4 ms·

Would your 1000 core CPU do well on neuroevolution?

by ralphc 1y ago

zackmorris 1y ago
Thank you, believe it or not I hadn't heard that word so it filled in a piece of the puzzle for me. https://en.wikipedia.org/wiki/Neuroevolution https://en.wikipedia.org/wiki/Neuroevolution https://medium.com/@roopal.tatiwar20/neuroevolution-evolving-neural-network-with-genetic-algorithms-8ca2165ad04c https://medium.com/@roopal.tatiwar20/neuroevolution-evolving... That was the state of the art when I got my ECE degree in 1999. They were using genetic algorithms (GAs) to evolve initial weights for neural net (NN) gradient descent right before the Dot Bomb. Then pretty much all R&D was cut/abandoned overnight, and the 2000s went to offshoring jobs and shuttering factories which led to the Housing Bubble popping. IMHO that set AI research back at least 10 years, maybe 20, at least in the US. I know it derailed my career dreams. I feel that GPUs by their SIMD nature do poorly with a large number of concurrent processes. For example, a population of GA agents running lisp with each instruction of the intermediate code (icode) tree encoded as bits in a bitfield which evolves using techniques like mutation, crossover and sexual reproduction. Some of those bits represent conditional branching. Ideally we'd want the opposite of a GPU, more like a transputer, on the order of thousands of independent arithmetic logic units (ALUs) with their own local memories, and perhaps able to use custom instructions. That way each lisp tree can execute concurrently in isolation. There may be branch-avoidance transformations from shaders that could help with this, to transpile the von Neumann SISD and MIMD code we're used to into the narrower SIMD so it can run on GPUs. Companies like Nvidia aren't touching this for some reason, and I don't know if it's just a blind spot or deliberate. This is one reason why I wanted FPGAs to take off, since they'd make great transputers. Unfortunately they stagnated due to proprietary compilers and a lack of profitable applications. Today it looks like microcontroller units (MCUs) might take their place, but they're still at least 4 orders of magnitude or more too small and 100 times too slow to be cost-effective. Imagine having a 1 GHz CPU that can run 1000 concurrent UNIX processes running lisp that only communicate through sockets. It's easy to see how mutating them is trivial, then running them as unit tests to see which ones pass is also straightforward. Erlang and Go figured that out years ago. It's so easy in fact to "just come up with the answer" this way, that I think this technique has been suppressed. With that foundation and removing the element of time, it's easy to see that this could be a drop-in replacement for gradient descent. Then the work shifts to curating large data and training sets. I suspect that this is the real work of AI research, and that the learning technique doesn't matter so much. I think of it as: GAs are good for getting to a local minimum error - finding a known-good state. NNs are better for hill climbing - exploring the local solution space. Another way to say that is, corporations spent billions of dollars to train the first LLM MVPs with gradient descent and other "hands on" techniques. When maybe they could have spent thousands of dollars if they had used GAs instead and "let go". Now that we're here, refinement is more important, so gradient descent is here to stay. Although I think of LLM blueprints as eventually fitting on a napkin and running on one chip. Then we'll orchestrate large numbers of them to solve problems in a coordinated fashion. At which point it might make sense to use techniques from GAs to create an orchestra of the mind, where each region of an artificial brain is always learning and evolving and bouncing ideas off the others.
- ralphc 1y ago
  Erlang figured this out so much you've described the BEAM. You can have hundreds of thousands of small "processes" communicating through BEAM mailboxes on a modern machine. Elixir has protocols and macros, improvements on Erlang, and if you really require lisp there is LFE, Lisp Flavored Erlang, that has true lisp homoiconicity.
