A key thing here, as far as I can tell hinton is most driven to understand the brain, not so much to create superintelligent machines. He’s making intelligent machines in order to build models of neuroscience; the generative perspective on intelligence.
re: brain uploading, I think the idea is that you’d distill into and out of the volumetric compute system by reading out its’ weights’ implications on its outputs, whether it’s a bio brain or a silicon one, instead of uploading its contents directly using lossless readout of the weights. What he claimed was specifically that you can’t upload a high error rate brain, not that you can’t make anything that duplicates the brain’s behavior. In my view, if tightly linked during online learning, incremental distillation will cause the physical systems to operate as one conscious entity and still counts as uploading for my purposes as a brain myself. You could say that distillation is where you have kids but also replicate at the connectome level not just the genome level; mitosis of a brain, perhaps. He’s saying you can’t make an exact copy of a system that does hardware-level error correction, because a system that learns to make use of its own errors as part of normal operation won’t perform the same way if its internal state is what is copied, and thus any structural copy you make will lose tiny details that add up to a replica that is inexact.
I don’t think he’s totally barking up the wrong tree there, but I think it’ll also be fairly possible to copy and relearn. The copy won’t be an exact replica but whatever, who cares, if you then also do the teacher-student distillation he’s talking about then you end up with an even better replica than if you’d just distillled. well, maybe, anyway.
re: efficiency and cost—I think the chip will, for the time being, probably still be made of silicon or at least standard chip components even if we move off silicon. There are neuromorphic chip groups where a lot of worker and chip fab time and energy are directed at this problem. The benefit is in completely avoiding any IO wiring, which is most of the cost of current massively parallel chips, not avoiding use of transistors. That said, more generally, I imagine you’re more or less correct about the tradeoff, and that mortality is not the key to why this approach could produce significant intelligence—only compute-in-memory is needed to get the benefits he imagines, and the algorithms he comes up with intended for mortal compute should also work for compute-in-memory. But, algorithms that scale down to chips where computation makes no attempt at all to be error free or structured in ways that generalize to other chips is both a promising area of research for increasing capability per watt, the only true metric of intelligence, and also should scale up to bulkier circuitry as well.
An issue I see here, though, is that this approach seems much more likely to be able to exceed human intelligence per watt in the near term, which is a key metric that gives us a guideline about the foom curve and when to expect biological brains to be at significant risk of acute disempowerment. the question we need to answer from a safety perspective is still how to end up in a co-protective equilibrium with artificial beings, but now with the added modifier that their brains may only be imperfectly copyable.
A key thing here, as far as I can tell hinton is most driven to understand the brain, not so much to create superintelligent machines. He’s making intelligent machines in order to build models of neuroscience; the generative perspective on intelligence.
re: brain uploading, I think the idea is that you’d distill into and out of the volumetric compute system by reading out its’ weights’ implications on its outputs, whether it’s a bio brain or a silicon one, instead of uploading its contents directly using lossless readout of the weights. What he claimed was specifically that you can’t upload a high error rate brain, not that you can’t make anything that duplicates the brain’s behavior. In my view, if tightly linked during online learning, incremental distillation will cause the physical systems to operate as one conscious entity and still counts as uploading for my purposes as a brain myself. You could say that distillation is where you have kids but also replicate at the connectome level not just the genome level; mitosis of a brain, perhaps. He’s saying you can’t make an exact copy of a system that does hardware-level error correction, because a system that learns to make use of its own errors as part of normal operation won’t perform the same way if its internal state is what is copied, and thus any structural copy you make will lose tiny details that add up to a replica that is inexact.
I don’t think he’s totally barking up the wrong tree there, but I think it’ll also be fairly possible to copy and relearn. The copy won’t be an exact replica but whatever, who cares, if you then also do the teacher-student distillation he’s talking about then you end up with an even better replica than if you’d just distillled. well, maybe, anyway.
re: efficiency and cost—I think the chip will, for the time being, probably still be made of silicon or at least standard chip components even if we move off silicon. There are neuromorphic chip groups where a lot of worker and chip fab time and energy are directed at this problem. The benefit is in completely avoiding any IO wiring, which is most of the cost of current massively parallel chips, not avoiding use of transistors. That said, more generally, I imagine you’re more or less correct about the tradeoff, and that mortality is not the key to why this approach could produce significant intelligence—only compute-in-memory is needed to get the benefits he imagines, and the algorithms he comes up with intended for mortal compute should also work for compute-in-memory. But, algorithms that scale down to chips where computation makes no attempt at all to be error free or structured in ways that generalize to other chips is both a promising area of research for increasing capability per watt, the only true metric of intelligence, and also should scale up to bulkier circuitry as well.
An issue I see here, though, is that this approach seems much more likely to be able to exceed human intelligence per watt in the near term, which is a key metric that gives us a guideline about the foom curve and when to expect biological brains to be at significant risk of acute disempowerment. the question we need to answer from a safety perspective is still how to end up in a co-protective equilibrium with artificial beings, but now with the added modifier that their brains may only be imperfectly copyable.