>
>
> You will have another, different CYC-type failure, if you attempt to
> hand-code (by humans) the visual subsystem. Automation is kind-of the
> whole point of deep learning, etc.
>
> This would be great if the system could also learn how to interpret visual
data without a need to hard code anything. However, I am afraid it would be
more chalanging than anything else. Humans have built-in visual processing
system. Imagine that someone presents you images in a form of sequence of
color codes of consecutive pixels like #FF88AA#0F0F0F and expect to match
images presenting the same object seen from different angle. This would be
a very difficult task for human.
It is chalanging enough to make a system, which learn that cat ("C") chases
and eats every mouse ("M") if the only input given is a time-based sequence
of text screen-shots of the situation like
........................
....M...................
........................
................C.......
........................
....M..........M........
........................
Transforming character matrix representation into list of animals with
their current position, finding that x, y of C approaches x, y of some M,
etc. would not be easy. That's why testing OpenCog in Minecraft environment
seems like a good first step.
>One reason that people are infatuated with deep-learning neural nets is
>that NN's provide a concrete, achievable architecture that is proven to
>work, and is closely described in thousands of papers and books, so any
>joe-blow programmer can sit down and start coding up the algorithms, >and
get some OK results
I also experienced that when I say Artificial Intelligence, people think:
Neural Networks. Too few people try different approaches.
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