  - zackmorris 1y ago
    Wow LFE is really cool, thanks for showing me another new thing! Admittedly I got pretty depressed about the lack of progress on this stuff and kinda checked out over the years. So my knowledge has gaps since the 90s. - I'll use this as an excuse to talk about another learning model besides NNs and GAs: Boltzmann Machines (BMs). For anyone unfamiliar with them, they can be thought of as a temperature minimization or gas diffusion model. Rather than gradient descent, simpler formulas like summations and random sampling can be used, so they are "big dumb algorithms" like GAs as opposed to "fickle algorithms" like NNs. They borrow concepts from Markov chains and simulated annealing. I'm having trouble finding a single approachable summary though: https://en.wikipedia.org/wiki/Boltzmann_machine https://en.wikipedia.org/wiki/Boltzmann_machine https://www.geeksforgeeks.org/types-of-boltzmann-machines/ https://www.geeksforgeeks.org/types-of-boltzmann-machines/ https://medium.com/@soumallya160/a-complete-guide-to-boltzmann-machine-deep-learning-7f7ce29b9e09 https://medium.com/@soumallya160/a-complete-guide-to-boltzma... https://blog.paperspace.com/beginners-guide-to-boltzmann-machines-pytorch/ https://blog.paperspace.com/beginners-guide-to-boltzmann-mac... https://deepai.org/machine-learning-glossary-and-terms/boltzmann-machine https://deepai.org/machine-learning-glossary-and-terms/boltz... The main problem is that their fully-connected nature makes them difficult to train beyond a certain size. Restricted Boltzmann Machines (RBMs) try to overcome that, but their increased complexity makes them more like NNs. I think that GAs could be useful for training fully-connected BMs by using evolution to decide which weights to update, similarly to your original question of whether GAs could be used in place of gradient descent. Unfortunately I can't find succinct articles about it because it's pretty fringe: Using GAs to train BMs: http://www.icj-e.org/download/ICJE-5-10-108-116.pdf http://www.icj-e.org/download/ICJE-5-10-108-116.pdf Using BMs to train GAs: https://doc.lagout.org/science/0_Computer%20Science/2_Algorithms/Practical%20Handbook%20of%20GENETIC%20ALGORITHMS%2c%20Volume%20II/ganf5.pdf https://doc.lagout.org/science/0_Computer%20Science/2_Algori... Towards combining GAs with BMs and possibly NNs: https://www.sciencedirect.com/science/article/abs/pii/0375960187901496 https://www.sciencedirect.com/science/article/abs/pii/037596... https://www.sciencedirect.com/science/article/abs/pii/016781919400071H https://www.sciencedirect.com/science/article/abs/pii/016781... Note the dates on those last papers: 1987 and 1995. These are not new ideas. Unfortunately they're paywalled and I can't find online PDFs for them. I think what went wrong with LLMs is their fixation on NNs. The code smell for that is their exorbitant energy cost of training. I'd compare it to the high energy cost of proof-of-work crypto like Bitcoin vs proof-of-stake crypto like Ethereum. Or the high mental load of React vs Vue or especially htmx. A programmer's productivity is proportional to the level of abstraction, in other words how much mental load can be offloaded to the tooling. Which means that programming started regressing when we abandoned web for native mobile apps and doubled down on imperative programming with single-page applications around 2007, probably as a result of the Dot Bomb and lean approaches like Agile which resemble austerity, as opposed to those like Waterfall which resemble central planning. Understandable from a business perspective, but tragic for the cause of pure research and human progress towards achieving self-actualization through freedom from labor. Since efficiency didn't pan out, I see that as a sign that big dumb approaches like GAs deserve further study. My feeling is that all machine learning methods are actually equivalent and interconvertible, like with change of coordinates in math and transpiling. So we should use the conceptually simplest ones to avoid the complexity pitfalls of more complex ones that often lead to poor performance. - To put this in perspective, a neuron can perform about 1000 operations per second, or 1/1000 mips (megaflops), which is about as powerful as ENIAC: https://jetpress.org/volume1/moravec.htm https://jetpress.org/volume1/moravec.htm Our mind is about 100 billion neurons that have the potential to connect to others very far away. So a 100 million megaflops or 100 teraflops (10^14 flops) computer should be able to simulate aspects of the human brain. We just reached 1 exaflops (10^18 flops) supercomputers 3 years ago, which is about 10,000 times faster than needed: https://en.wikipedia.org/wiki/List_of_fastest_computers https://en.wikipedia.org/wiki/List_of_fastest_computers So something has gone terribly wrong with the way we utilize computing power. I blame it on our fixation