Open Issues in AI
We have now a spectrum of techniques of search,
learning and evolution.
How do we put all these together?
Some open issues in AI:
- Architectures of Mind
- What does the whole mind look like?
Network, Hierarchy or Society?
Does I/O link to many brains or one?
Who is in charge?
Where am I? What is consciousness?
- Action Selection
- As a more specific example of the above.
We know how to solve 1 problem.
How does the creature deal with multiple problems
at once?
- "Learning to Learn"
- How does the creature generate goals for itself
in the first place?
Machine learning algorithms all learn for a while
and then converge (stop learning).
Why do humans not converge?
- Symbol-grounding, Evolution of language.
- What is language?
How do creatures processing numerical sensory data
end up processing symbolic "words" with meanings?
What does "chair" mean, internally?
Is it a meaningless token #5099 being passed around,
or is it a whole specialised sub-system,
firing away?
Do parts of the brain talk to each other?
Do we have an internal language?
Is it English, or is it something more messy?
Will sub-symbolic AI plug in neatly to symbolic AI?
- Robots or simulation?
- Robots are more real, may solve symbol-grounding.
But experiments in simulation
(the Web?)
are more practical.
Sims could never have evolved his 3-D robots
in hardware.
(Though a field of Evolutionary Hardware does exist.)
On Internet since 1987.
