Chuck Esterbrook wrote:
On 3/20/07, Ben Goertzel <[EMAIL PROTECTED]> wrote:
I would certainly expect that a mature Novamente system would be able to
easily solve this kind of
invariant recognition problem. However, just because a human toddler
can solve this sort of problem easily, doesn't
mean a "toddler" level AGI should be able to solve it equally easily.
Different specific modalities will
come more naturally to different intelligences, and humans are
particularly visual in focus...
I generally agree, but wanted to ask this: Shouldn't AGIs be visual in
focus because we are? We want AGIs to help us with various tasks many
of which will require looking at diagrams, illustrations and pictures.
And that's just the static material.
Eventually, yeah, a useful AGI should be able to process visual info,
just like it should be able to understand human language.
But I feel that the strong focus on vision that characterizes much
AI work today (especially AI work with a neuroscience foundation)
generally tends to lead in the wrong direction, because vision
processing in humans is carried out largely by fairly specialized
structures and processes (albeit in combination with more general-
purpose structures and processes). So, one can easily progress
incrementally
toward better and better vision processing systems, via better and
better emulating the specialized component of human vision processing,
without touching the general-understanding-based component...
Of course, the same dynamic happens across all areas of AI
(creating specialized rather than general methods being a better
way to get impressive, demonstrable incremental progress), but it happens
particularly acutely with vision
Gary Lynch, in the late 80's, made some strong arguments as to why
olfaction might in some ways be a better avenue to cognition than vision.
Walter Freeman's work on the neuroscience of olfaction is inspired by
this same idea.
One point is that vision processing has an atypically hierarchical
structure in the
human brain. Olfaction OTOH seems to work more based on attractors
and nonlinear dynamics (cf Freeman's work), sorta like a fancier Hopfield
net (w/asymmetric weights thus leading to non fixed point attractors). The
focus on vision has led many researchers to overly focus on the hierarchal
aspect rather than the attractor aspect, whereas both aspects obviously
play
a bit role in cognition.
I guess I worry about the applicability... Would a blind AGI really be
able to find more effective treatments for heart disease, cancer and
aging?
IMO vision is basically irrelevant to these biomedical research tasks.
Direct "sensory" connections to biomedical lab equipment would
be more useful ;-)
Regarding Numenta, they tout "irrespective of scale, distortion and
noise" and they chose a visual demonstration, so it seems that at
least their AGI work is deserving of Kevin's criticism.
I agree. Poggio's recent work on vision processing using brain models
currently seems more impressive than Numenta's, in terms of combining
-- far greater fidelity as a brain simulation
-- far better performance as an image processing system
But, the Numenta architecture is more general and may be used very
interestingly in future, who knows...
-- Ben
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