with narrow single-threaded performance leading to an inability to see outside-the-box solutions. For a tiny fraction of the effort we've expended, we could have built wide multi-threaded CPUs like the ones I mentioned, that more naturally handle the parallel computation of GAs and the human brain. This is analogous to the problems we're facing when 1 billionaire has the wealth and resources of 1 million people. Even if they have an IQ of 200, they're still orders of magnitude less effective than all of those people solving a problem in a unified fashion. That's why systems-level problems like infant mortality, mass-starvation, global warming, etc aren't being solved. When we look deep enough, we start to see that everything is connected, and that it's impossible to talk about technological progress without political progress. But I digress. - Where I'm going with this is that whatever machine learning algorithm we settle on will have an underlying concept like evolution at its core. Each neuron is doing the best it can to find connection with the others and minimize its workload to maximize the resources available to it. Just like ants and fungi and human beings. I believe that this evolution away from entropy towards complex structure is the essence of life, and that instinct, awareness, feeling and meaning - along with problem-solving, thinking, doing and reason - are two sides of the same coin. Currently tech mainly addresses the right (masculine?) side of that coin, while the left (feminine?) side was barely touched on by companies like Apple and Atari early on. I mean masculine/feminine in the divine sense in all of us, not gender. Now we're immersed in a world of technological magic with virtually no understanding of our magical nature or how to wield that power responsibly. Basically we're so distracted that we don't realize that we're divine beings with the power to change our outer reality through manifestation by being mindful of where we place our attention in our inner reality. That quantum effects bubble up into the real world like the butterfly effect, how life "finds a way" and subtly shifts probability to create outcomes favorable to it. I believe that if we mimick this quantum-determinism bridge using highly-parallel processors running on the order of 100 billion (10^11) threads that machines will acquire consciousness. Because at some level it won't matter if the smallest components are made of carbon or silicon. In other words, artificial intelligence can be achieved by brute forcing sequential computing power (intellect/knowledge). But artificial consciousness can be achieved by brute forcing parallel computing power (intuition/wisdom). This is the piece of the puzzle that's missing with LLMs and most other competing approaches. A GPU is basically a hugely wide processor running in the low hundreds of threads. It may never reach a level that could be mistaken for consciousness, because it can't explore a large number of potential solutions simultaneously. Unless we build transputers from FPGAs or find a way to transpile SISD and MIMD into SIMD to truly run billions of isolated threads with something like the BEAM/LFE you mentioned. On that note, we also need advances in programming languages to play with this sort of intelligent machine. There are countless examples of running lisp in C, but almost no examples of running C in lisp. That's a huge problem, because the real world is imperative. We need to be able to tell computers our problems and the solutions we need using familar formula-style syntax like C, but have the computer work with lisp's icode tree internally like a spreadsheet so that it doesn't get lost in the weeds. This is where I would start, if I had the resources to do so. I'd write a functional imperative language that uses all const variables so that all logic is free of side effects and can be statically analyzed so that it translates directly to lisp and back, avoiding the complexities of borrow checkers like in Rust. But it would have every convenience method we've come to expect from languages like PHP and Javascript so that we can get real work done. And be decomposable into as many concurrent threads as possible from the start using a divide and conquer strategy, so that even uninspired/unoptimized code would run thousands or millions of times faster than the languages we're used to. And it would have a JIT to transpile most other languages to/from itself to create a functional/imperative bridge. Then I'd write a runtime for GPUs to run those isolated threads. I don't know if this is possible, but if it isn't, that might create demand for true multicore CPUs. Well I've completely gotten off topic and revealed some of my most heartfelt dreams here, and I doubt that anyone will read this. I wish I could be as concise as you, because you connected a lot of insights from so few words.

