I’m not sure exactly what you’re trying to learn here, or what debate you’re trying to resolve. (Do you have a reference?)
If almost all the complexity is in architecture, you can have fast takeoff because it doesn’t work well until the pieces are all in place; or you can have slow takeoff in the opposite case. If almost all the complexity is in learned content, you can have fast takeoff because there’s 50 million books and 100,000 years of YouTube videos and the AI can deeply understand all of them in 24 hours; or you can have slow takeoff because, for example, maybe the fastest supercomputers can just barely run the algorithm at all, and the algorithm gets slower and slower as it learns more, and eventually grinds to a halt, or something like that.
If an algorithm uses data structures that are specifically suited to doing Task X, and a different set of data structures that are suited to Task Y, would you call that two units of content or two units of architecture?
(I personally do not believe that intelligence requires a Swiss-army-knife of many different algorithms, see here, but this is certainly a topic on which reasonable people disagree.)
I’m not sure exactly what you’re trying to learn here, or what debate you’re trying to resolve. (Do you have a reference?)
I’m not entirely sure what I’m trying to learn here (which is part of what I was trying to express with the final paragraph of my question); this just seemed like a natural question to ask as I started thinking more about AI takeoff.
In “I Heart CYC”, Robin Hanson writes: “So we need to explicitly code knowledge by hand until we have enough to build systems effective at asking questions, reading, and learning for themselves. Prior AI researchers were too comfortable starting every project over from scratch; they needed to join to create larger integrated knowledge bases.”
It sounds like he expects early AGI systems to have lots of hand-coded knowledge, i.e. the minimum number of bits needed to specify a seed AI is large compared to what Eliezer Yudkowsky expects. (I wish people gave numbers for this so it’s clear whether there really is a disagreement.) It also sounds like Robin Hanson expects progress in AI capabilities to come from piling on more hand-coded content.
If ML source code is small and isn’t growing in size, that seems like evidence against Hanson’s view.
If ML source code is much smaller than the human genome, I can do a better job of visualizing the kind of AI development trajectory that Robin Hanson expects, where we stick in a bunch of content and share content among AI systems. If ML source code is already quite large, then it’s harder for me to visualize this (in this case, it seems like we don’t know what we’re doing, and progress will come from better understanding).
If the human genome is small, I think that makes a discontinuity in capabilities more likely. When I try to visualize where progress comes from in this case, it seems like it would come from a small number of insights. We can take some extreme cases: if we knew that the code for a seed AGI could fit in a 500-line Python program (I don’t know if anybody expects this), a FOOM seems more likely (there’s just less surface area for making lots of small improvements). Whereas if I knew that the smallest program for a seed AGI required gigabytes of source code, I feel like progress would come in smaller pieces.
If an algorithm uses data structures that are specifically suited to doing Task X, and a different set of data structures that are suited to Task Y, would you call that two units of content or two units of architecture?
I’m not sure. The content/architecture split doesn’t seem clean to me, and I haven’t seen anyone give a clear definition. Specialized data structures seems like a good example of something that’s in between.
It sounds like your question should really be more like how many programmer-hours go into putting domain-specific content / capabilities into an AI. (You can disagree.) If it’s very high, then it’s the Robin-Hanson-world where different companies make AI-for-domain-X, AI-for-domain-Y, etc., and they trade and collaborate. If it’s very low, then it’s more plausible that someone will have a good idea and Bam, they have an AGI. (Although it might still require huge amounts of compute.)
If so, I don’t think the information content of the weights of a trained model is relevant. The weights are learned automatically. Changing the code from num_hidden_layers = 10 to num_hidden_layers = 100 is not 10× the programmer effort. (It may or may not require more compute, and it may or may not require more labeled examples, and it may or may not require more hyperparameter tuning, but those are all different things, and in no case is there any reason to think it’s a factor of 10, except maybe some aspects of compute.)
I don’t think the size of the PyTorch codebase is relevant either.
