4 ms·

The closest biological equivalent to a parameter in an ann is a synapse. Well humans have about 100 trillion synapses. We already know that the higher the param

The closest biological equivalent to a parameter in an ann is a synapse. Well humans have about 100 trillion synapses. We already know that the higher the parameter count, the lower the training data required. a 50 billion parameter model will far outperform a 5 billion one trained on the same data. and a 500b one would far outperform that 50 billion one. Current LLMs are actually nowhere near the scale of the human brain, either in parameters/neurons or training data (all the text we've ever trained an LLM on would be dwarfed by all the sense data humans perceive), as well as not having the headstart the human brain has. It's a bogus comparison when you really think about it. You could easily make the case that LLMs are far more efficient.

chaxor 3y ago
It is a bogus comparison, because typical language models used already have textual representations, and output textual representations; which is a very small fraction of 'the brain'. An astounding portion of the brain's neurons really go towards proprioception, smell, taste, motor function, etc. Which are not at all even slightly part of most models today. Wernicke's area, a small sliver of frontal cortex, and a dash of dopaminergic circuitry is maybe the best 'coverage' made in these models if you want to be exceedingly facile/ham-handed about the analogies here. That's a very small portion of cortex, and much closer than what you may think in terms of capability/ TEPS and unit count.
- reaperman 3y ago
  It's not clear how many classical calculations a single human neuron is equivalent to. There's a strong analog component in multiple domains (strength, frequency, timing) and each neuron can connect to up to 15,000 other neurons. Assuming the brain's neurons are (probably unrealistically) fairly 'digital' we get an estimation of the human brain being equivalent to 1 exaflop (this is the currently accepted lower bound, and rather disputed as being too low). Current TPUv4 pods currently provide approximately 9 exaflops. I don't think we're currently reaching human-level learning rates. There’s currently no accepted “upper bound” on estimates of FLOP equivalency to a human brain. https://www.youtube.com/watch?v=HB5TrK7A4pI https://www.youtube.com/watch?v=HB5TrK7A4pI is a recently posted video to HN Frontpage which was summarized as such: > Though we have been building and programming computing machines for about 60 years and have learned a great deal about composition and abstraction, we have just begun to scratch the surface. > A mammalian neuron takes about ten milliseconds to respond to a stimulus. A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it. > The human genome -- the information required to build a human from a single, undifferentiated eukariotic cell -- is about 1GB. The instructions to build a mammal are written in very dense code, and the program is extremely flexible. Only small patches to the human genome are required to build a cow or a dog rather than a human. Bigger patches result in a frog or a snake. We don't have any idea how to make a description of such a complex machine that is both dense and flexible. > New design principles and new linguistic support are needed. I will address this issue and show some ideas that can perhaps get us to the next phase of engineering design. > Gerald Sussman Massachusetts Institute of Technology
  - chaxor 3y ago
    My understanding is that TEPS were used to determine computing for these types operations, rather than FLOPS, as they were more useful specifically for that comparison. There metrics put them in the same order of magnitude; however, as stated before, these miss the point by quite a bit, since much of the 'computations' humans do are quite irrelevant (taste, smell, etc) to producing language or solving algorithmic problems, etc. For example, the cerebellum is 50-80% of what people keep quoting here (Number of neurons in the brain) and is not activated much in language processing. Wernicke's area spans just a few percent of the cortical neurons. The amount of pre processing we do by providing text is actually quite enormous, so that already removes a remarkable amount of complexity from the model. So, despite the differences between biology and ANNs, it's not unreasonable what were seeing right now.
  - fdgddggfddfg 3y ago
    Look this is great thinking... I don't want to diminish that but think of a brain like an fpga (parallel logic) not a synchronous chip with memory and fetch decode execute style steps.... We do things in a massively parallel way and that is why and how we can do things quickly and efficiently!
- mr_toad 3y ago
  > An astounding portion of the brain's neurons really go towards proprioception, smell, taste, motor function, etc Object recognition leads to abstraction. Motion perception to causality. I wouldn’t be surprised if proprioception is key to human self-awareness. These are key logical concepts that are used in language, they are not isolated.
  - pgt 3y ago
    Yes, I don't think consciousness can exist without feedback and delay. You can still experience a motionless room, but that's because the previous "frames" are still bouncing around in your brain. If you remove the historical replay (delayed feedback), it's something else.
  - chaxor 3y ago