Would your 1000 core CPU do well on neuroevolution?

by ralphc 1y ago

Would your 1000 core CPU do well on neuroevolution?

Thank you, believe it or not I hadn't heard that word so it filled in a piece of the puzzle for me. https://en.wikipedia.org/wiki/Neuroevolution https://en.wikipedia.org/wiki/Neuroevolution https://medium.com/@roopal.tatiwar20/neuroevolution-evolving-neural-network-with-genetic-algorithms-8ca2165ad04c https://medium.com/@roopal.tatiwar20/neuroevolution-evolving... That was the state of the art when I got my ECE degree in 1999. They were using genetic algorithms (GAs) to evolve initial weights for neural net (NN) gradient descent right before the Dot Bomb. Then pretty much all R&D was cut/abandoned overnight, and the 2000s went to offshoring jobs and shuttering factories which led to the Housing Bubble popping. IMHO that set AI research back at least 10 years, maybe 20, at least in the US. I know it derailed my career dreams. I feel that GPUs by their SIMD nature do poorly with a large number of concurrent processes. For example, a population of GA agents running lisp with each instruction of the intermediate code (icode) tree encoded as bits in a bitfield which evolves using techniques like mutation, crossover and sexual reproduction. Some of those bits represent conditional branching. Ideally we'd want the opposite of a GPU, more like a transputer, on the order of thousands of independent arithmetic logic units (ALUs) with their own local memories, and perhaps able to use custom instructions. That way each lisp tree can execute concurrently in isolation. There may be branch-avoidance transformations from shaders that could help with this, to transpile the von Neumann SISD and MIMD code we're used to into the narrower SIMD so it can run on GPUs. Companies like Nvidia aren't touching this for some reason, and I don't know if it's just a blind spot or deliberate. This is one reason why I wanted FPGAs to take off, since they'd make great transputers. Unfortunately they stagnated due to proprietary compilers and a lack of profitable applications. Today it looks like microcontroller units (MCUs) might take their place, but they're still at least 4 orders of magnitude or more too small and 100 times too slow to be cost-effective. Imagine having a 1 GHz CPU that can run 1000 concurrent UNIX processes running lisp that only communicate through sockets. It's easy to see how mutating them is trivial, then running them as unit tests to see which ones pass is also straightforward. Erlang and Go figured that out years ago. It's so easy in fact to "just come up with the answer" this way, that I think this technique has been suppressed. With that foundation and removing the element of time, it's easy to see that this could be a drop-in replacement for gradient descent. Then the work shifts to curating large data and training sets. I suspect that this is the real work of AI research, and that the learning technique doesn't matter so much. I think of it as: GAs are good for getting to a local minimum error - finding a known-good state. NNs are better for hill climbing - exploring the local solution space. Another way to say that is, corporations spent billions of dollars to train the first LLM MVPs with gradient descent and other "hands on" techniques. When maybe they could have spent thousands of dollars if they had used GAs instead and "let go". Now that we're here, refinement is more important, so gradient descent is here to stay. Although I think of LLM blueprints as eventually fitting on a napkin and running on one chip. Then we'll orchestrate large numbers of them to solve problems in a coordinated fashion. At which point it might make sense to use techniques from GAs to create an orchestra of the mind, where each region of an artificial brain is always learning and evolving and bouncing ideas off the others.

ralphc 1y ago
Erlang figured this out so much you've described the BEAM. You can have hundreds of thousands of small "processes" communicating through BEAM mailboxes on a modern machine. Elixir has protocols and macros, improvements on Erlang, and if you really require lisp there is LFE, Lisp Flavored Erlang, that has true lisp homoiconicity.
