain
• We know almost nothing about how
memories are encoded or faces are
recognised
Innate Knowledge
• Can learning occur on a blank slate?
• Must there be some prior bias?
• Are memories inherited?
• Meaningful convergence of ANNs depends on
number of neurons + topology + learning
• Is this also true of a brain?
• Are there hard wired cognitive biases?
Genetics/emergence
• Darwinian (natural) selection – shapes
individual behaviours
• AND/OR
• Lamarckian evolution – offspring inherit
acquired characteristics (e.g. giraffe)
• LEAD TO
• Strengthening of particular circuits in the
brain & weakening of others
Plato – unsupervised learning?
• How can you enquire, Socrates, into that
which you do not already know?
• What will you put forth as the subject of
the enquiry?
• And if you find out what you want, how will
you ever know that this is what you did not
know?
• i.e. how can we know we are someplace
when we do not know where we are going?
Questions
• Perceptions depend on distributed neural
codes – how are these combined?
• What we perceive is highly dependent on
how our brain attempts to interpret a
situation/scene – how?
• How does an individual acquire language?
• How does a brain index temporally related
information?
Agents + Emergence
• Idea - The mind is organised into sets of
specialised functional units (Minsky)
• Modular theories good for agents
• Emergent globally intelligent behaviour
arises from the cooperation of large
numbers of agents
• Supported by fMRI scans
Piaget
• Humans assimilate external phenomena
according to our present understanding
• We accommodate our understanding to
the demands of the phenomena
Kant
• Schemata – apriori structure used to
organise experience of the external world
• Observation is not passive and neutral but
active and interpretive
Perception
• Perceived information never fits precisely into
our schemata
• Depends on I/O devices – in humans and robots
• With different I/O the real world will be
perceived differently
• Each entity has a different concept of reality
• There is NO absolute reality! (Berkeley)
Embodiment in cognition
• Classical AI – instantiation of a physical
symbol system is irrelevant to its
performance – structure is important
(Brain in a vat)
• New AI - Intelligent action requires a
physical embodiment that allows the entity
to be integrated in the world
• Present day robot I/O limited – requires
more complexity in interfacing
Culture
• Classical AI – Individual mind is the sole
source of intelligence
• But knowledge is a social construct – an
understanding of the social context of
knowledge and behaviour is also
important (memes!)
Interpretations - Communication
• Symbols are used in context – a domain
has different interpretations, depending on
the goals
• Sign interpretation – coding system
• The meaning of a symbol is understood in
the context of its role as an interpretor
Falsifiable Computation
• Any number of confirming experiments are
not sufficient for confirmation of a theory
• Scientific theories must be falsifiable
• There must exist circumstances under
which a model is a poor approximant
• Many computational models are not
falsifiable – universal machines!
• Need computation that is falsifiable
Let’s Move On
• Classical AI – (Hobbes/Locke/Aristotle) –
•
•
•
intelligent processes conform to universal laws
and are understandable/modelable
Converse (Winograd/Penrose/Weisenbaum) –
important aspects of intelligence cannot be
modelled
A model/simulation is not the real thing
The only ‘exact’ simulation of a human brain
would be that specific human brain and no other
– even then it would need to be in its place/time
Differences
• Just because something is different does
not make it worse
• A simulation of a human brain could be
more/less intelligent/conscious/selfaware/understanding
• Models/simulations are used to explore,
explain & predict – if a model is proven to
be accurate for this then that’s just fine
Comments on Intelligence
• As long as we understand the basics of
what intelligence is, that is sufficient
• We should not be bogged down by trying
to copy exactly the functioning of the
human brain, interesting though that
might be
• More interesting is to create entities that
are intelligent in their own right
Next
• Growing Brains – Biological AI
Contact Information
• Web site: www.kevinwarwick.com
• Email: [email protected]
• Tel: (44)-1189-318210
• Fax: (44)-1189-318220
• Professor Kevin Warwick, Department of
Cybernetics, University of Reading,
Whiteknights, Reading, RG6 6AY,UK