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As someone who took this approach to AI I don’t see another way to get there. We should be reverse engineering the salient aspects. But neuroscience won’t be su
by rdlecler1 8y ago
As someone who took this approach to AI I don’t see another way to get there. We should be reverse engineering the salient aspects. But neuroscience won’t be sufficient, we should be looking at neurogenesis. My bet is that the implementation details won’t matter that much and that the main driver is the architecture and ability to construct those architectures. Right now we assume fully connected ANNs and then optimize those connections. Neurogeneis certainly doesn’t work that way.
- sqrt17 8y agoOur understanding of the neural tissue of the brain - as well as what it's actually doing are so poor, and the needs of what we're doing so different that there's a greater chance that our understanding of neural tissue is improved by developing our understanding of deep learning than the other way around. To put it in bird flight analogy, by constructing airplanes and making it an exact science, we're able to get a much finer understanding of the pricipal problems in flight and get to appreciate the difference between the flight of colibris and bumblebees (who can't fly by gliding but must beat their wings frenetically) versus larger birds (which can fly by gliding, more closely resembling plane flight).
- rdlecler1 8y agoYour underlying assumptions here is that the the brains material implementation details are a dominant driver in the computational function of the brain. I’ve studied networks that suggest the function falls out of the topology (circuitry) not the material implementation details.