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While I agree that ANNs are probably a poor model for how the brain really works, your comparison isn't necessarily a fair one. While ANNs start their learning
by olalonde 4y ago
While I agree that ANNs are probably a poor model for how the brain really works, your comparison isn't necessarily a fair one. While ANNs start their learning with an empty/random state, the human brain starts with a bunch of connections that are pre-encoded in DNA.
With that said, my guess is that we'll probably have to take a few more hints from the biology of the brain before we are able to achieve "human level intelligence".
- hansword 4y agoYou seem to have completely missed that NNs run on physical hardware. A contemporary CPU and/or GPU is 'a bunch of connections that are pre-encoded' - just not in DNA, but in silicon.
- olalonde 4y ago> A contemporary CPU and/or GPU is 'a bunch of connections that are pre-encoded' - just not in DNA, but in silicon. No, CPUs/GPUs are not relevant if we are interested in the speed of learning in relation to the quantity of stimuli processed. You could even compute an ANN's learning algorithm with a pen and paper and it wouldn't change its "learning speed" within that definition. Pre-trained weights would. Regardless, this head start is probably not sufficient to explain the disparity between the brain's capacity to learn and modern ANNs. ANNs are probably just not a very good approximation of how the brain works, for now at least.
- hansword 4y agoI read this four times now, but I don't understand what you are saying.
- olalonde 4y agoCan you be more specific? What I meant is this: if we postulate that the brain really is just like an ANN, it would be more like an ANN with pre-trained weights, thanks to evolution. In contrast, an ANN is typically initialized with random weights. A pre-trained network learns a lot faster than a randomly initialized one.