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I don't think it matters so much how the brain is made, what matters is the training data. And we obtain data by searching. Search is a great concept, it covers

I don't think it matters so much how the brain is made, what matters is the training data. And we obtain data by searching. Search is a great concept, it covers evolution, intelligence and creativity, it's also social. Search is discrete, recursive, combinatorial and based on some kind of language (DNA, or words, or just math/code). Searching the environment provides the data brain is trained on. I don't believe we can understand the brain in isolation without its data engine and the problem space where it develops. Neural nets showed that given a dataset, you can obtain similar results with very different architectures, like transformer and diffusion models, or transformer vs Mamba. The essential ingredient is data, architecture only needs to pass some minimal bar for learning. Studying just the brain misses the essential - we are search processes, the whole life is search for optimal actions, and evolution itself is search for environment fitness. These search processes made us what we are.

advael 2y ago
What in the world Most "diffusion models" use similar VAE to transformer backbone architectures. Diffusion isn't an architecture, it's a problem framing As for the rest of this, I'm torn between liking the poetry of it and pointing out that this is kind of that thing where you say something like it's supposed to be a mind-blowing insight when it's well-known and pretty obvious. Most people familiar with learning theory already understand learning algorithms of any kind as a subset of probabilistic search algorithms with properties that make them responsive to data. The idea that the structure of the information processing system doesn't matter and there's just this general factor of learning capacity a thing has is... not well supported by the way in which research has progressed in the entire period of time when this has been relevant to most people? Sure, in theory any neural network is a general function approximator and could in theory learn any function it's complex enough to represent. Also, we can arrive at the solution to any computable problem by representing it as a number and guessing random numbers until we can verify a solution. Learning algorithms can almost be defined as attempts to do better search via structured empiricism than can be done with the assumption that structure doesn't matter. Like, sometimes multiple things work, sure. That doesn't mean it's arbitrary TL;DR: Of course learning is a kind of search, but discovering structures that are good at learning is the whole game
- Xcelerate 2y ago
  Yeah, I really don’t understand this recently popular viewpoint that the algorithm doesn’t matter, just how much data you throw at it. It doesn’t seem to be based on anything more than wishful thinking. One can apply Hutter search to solve just about any problem conceivable given the data and guess what—you’ll approach the optimal solution! The only downside is that this process will take more time than available in our physical universe. I think people forget the time factor and how the entire field of computational complexity theory arose because the meta problem is not that we can’t solve the problem—it’s that we can’t solve it quickly enough on a timescale that matters to humans. Current NN architectures are missing something very fundamental related to the efficiency of problem solving, and I really don’t see how throwing more data at them is going to magically convert an EXPTIME algorithm into a PTIME one. (I’m not saying NNs are EXPTIME; I’m saying that they are incapable of solving entire classes of problems that have both PTIME and EXPTIME solutions, as the NN architecture is not able to “discover” PTIME solutions, thus rendering them incapable of solving those classes of problems in any practical sense).
  - advael 2y ago
    Also, one of the major classes of problem that gets solved and we view as "progress" in machine learning is framing problems. Like we couldn't define "draw a good picture" in a way we could actually assess well, GANs and Diffusion turn out to be good ways to phrase problems like that. In the former case, it creates a way to define the problem as "make something indistinguishable from an example pulled from this dataset" and in the latter case, "I've randomized some of these pixels, undo that based on the description" The idea of "efficiency" and "progress" is this post-hoc rationalization that people who never understood the problem, pay people to understand the problem, apply to problems once they have a working solution in hand. It's a model that is inherently as dumb as a model can be, and the assumption it makes is that there is some general factor of progress on hard problems that can be dialed up and down. Sure, you can pay more scientists and probabilistically increase the rate at which problems are solved, but you can't predict how long it will take, how much failure it will involve, whether a particular scientist will solve a particular problem at all, whether that problem is even solvable in principle sometimes. Businesspeople and investors like models where you put in money and you get out growth at a predictable rate with a controllable timescale, and if this doesn't work you just kick it harder, and this ill fits most frontier research. Hell, it ill suits a lot of regular work.
calepayson 2y ago
>I don't think it matters so much how the brain is made, what matters is the training data. I agree that training data is hugely important but I think it does matter how the brain is made. Structures in the brain are remarkably well preserved between species. Despite the fact that evolution loves to try different methods, if it can get away with it. > Searching the environment provides the data brain is trained on. I don't believe we can understand the brain in isolation without its data engine and the problem space where it develops. I completely agree and suspect we might be on the same page. What I find most compelling about the idea of Darwin Machines is the fact that it relies on evolution. In my opinion, true Dawkinsian evolution, is the most efficient search algorithm. I'd love to hear you go deeper on what you mean by data engine and problem space. To (possibly) abuse those terms, I think evolution is the data engine. The problem space is fun and I love David Eagleman's description of the brain as sitting in a warm bath in a dark room trying to figure out what to do with all these electric shocks. > Neural nets showed that given a dataset, you can obtain similar results with very different architectures, like transformer and diffusion models, or transformer vs Mamba. The essential ingredient is data, architecture only needs to pass some minimal bar for learning. My understanding of neural nets, and please correct me if I'm wrong, is that they solve system-one thinking, intuition. As of yet, they haven't been able to do much more than produce an average of their training data (which is incredible). With a brute force approach they can innovate in constrained environments, e.g. move 37 (or so I'm told, I haven't played go :)). I haven't seen evidence that they might be able to innovate in open-ended environments. In other words, there's no suggestion they can do system-two thinking where time spent on a problem correlates with the quality of the answer. > Studying just the brain misses the essential - we are search processes, the whole life is search for optimal actions, and evolution itself is search for environment fitness. I completely agree. I even suspect that, in a few years, we'll see "life" and "intelligence" as synonymous concepts, just implemented in different mediums. At the same time, studying those mediums can be a blast.

