3 ms·

ah yes, it's true we can keep adding on processors and memory until we go beyond what's in the brain (i think currently deep mind has ~ 11 billion neurons to ou

by modzu 8y ago

ah yes, it's true we can keep adding on processors and memory until we go beyond what's in the brain (i think currently deep mind has ~ 11 billion neurons to our 100 billion), and indeed so far it's helping, but my point is that it's not at all clear that "more = better". there is more complexity involved than simply scale. the machine could become schizophrenic for exmaple (seriously).

deleted 8y ago
[deleted]
riku_iki 8y ago
> in the brain you focus only on single brain, when really we need to look at many trillions brains and experiments with complex reward function(real world): billion years of evolution * billions of various creatures born and died within every year. All this giant sequence of experiments converged to current human brain. In ML terms: to achieve result similar to human brain we need to run that many hyperparameter optimization trials. Or find some better shortcuts than human brain biological structure.
vl 8y ago
This is not a best way to compare it: brain neurons do orders of magnitudes less operations per second. Current Google's TPU clusters are 10 petaflops, with new ones being 1 exoflops. We can estimate 100Hz frequency for brain, so 100 * 10^11 = 10^13 "neuron operations". Let's estimate 1000 flops to simulate single neuron. So it gives us 10^16 flops for brain (aka 10 petaplops). I.e. Google's new deployments are more powerful than single brain.
- modzu 8y ago
  and what says that is the best way to compare it? neural networks don't model anything but the electrical synapse. there's much more going on (cf: chemical synapse). also speed isn't the holy grail: too fast in a human brain and you get seizures. why? it could be that as these systems scale up magical things happen. but it's a conjecture -- something we will discover, and not something we know.

3 ms·

ah yes, it's true we can keep adding on processors and memory until we go beyond what's in the brain (i think currently deep mind has ~ 11 billion neurons to ou

by modzu 8y ago

deleted 8y ago
[deleted]
riku_iki 8y ago
> in the brain you focus only on single brain, when really we need to look at many trillions brains and experiments with complex reward function(real world): billion years of evolution * billions of various creatures born and died within every year. All this giant sequence of experiments converged to current human brain. In ML terms: to achieve result similar to human brain we need to run that many hyperparameter optimization trials. Or find some better shortcuts than human brain biological structure.
vl 8y ago
This is not a best way to compare it: brain neurons do orders of magnitudes less operations per second. Current Google's TPU clusters are 10 petaflops, with new ones being 1 exoflops. We can estimate 100Hz frequency for brain, so 100 * 10^11 = 10^13 "neuron operations". Let's estimate 1000 flops to simulate single neuron. So it gives us 10^16 flops for brain (aka 10 petaplops). I.e. Google's new deployments are more powerful than single brain.
- modzu 8y ago
  and what says that is the best way to compare it? neural networks don't model anything but the electrical synapse. there's much more going on (cf: chemical synapse). also speed isn't the holy grail: too fast in a human brain and you get seizures. why? it could be that as these systems scale up magical things happen. but it's a conjecture -- something we will discover, and not something we know.