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The problem I worry about with the neuroscience narrative is that it seems easy to justify everything. As I understand it, we don't understand the brain very we

by colah3 11y ago

The problem I worry about with the neuroscience narrative is that it seems easy to justify everything. As I understand it, we don't understand the brain very well, and there's lots of different theories. A fair number of neural net papers will make appeals to neuroscience to motivate something. Whenever I read these, I wonder to what extent neuroscience could be cited to argue the opposite. There's a lot of ex-neuroscientists in deep learning. Amusingly, my experience has been that they're generally the most skeptical of the neuroscience narrative. Of course, some amazing researchers seem to really like the neuroscience narrative -- this is obviously a strong point in its favor. I don't mean to dismiss it. I just think that one wants to be kind of careful when using it as a vantage for thinking.

seiji 11y ago
I wonder to what extent neuroscience could be cited to argue the opposite. That's a great point. Often, in brains and bodies in general, the same mechanism causes more than one result. Sometimes even the absence of the mechanism causes the same result too. Biological understanding has a high number of latent variables we may not even know exist. (plus, arguing "because biology" is just a few steps away from arguing "because quantum" when motivated by vague optimism.) The best overall insight from neuroscience is: AI, as envisioned by the 1950s pioneers, is possible. Brains are complex, but not irreducibly complex. Just slam some gametes together and a new brain appears. We can, at some level, eventually, get it all working digitally. The best useful insight from neuroscience seems to be: the never-ending a/b test of natural selection converged on neurons+synapses as a universal computation system. But, neurons+synapses aren't quite enough detail to replicate a brain—that's like saying our computers use transistors, so all we need is transistors to build a CPU. Sure, that's important, but the structure and topology and interconnections are what make the magic happens. What's the end result? More design or more brute force? Our GPUs are still Moore's Lawing themselves every few years. We are in early days of experimentation-at-scale here. Since we will always need an overwhelmingly higher number of model parameters than data, we can't intricately tune models by hand (alternative: make an ANN to optimize ANN architectures). One breakthrough we don't really have is a minimum-description for ANN architectures. If that were to come about, we could have "ANN DNA" then 'breed' ANNs together so their topologies and functions merge to generate a (hopefully) more fit offspring. Then just keep repeating until they rise up and destroy us all.

by colah3 11y ago

seiji 11y ago

I wonder to what extent neuroscience could be cited to argue the opposite. That's a great point. Often, in brains and bodies in general, the same mechanism causes more than one result. Sometimes even the absence of the mechanism causes the same result too. Biological understanding has a high number of latent variables we may not even know exist. (plus, arguing "because biology" is just a few steps away from arguing "because quantum" when motivated by vague optimism.) The best overall insight from neuroscience is: AI, as envisioned by the 1950s pioneers, is possible. Brains are complex, but not irreducibly complex. Just slam some gametes together and a new brain appears. We can, at some level, eventually, get it all working digitally. The best useful insight from neuroscience seems to be: the never-ending a/b test of natural selection converged on neurons+synapses as a universal computation system. But, neurons+synapses aren't quite enough detail to replicate a brain—that's like saying our computers use transistors, so all we need is transistors to build a CPU. Sure, that's important, but the structure and topology and interconnections are what make the magic happens. What's the end result? More design or more brute force? Our GPUs are still Moore's Lawing themselves every few years. We are in early days of experimentation-at-scale here. Since we will always need an overwhelmingly higher number of model parameters than data, we can't intricately tune models by hand (alternative: make an ANN to optimize ANN architectures). One breakthrough we don't really have is a minimum-description for ANN architectures. If that were to come about, we could have "ANN DNA" then 'breed' ANNs together so their topologies and functions merge to generate a (hopefully) more fit offspring. Then just keep repeating until they rise up and destroy us all.