I agree that the size of the human genome is relevant, as long as we all keep in mind that it’s a massive upper bound, because perhaps a vanishingly small fraction of that is “domain-specific content / capabilities”. Even within the brain, you have to synthesize tons of different proteins, control the concentrations of tons of chemicals, etc. etc.
I think the core of your question is generalizability. If you have AlphaStar but want to control a robot instead, how much extra code do you need to write? Do insights in computer vision help with NLP and vice-versa? That kind of stuff. I think generalizability has been pretty high in AI, although maybe that statement is so vague as to be vacuous. I’m thinking, for example, it’s not like we have “BatchNorm for machine translation” and “BatchNorm for image segmentation” etc. It’s the same BatchNorm.
On the brain side, I’m a big believer in the theory that the neocortex has one algorithm which simultaneously does planning, action, classification, prediction, etc. (The merging of action and understanding in particular is explained in my post here, see also Planning By Probabilistic Inference.) So that helps with generalizability. And I already mentioned my post on cortical uniformity. I think a programmer who knows the core neocortical algorithm and wants to then imitate the whole neocortex would mainly need (1) a database of “innate” region-to-region connections, organized by connection type (feedforward, feedback, hormone receptors) and structure (2D array of connections vs 1D, etc.), (2) a database of region-specific hyperparameters, especially when the region should lock itself down to prevent further learning (“sensitive periods”). Assuming that’s the right starting point, I don’t have a great sense for how many bits of data this is, but I think the information is out there in the developmental neuroscience literature. My wild guess right now would be on the order of a few KB, but with very low confidence. It’s something I want to look into more when I get a chance. Note also that the would-be AGI engineer can potentially just figure out those few KB from the neuroscience literature, rather than discovering it in a more laborious way.
Oh, you also probably need code for certain non-neocortex functions like flagging human speech sounds as important to attend to etc. I suspect that that particular example is about as straightforward as it sounds, but there might be other things that are hard to do, or where it’s not clear what needs to be done. Of course, for an aligned AGI, there could potentially be a lot of work required to sculpt the reward function.
I’m not sure exactly what you’re trying to learn here, or what debate you’re trying to resolve. (Do you have a reference?)
If almost all the complexity is in architecture, you can have fast takeoff because it doesn’t work well until the pieces are all in place; or you can have slow takeoff in the opposite case. If almost all the complexity is in learned content, you can have fast takeoff because there’s 50 million books and 100,000 years of YouTube videos and the AI can deeply understand all of them in 24 hours; or you can have slow takeoff because, for example, maybe the fastest supercomputers can just barely run the algorithm at all, and the algorithm gets slower and slower as it learns more, and eventually grinds to a halt, or something like that.
If an algorithm uses data structures that are specifically suited to doing Task X, and a different set of data structures that are suited to Task Y, would you call that two units of content or two units of architecture?
(I personally do not believe that intelligence requires a Swiss-army-knife of many different algorithms, see here, but this is certainly a topic on which reasonable people disagree.)
I’m not entirely sure what I’m trying to learn here (which is part of what I was trying to express with the final paragraph of my question); this just seemed like a natural question to ask as I started thinking more about AI takeoff.
In “I Heart CYC”, Robin Hanson writes: “So we need to explicitly code knowledge by hand until we have enough to build systems effective at asking questions, reading, and learning for themselves. Prior AI researchers were too comfortable starting every project over from scratch; they needed to join to create larger integrated knowledge bases.”
It sounds like he expects early AGI systems to have lots of hand-coded knowledge, i.e. the minimum number of bits needed to specify a seed AI is large compared to what Eliezer Yudkowsky expects. (I wish people gave numbers for this so it’s clear whether there really is a disagreement.) It also sounds like Robin Hanson expects progress in AI capabilities to come from piling on more hand-coded content.
If ML source code is small and isn’t growing in size, that seems like evidence against Hanson’s view.
