“When David Marr at MIT moved into computer vision, he generated a lot of excitement, but he hit up against the problem of knowledge representation he had no good representations for knowledge in his vision systems.”
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Kubrick's vision seemed to be that humans are doomed, whereas Clarke's is that humans are moving on to a better stage of evolution.
If you just have a single problem to solve, then fine, go ahead and use a neural network. But if you want to do science and understand how to choose architectures, or how to go to a new problem, you have to understand what different architectures can and cannot do.
I think Lenat is headed in the right direction, but someone needs to include a knowledge base about learning.
Societies need rules that make no sense for individuals. For example, it makes no difference whether a single car drives on the left or on the right. But it makes all the difference when there are many cars!