- zackmorris 1y ago
  Wow LFE is really cool, thanks for showing me another new thing! Admittedly I got pretty depressed about the lack of progress on this stuff and kinda checked out over the years. So my knowledge has gaps since the 90s. - I'll use this as an excuse to talk about another learning model besides NNs and GAs: Boltzmann Machines (BMs). For anyone unfamiliar with them, they can be thought of as a temperature minimization or gas diffusion model. Rather than gradient descent, simpler formulas like summations and random sampling can be used, so they are "big dumb algorithms" like GAs as opposed to "fickle algorithms" like NNs. They borrow concepts from Markov chains and simulated annealing. I'm having trouble finding a single approachable summary though: https://en.wikipedia.org/wiki/Boltzmann_machine https://en.wikipedia.org/wiki/Boltzmann_machine https://www.geeksforgeeks.org/types-of-boltzmann-machines/ https://www.geeksforgeeks.org/types-of-boltzmann-machines/ https://medium.com/@soumallya160/a-complete-guide-to-boltzmann-machine-deep-learning-7f7ce29b9e09 https://medium.com/@soumallya160/a-complete-guide-to-boltzma... https://blog.paperspace.com/beginners-guide-to-boltzmann-machines-pytorch/ https://blog.paperspace.com/beginners-guide-to-boltzmann-mac... https://deepai.org/machine-learning-glossary-and-terms/boltzmann-machine https://deepai.org/machine-learning-glossary-and-terms/boltz... The main problem is that their fully-connected nature makes them difficult to train beyond a certain size. Restricted Boltzmann Machines (RBMs) try to overcome that, but their increased complexity makes them more like NNs. I think that GAs could be useful for training fully-connected BMs by using evolution to decide which weights to update, similarly to your original question of whether GAs could be used in place of gradient descent. Unfortunately I can't find succinct articles about it because it's pretty fringe: Using GAs to train BMs: http://www.icj-e.org/download/ICJE-5-10-108-116.pdf http://www.icj-e.org/download/ICJE-5-10-108-116.pdf Using BMs to train GAs: https://doc.lagout.org/science/0_Computer%20Science/2_Algorithms/Practical%20Handbook%20of%20GENETIC%20ALGORITHMS%2c%20Volume%20II/ganf5.pdf https://doc.lagout.org/science/0_Computer%20Science/2_Algori... Towards combining GAs with BMs and possibly NNs: https://www.sciencedirect.com/science/article/abs/pii/0375960187901496 https://www.sciencedirect.com/science/article/abs/pii/037596... https://www.sciencedirect.com/science/article/abs/pii/016781919400071H https://www.sciencedirect.com/science/article/abs/pii/016781... Note the dates on those last papers: 1987 and 1995. These are not new ideas. Unfortunately they're paywalled and I can't find online PDFs for them. I think what went wrong with LLMs is their fixation on NNs. The code smell for that is their exorbitant energy cost of training. I'd compare it to the high energy cost of proof-of-work crypto like Bitcoin vs proof-of-stake crypto like Ethereum. Or the high mental load of React vs Vue or especially htmx. A programmer's productivity is proportional to the level of abstraction, in other words how much mental load can be offloaded to the tooling. Which means that programming started regressing when we abandoned web for native mobile apps and doubled down on imperative programming with single-page applications around 2007, probably as a result of the Dot Bomb and lean approaches like Agile which resemble austerity, as opposed to those like Waterfall which resemble central planning. Understandable from a business perspective, but tragic for the cause of pure research and human progress towards achieving self-actualization through freedom from labor. Since efficiency didn't pan out, I see that as a sign that big dumb approaches like GAs deserve further study. My feeling is that all machine learning methods are actually equivalent and interconvertible, like with change of coordinates in math and transpiling. So we should use the conceptually simplest ones to avoid the complexity pitfalls of more complex ones that often lead to poor performance. - To put this in perspective, a neuron can perform about 1000 operations per second, or 1/1000 mips (megaflops), which is about as powerful as ENIAC: https://jetpress.org/volume1/moravec.htm https://jetpress.org/volume1/moravec.htm Our mind is about 100 billion neurons that have the potential to connect to others very far away. So a 100 million megaflops or 100 teraflops (10^14 flops) computer should be able to simulate aspects of the human brain. We just reached 1 exaflops (10^18 flops) supercomputers 3 years ago, which is about 10,000 times faster than needed: https://en.wikipedia.org/wiki/List_of_fastest_computers https://en.wikipedia.org/wiki/List_of_fastest_computers So something has gone terribly wrong with the way we utilize computing power. I blame it on our fixation