If ML source code is much smaller than the human genome, I can do a better job of visualizing the kind of AI development trajectory that Robin Hanson expects, where we stick in a bunch of content and share content among AI systems. If ML source code is already quite large, then it’s harder for me to visualize this (in this case, it seems like we don’t know what we’re doing, and progress will come from better understanding).
If the human genome is small, I think that makes a discontinuity in capabilities more likely. When I try to visualize where progress comes from in this case, it seems like it would come from a small number of insights. We can take some extreme cases: if we knew that the code for a seed AGI could fit in a 500-line Python program (I don’t know if anybody expects this), a FOOM seems more likely (there’s just less surface area for making lots of small improvements). Whereas if I knew that the smallest program for a seed AGI required gigabytes of source code, I feel like progress would come in smaller pieces.
I’m not sure. The content/architecture split doesn’t seem clean to me, and I haven’t seen anyone give a clear definition. Specialized data structures seems like a good example of something that’s in between.
OK, I think that helps.
It sounds like your question should really be more like how many programmer-hours go into putting domain-specific content / capabilities into an AI. (You can disagree.) If it’s very high, then it’s the Robin-Hanson-world where different companies make AI-for-domain-X, AI-for-domain-Y, etc., and they trade and collaborate. If it’s very low, then it’s more plausible that someone will have a good idea and Bam, they have an AGI. (Although it might still require huge amounts of compute.)
If so, I don’t think the information content of the weights of a trained model is relevant. The weights are learned automatically. Changing the code from
num_hidden_layers = 10tonum_hidden_layers = 100is not 10× the programmer effort. (It may or may not require more compute, and it may or may not require more labeled examples, and it may or may not require more hyperparameter tuning, but those are all different things, and in no case is there any reason to think it’s a factor of 10, except maybe some aspects of compute.)I don’t think the size of the PyTorch codebase is relevant either.
I agree that the size of the human genome is relevant, as long as we all keep in mind that it’s a massive upper bound, because perhaps a vanishingly small fraction of that is “domain-specific content / capabilities”. Even within the brain, you have to synthesize tons of different proteins, control the concentrations of tons of chemicals, etc. etc.
I think the core of your question is generalizability. If you have AlphaStar but want to control a robot instead, how much extra code do you need to write? Do insights in computer vision help with NLP and vice-versa? That kind of stuff. I think generalizability has been pretty high in AI, although maybe that statement is so vague as to be vacuous. I’m thinking, for example, it’s not like we have “BatchNorm for machine translation” and “BatchNorm for image segmentation” etc. It’s the same BatchNorm.
On the brain side, I’m a big believer in the theory that the neocortex has one algorithm which simultaneously does planning, action, classification, prediction, etc. (The merging of action and understanding in particular is explained in my post here, see also Planning By Probabilistic Inference.) So that helps with generalizability. And I already mentioned my post on cortical uniformity. I think a programmer who knows the core neocortical algorithm and wants to then imitate the whole neocortex would mainly need (1) a database of “innate” region-to-region connections, organized by connection type (feedforward, feedback, hormone receptors) and structure (2D array of connections vs 1D, etc.), (2) a database of region-specific hyperparameters, especially when the region should lock itself down to prevent further learning (“sensitive periods”). Assuming that’s the right starting point, I don’t have a great sense for how many bits of data this is, but I think the information is out there in the developmental neuroscience literature. My wild guess right now would be on the order of a few KB, but with very low confidence. It’s something I want to look into more when I get a chance. Note also that the would-be AGI engineer can potentially just figure out those few KB from the neuroscience literature, rather than discovering it in a more laborious way.
Oh, you also probably need code for certain non-neocortex functions like flagging human speech sounds as important to attend to etc. I suspect that that particular example is about as straightforward as it sounds, but there might be other things that are hard to do, or where it’s not clear what needs to be done. Of course, for an aligned AGI, there could potentially be a lot of work required to sculpt the reward function.
Just thinking out loud :)