I think it’s plausible we’ll be able to use deep learning to model a brain well before we understand how the brain works.
Record a ton of brain activity + human behaviour with a brain computer interface and wearable recording devises, respectively.
Train a model to predict future brain activity + behaviour, conditioned on past brain activity + behaviour.
Continue running the model by feeding it its own predicted brain activity + behaviour as the conditioning data for future predictions.
Congratulations, you now have an emulated human. No need to understand any brain algorithms. You just need tons of brain + behaviour data and compute.
I think this will be possible before non brain-based AGI because current AI research indicates it’s easier to train a model by distilling/imitating an already trained model than it is to train from scratch, e.g., DistilBERT: https://arxiv.org/abs/1910.01108v4
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.
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. 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, 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.
I think it’s plausible we’ll be able to use deep learning to model a brain well before we understand how the brain works.
Record a ton of brain activity + human behaviour with a brain computer interface and wearable recording devises, respectively.
Train a model to predict future brain activity + behaviour, conditioned on past brain activity + behaviour.
Continue running the model by feeding it its own predicted brain activity + behaviour as the conditioning data for future predictions.
Congratulations, you now have an emulated human. No need to understand any brain algorithms. You just need tons of brain + behaviour data and compute. I think this will be possible before non brain-based AGI because current AI research indicates it’s easier to train a model by distilling/imitating an already trained model than it is to train from scratch, e.g., DistilBERT: https://arxiv.org/abs/1910.01108v4
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.
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. 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.
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, 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.