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It's crazy and scary at the same time how fast new approaches are improving machine learning efficiency. Am I only one who thinks that we will be able to simula
by xiphias 10y ago
It's crazy and scary at the same time how fast new approaches are improving machine learning efficiency. Am I only one who thinks that we will be able to simulate the brain with much less computational power than the brain itself has?
- zenlikethat 10y agoThe brain is a highly inefficient system evolved arbitrarily and slowly over a long time. It does seem likely that we could, with much fewer computational resources, simulate the function of some of the tasks which brains are responsible for or "good at", such as image classification. However, "simulating the brain" in general is a much broader problem in scope. Consider: Can you even really truly say that you have "simulated the brain" without also including a physical body for this system, since a brain is inherently tied to a body and corporal existence in the world? It's not clear. It's likely the problem won't be computational power but figuring out how to extract or isolate only the bits of the system we are interested in from the rest. Consider the genome, for instance: we have had the human genome mapped since 2003 but the unfathomable complexity of the system as a whole makes it difficult (though not impossible) to apply for useful stuff. It's not just about computational power, it's also about defining computational models that can create human-competitive performance for certain tasks, or, in the case of AGI, for all tasks that might be expected of a human. That's the fiendishly tricky bit.
- zbyte64 10y agoWho you calling inefficient? :-p Those "inefficiencies" are for handling a messy noisy world where we can't just settle on a single solution. There is allot of overhead to play in the game of evolution.
- BenoitP 10y agoYou're not the only one. A neuron nucleus can be as small as 3 μm, ... which could pack about tens or hundreds million 5nm transistors. There is plenty of room at the bottom, as Feynman said.
- option 10y agoauthor here. didn't expect our paper to appear on HN :) But regarding: "Am I only one who thinks that we will be able to simulate the brain with much less computational power than the brain itself has?" - I really doubt that. In fact my bet is that we won't. If anything our learning approaches looks much more inefficient (data-wise) compared to what brain can do. - And the paper doesn't study this question. It is a focused study on speed/accuracy tradeoff for LSTM cells
- andrepd 10y agoWhat does "computation power of the brain mean"? The brain does not operate similar to a computer; how can you make comparisons?