8 ms·

You 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

• 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 ago
    I read this four times now, but I don't understand what you are saying.
    • olalonde 4y ago
      Can 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.