> Naive trend-extrapolation (assuming 1 synapse == 1 parameter) implies 17 iterations of Moore's law before we get human level AI, or some sort of ASIC, I suppo

> Naive trend-extrapolation (assuming 1 synapse == 1 parameter) implies 17 iterations of Moore's law before we get human level AI, or some sort of ASIC, I suppose. I'm not sure how you get that. My point is that artificial NNs may well be more efficient than biological ones because they are punching so far above their weight based on what you would expect from a naive comparison of parameters, in which case it's not 17 iterations but a lot less. Further, modern NNs don't typically max out the memory of a single GPU (since we want to do minibatches of at least n=10 for the training phase and researchers care a lot less about the forward passes or deployment), and groups like Google Brain or Deepmind have shown the ability to use ~1000 GPUs, so that's 4 orders of magnitude right there (doable with asychronous training like synthetic gradients). > I'm not sure what evidence the performance of these systems show us, as bees are pretty smart for their size. What do bees do that is remotely as demanding as being able to translate between English, French, German, Japanese etc at very high quality?