I've been trying to brand this paradigm as "brain imitation learning" but it hasn't caught on. The research still continues and we're seeing exponential increases in neuron recording capabilities and DL models are doing ever better in cracking open the human brain's neural code*, but this in-between approach is still mostly ignored.

* so IMO the only reason to be less interested in it than a few years ago is if you think pure DL scaling/progress has gone so fast that it's outpacing even that, which is reasonable but given the imponderables here and the potential for sudden plateaus in scaling or pure DL progress, I think people should still be keeping more of an eye on brain imitation learning than they do.

Congratulations, you now have an emulated human.

I don't think the thing you're talking about is "an emulated human", at least not in the WBE sense of the term.

I think the two reasons people are interested in WBE is:

• Digital immortality—the WBE of my brain is me, with all my hopes and aspirations and memories, including the memory of how I felt when Pat kissed me in fourth grade etc. etc.
• Safety—the WBE of a particular human will have the same motivations and capabilities as that human. If the human is my friend and I trust them to do the right thing, then I trust the WBE too.

What you're talking about wouldn't have either of those benefits, or at least not much.

I wasn't recording my brain when Pat kissed me in fourth grade, and I haven't recalled that memory since then, so there's no way that an emulation could have access to that memory just based on a database of real-time brain recording. The only way to get that memory is to slice up my brain and look at the synapses under a microscope. (Made-up example of course, nobody in fourth grade would have dreamed of kissing me.)

Also, I believe that human motivation—so important for safety—heavily involves autonomic inputs and outputs (pain, hunger, circulating hormone levels, vasoconstriction, etc. etc.)—and in this domain your proposed system wouldn't be able to measure most of the inputs, and wouldn't be able to measure most of the outputs, and probably wouldn't be able to measure most of the brain processing that goes on between the inputs and outputs either! (Well, it depends on exactly what the brain-computer interface type is, but autonomic processing tends to happen in deeply-buried hard-to-measure brain areas like the insular and cingulate cortex, brainstem, and even inside the spinal cord). Maybe you'll say "that's fine, we'll measure a subset of inputs and a subset of outputs and a subset of brain processing, and then we'll fill in the gaps by learning". And, well, that's not unreasonable. I mean, by the same token, GPT-3 had only a tiny subset of human inputs and outputs, and zero direct measurements of brain processing, and yet GPT-3 arguably learned an implicit model of brain processing. Not a perfect one by any means, but something.

So anyway, one can make an argument that there are safety benefits of human imitation learning (versus, say, training by pure RL in a virtual environment), and then one can add that there are additional safety benefits when we go to "human imitation learning which is souped-up via throwing EEG data or whatever into the model prediction target". I'm open-minded to that kind of argument and have talked about vaguely similar things myself [LW · GW]. But I still think that's a different sort of argument then the WBE safety argument above, the argument that the WBE of a trustworthy human is automatically trustworthy because it's the same person. In particular, the imitation-learning safety argument is much less airtight I think. It requires additional careful thought about distributional shifts and so on.

So my point is: I don't think what you're talking about should be called "emulations", and even if you're right, I don't think it would undermine the point of this post, which is that WBE is unlikely to happen before non-WBE AGI even if we wanted it to.

I think this will be possible

So now we move on to whether I believe your scenario. Well it's hard to be confident, but I don't currently put much weight on it. I figure, option 1 is: "deep neural nets do in fact scale to AGI". In that case, your argument is that EEG data or whatever will reduce training time/data because it's like model distillation. I would say "sure, maybe model distillation helps, other things equal … but on the other hand we have 100,000 years of YouTube videos to train on, and a comparatively very expensive and infinitesimal amount of EEG data". So I expect that all things considered, future engineers would just go with the YouTube option. Option 2 is: "deep neural nets do not in fact scale to AGI"—they're the wrong kind of algorithm for AGI. (I've made this argument [LW · GW], although I mean who knows, I don't feel that strongly.) In that case adding EEG data as an additional prediction target wouldn't help.

Accidentally creating AGI seems unlikely

Sorry if I was unclear; my intended parsing was "accidentally (creating catastrophically-out-of-control AGIs)". In other words, I don't expect that people will try to create catastrophically-out-of-control AGIs. Therefore, if they create catastrophically-out-of-control AGIs, it would be by accident.

Emulating brains in order to increase capability is currently...an idea.

I think you're overly confident that WBE would be irrelevant to the timeline of AGI capabilities research, but I think it's a moot point anyway, since I don't expect WBE before AGI, so I'm not really interested in arguing about it. :-P

Practically, progress requires doing both, i.e. better equipment to create and measure electricity is needed to understand it better, which helps understand how to direct, contain, and generate it better, etc.

I do in fact agree with you, but I think it's not as clear-cut as you make it out to be in the WBE case, I think it takes a more detailed argument where reasonable people could disagree. In particular, there's an argument on the other side that says "implementation-level understanding" is a different thing from "algorithm-level understanding", and you only need the first one for WBE, not the second one.

So for example, if I give you a binary executable "factor.exe" that solves hard factorization problems, you would be able to run it on a computer much more easily than you could decompile it and understand how the algorithm works.

This example goes through because we have perfect implementation-level understanding about running executables on CPUs. In the brain case, Randal is arguing (and I agree) that we don't have perfect implementation-level understanding, and we won't get it by just studying the implementation level. The implementation-level is just very complicated—much more complicated than "dendrites are inputs, axons are outputs" etc. And it could involve subtle things that we won't actually go measure and simulate unless we know that we need to go looking for them. So in practice, the only way to make up for our expected deficiencies in implementation-level understanding is to also have good algorithm-level understanding.

Fixed! Thanks!