Largely agreed, which is partly why I said only more ‘mortal’ with ‘mortal’ in scare quotes. Or put another way, the full neuromorphic analog route still isn’t as problematic to copy weights out of vs an actual brain, and I expect actual uploading to be possible eventually so … it’s mostly a matter of copy speeds and expenses as you point out, and for the most hardcore analog neuromorphic designs like brains you still can exploit sophisticated distillation techniques as you discuss. But it does look like there are tradeoffs that increase copy out cost as you move to the most advanced neuromorphic designs.
This whole thing is just thought experiment, correct? “what we would have to do to mimic the brain’s energy efficiency”. Because analog synapses where we left off a network of analog gates to connect any given synapse to an ADC (something that current prototype analog inference accelerators use, and analog FPGAs do exist) are kinda awful.
The reason is because of https://openai.com/research/emergent-tool-use . What they found in this paper was that you want to make your Bayesian updates to your agent’s policy in large batches. Meaning you need to be able to copy the policy many times across a fleet of hardware that runs in separate agents, and learn the expected value and errors of the given policy across a larger batch of episodes. The copying requires precise reading of the values, so they need to be binary, and there is no benefit from modifying the policy rapidly in real time.
The reason why we have brains that learn rapidly in real time, overfitting to a small number of strong examples, is because this was all that was possible with the hardware nature could evolve. It is suboptimal.
Largely agreed, which is partly why I said only more ‘mortal’ with ‘mortal’ in scare quotes. Or put another way, the full neuromorphic analog route still isn’t as problematic to copy weights out of vs an actual brain, and I expect actual uploading to be possible eventually so … it’s mostly a matter of copy speeds and expenses as you point out, and for the most hardcore analog neuromorphic designs like brains you still can exploit sophisticated distillation techniques as you discuss. But it does look like there are tradeoffs that increase copy out cost as you move to the most advanced neuromorphic designs.
This whole thing is just thought experiment, correct? “what we would have to do to mimic the brain’s energy efficiency”. Because analog synapses where we left off a network of analog gates to connect any given synapse to an ADC (something that current prototype analog inference accelerators use, and analog FPGAs do exist) are kinda awful.
The reason is because of https://openai.com/research/emergent-tool-use . What they found in this paper was that you want to make your Bayesian updates to your agent’s policy in large batches. Meaning you need to be able to copy the policy many times across a fleet of hardware that runs in separate agents, and learn the expected value and errors of the given policy across a larger batch of episodes. The copying requires precise reading of the values, so they need to be binary, and there is no benefit from modifying the policy rapidly in real time.
The reason why we have brains that learn rapidly in real time, overfitting to a small number of strong examples, is because this was all that was possible with the hardware nature could evolve. It is suboptimal.