advael 2y ago

What in the world Most "diffusion models" use similar VAE to transformer backbone architectures. Diffusion isn't an architecture, it's a problem framing As for the rest of this, I'm torn between liking the poetry of it and pointing out that this is kind of that thing where you say something like it's supposed to be a mind-blowing insight when it's well-known and pretty obvious. Most people familiar with learning theory already understand learning algorithms of any kind as a subset of probabilistic search algorithms with properties that make them responsive to data. The idea that the structure of the information processing system doesn't matter and there's just this general factor of learning capacity a thing has is... not well supported by the way in which research has progressed in the entire period of time when this has been relevant to most people? Sure, in theory any neural network is a general function approximator and could in theory learn any function it's complex enough to represent. Also, we can arrive at the solution to any computable problem by representing it as a number and guessing random numbers until we can verify a solution. Learning algorithms can almost be defined as attempts to do better search via structured empiricism than can be done with the assumption that structure doesn't matter. Like, sometimes multiple things work, sure. That doesn't mean it's arbitrary TL;DR: Of course learning is a kind of search, but discovering structures that are good at learning is the whole game

Xcelerate 2y ago
Yeah, I really don’t understand this recently popular viewpoint that the algorithm doesn’t matter, just how much data you throw at it. It doesn’t seem to be based on anything more than wishful thinking. One can apply Hutter search to solve just about any problem conceivable given the data and guess what—you’ll approach the optimal solution! The only downside is that this process will take more time than available in our physical universe. I think people forget the time factor and how the entire field of computational complexity theory arose because the meta problem is not that we can’t solve the problem—it’s that we can’t solve it quickly enough on a timescale that matters to humans. Current NN architectures are missing something very fundamental related to the efficiency of problem solving, and I really don’t see how throwing more data at them is going to magically convert an EXPTIME algorithm into a PTIME one. (I’m not saying NNs are EXPTIME; I’m saying that they are incapable of solving entire classes of problems that have both PTIME and EXPTIME solutions, as the NN architecture is not able to “discover” PTIME solutions, thus rendering them incapable of solving those classes of problems in any practical sense).
- advael 2y ago
  Also, one of the major classes of problem that gets solved and we view as "progress" in machine learning is framing problems. Like we couldn't define "draw a good picture" in a way we could actually assess well, GANs and Diffusion turn out to be good ways to phrase problems like that. In the former case, it creates a way to define the problem as "make something indistinguishable from an example pulled from this dataset" and in the latter case, "I've randomized some of these pixels, undo that based on the description" The idea of "efficiency" and "progress" is this post-hoc rationalization that people who never understood the problem, pay people to understand the problem, apply to problems once they have a working solution in hand. It's a model that is inherently as dumb as a model can be, and the assumption it makes is that there is some general factor of progress on hard problems that can be dialed up and down. Sure, you can pay more scientists and probabilistically increase the rate at which problems are solved, but you can't predict how long it will take, how much failure it will involve, whether a particular scientist will solve a particular problem at all, whether that problem is even solvable in principle sometimes. Businesspeople and investors like models where you put in money and you get out growth at a predictable rate with a controllable timescale, and if this doesn't work you just kick it harder, and this ill fits most frontier research. Hell, it ill suits a lot of regular work.

calepayson 2y ago

>I don't think it matters so much how the brain is made, what matters is the training data. I agree that training data is hugely important but I think it does matter how the brain is made. Structures in the brain are remarkably well preserved between species. Despite the fact that evolution loves to try different methods, if it can get away with it. > Searching the environment provides the data brain is trained on. I don't believe we can understand the brain in isolation without its data engine and the problem space where it develops. I completely agree and suspect we might be on the same page. What I find most compelling about the idea of Darwin Machines is the fact that it relies on evolution. In my opinion, true Dawkinsian evolution, is the most efficient search algorithm. I'd love to hear you go deeper on what you mean by data engine and problem space. To (possibly) abuse those terms, I think evolution is the data engine. The problem space is fun and I love David Eagleman's description of the brain as sitting in a warm bath in a dark room trying to figure out what to do with all these electric shocks. > Neural nets showed that given a dataset, you can obtain similar results with very different architectures, like transformer and diffusion models, or transformer vs Mamba. The essential ingredient is data, architecture only needs to pass some minimal bar for learning. My understanding of neural nets, and please correct me if I'm wrong, is that they solve system-one thinking, intuition. As of yet, they haven't been able to do much more than produce an average of their training data (which is incredible). With a brute force approach they can innovate in constrained environments, e.g. move 37 (or so I'm told, I haven't played go :)). I haven't seen evidence that they might be able to innovate in open-ended environments. In other words, there's no suggestion they can do system-two thinking where time spent on a problem correlates with the quality of the answer. > Studying just the brain misses the essential - we are search processes, the whole life is search for optimal actions, and evolution itself is search for environment fitness. I completely agree. I even suspect that, in a few years, we'll see "life" and "intelligence" as synonymous concepts, just implemented in different mediums. At the same time, studying those mediums can be a blast.