with narrow single-threaded performance leading to an inability to see outside-the-box solutions. For a tiny fraction of the effort we've expended, we could have built wide multi-threaded CPUs like the ones I mentioned, that more naturally handle the parallel computation of GAs and the human brain. This is analogous to the problems we're facing when 1 billionaire has the wealth and resources of 1 million people. Even if they have an IQ of 200, they're still orders of magnitude less effective than all of those people solving a problem in a unified fashion. That's why systems-level problems like infant mortality, mass-starvation, global warming, etc aren't being solved. When we look deep enough, we start to see that everything is connected, and that it's impossible to talk about technological progress without political progress. But I digress. - Where I'm going with this is that whatever machine learning algorithm we settle on will have an underlying concept like evolution at its core. Each neuron is doing the best it can to find connection with the others and minimize its workload to maximize the resources available to it. Just like ants and fungi and human beings. I believe that this evolution away from entropy towards complex structure is the essence of life, and that instinct, awareness, feeling and meaning - along with problem-solving, thinking, doing and reason - are two sides of the same coin. Currently tech mainly addresses the right (masculine?) side of that coin, while the left (feminine?) side was barely touched on by companies like Apple and Atari early on. I mean masculine/feminine in the divine sense in all of us, not gender. Now we're immersed in a world of technological magic with virtually no understanding of our magical nature or how to wield that power responsibly. Basically we're so distracted that we don't realize that we're divine beings with the power to change our outer reality through manifestation by being mindful of where we place our attention in our inner reality. That quantum effects bubble up into the real world like the butterfly effect, how life "finds a way" and subtly shifts probability to create outcomes favorable to it. I believe that if we mimick this quantum-determinism bridge using highly-parallel processors running on the order of 100 billion (10^11) threads that machines will acquire consciousness. Because at some level it won't matter if the smallest components are made of carbon or silicon. In other words, artificial intelligence can be achieved by brute forcing sequential computing power (intellect/knowledge). But artificial consciousness can be achieved by brute forcing parallel computing power (intuition/wisdom). This is the piece of the puzzle that's missing with LLMs and most other competing approaches. A GPU is basically a hugely wide processor running in the low hundreds of threads. It may never reach a level that could be mistaken for consciousness, because it can't explore a large number of potential solutions simultaneously. Unless we build transputers from FPGAs or find a way to transpile SISD and MIMD into SIMD to truly run billions of isolated threads with something like the BEAM/LFE you mentioned. On that note, we also need advances in programming languages to play with this sort of intelligent machine. There are countless examples of running lisp in C, but almost no examples of running C in lisp. That's a huge problem, because the real world is imperative. We need to be able to tell computers our problems and the solutions we need using familar formula-style syntax like C, but have the computer work with lisp's icode tree internally like a spreadsheet so that it doesn't get lost in the weeds. This is where I would start, if I had the resources to do so. I'd write a functional imperative language that uses all const variables so that all logic is free of side effects and can be statically analyzed so that it translates directly to lisp and back, avoiding the complexities of borrow checkers like in Rust. But it would have every convenience method we've come to expect from languages like PHP and Javascript so that we can get real work done. And be decomposable into as many concurrent threads as possible from the start using a divide and conquer strategy, so that even uninspired/unoptimized code would run thousands or millions of times faster than the languages we're used to. And it would have a JIT to transpile most other languages to/from itself to create a functional/imperative bridge. Then I'd write a runtime for GPUs to run those isolated threads. I don't know if this is possible, but if it isn't, that might create demand for true multicore CPUs. Well I've completely gotten off topic and revealed some of my most heartfelt dreams here, and I doubt that anyone will read this. I wish I could be as concise as you, because you connected a lot of insights from so few words.