    I certainly recognize that possibility; but I also realize that systems can be extremely useful, and have a great 'understanding' (for some definition of 'understanding') of linguistic and visual data, without any need for 'sentience', 'conscious', or any other completely ill-defined ideas anyone wants to throw around. There are a few cases where overlaps in sensory cortex above visual, audio, and linguistic processing (the main systems every decent AI already has as inputs, which are a very small fraction of the brain) would be very helpful, but clearly not absolutely necessary, in improving the capability of a world model - for example, know that a metal container half full water will slosh differently than a full or empty one. That requires proprioception, motor skills, as well as visual inputs etc. So cases such as this will be slightly less performant, but they're typically not relevant for tasks we are interested in automating.
coldtea 3y ago
>a 50 billion parameter model will far outperform a 5 billion one trained on the same data. and a 500b one would far outperform that 50 billion one. I'm not so sure. I'm pretty sure there are diminishing returns at play after some point. Plus haven't we already seen models with much less billions of parameters perform the same or very close to ChatGPT with had a much higher count (Llama and its siblings)?
- famouswaffles 3y ago
  >a 50 billion parameter model will far outperform a 5 billion one trained on the same data. and a 500b one would far outperform that 50 billion one. I'm not so sure. I'm pretty sure there are diminishing returns at play after some point. We can speculate about just how far this scaling can go or how far is even necessary but all i've said there is true. We have models trained and evaluated on all those sizes. >Plus haven't we already seen models with much less billions of parameters perform the same or very close to ChatGPT with had a much higher count (Llama and its siblings)? Only by training on far more data. Llama 13b has to be trained on over 3x more data just to reach the original GPT-3 model from 2020 (not 3.5).
  - deleted 3y ago
    [deleted]
  - coldtea 3y ago
    >We can speculate about just how far this scaling can go or how far is even necessary but all i've said there is true. We have models trained and evaluated on all those sizes. The part about "far outperforming", which is the main claim, is wrong though. We saw models much smaller being developed that fare quite well, and are even competitive, with the larger ones. You already said "only by training on far more data", which is different than "more parameters" being the only option.
    - famouswaffles 3y ago
      >You already said "only by training on far more data", which is different than "more parameters" being the only option. I never said more parameters was the only way to increase performance. I said the training data required to reach any arbitrary performance x reduces with parameter size. It's literally right there in what I wrote. >a 50 billion parameter model will far outperform a 5 billion one TRAINED ON THE SAME DATA.
somenameforme 3y ago
You run into the typical neural net problem with this logic. OpenAI (or at least Sam Altman) have already publicly acknowledged that the diminishing returns they're seeing in terms of model size are sufficient to effectively declare that 'the age of giant models is already over.' [1] It seems many people were unaware of his comments on this topic. Neural networks in literally every other field always repeat the exact same pattern. You can get from 0-80 without breaking a sweat. 80-90 is dramatically harder but you finally get there. So everybody imagines getting from 90-100 will be little more than a matter of a bit more compute and a bit more massaging of the model. But it turns out that each fraction of a percent progress you make starts becoming exponentially more difficult - and you eventually run into an asymptote that's nowhere near what you are aiming for. A prediction based on the typical history of neural nets would be that OpenAI will be able to continue to make progress on extremely specific metrics, like scoring well on some test or another, largely by hardcoding case-specific workarounds and tweaks. But in terms of general model usage, we're unlikely to see any real revolutionary leaps in the foreseeable future. If we see model accuracy increase I'd expect it to be thanks not to model improvement, but instead by doing something like adding a second layer where the software cross references the generated output against a 'fact database' and regenerates its answer when some correlation factor is insufficiently high. Of course that'd completely cripple the model's ability to ever move 'beyond' its training. It'd be like if mankind was forced to double check that any response on astronomy we made confirmed that the Earth is indeed the center of the universe, with no ability to ever change that ourselves. [1] - https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/ https://www.wired.com/story/openai-ceo-sam-altman-the-age-of...
- oezi 3y ago
  There is an argument that Altman's statement is just trying to distract competitors from outspending OpenAI. Prior to GPT-4 there was no indications that there are diminishing returns (at least on a log scale). The tremendous progress over the last year makes me vary of your statement that progress will stop coming from model size improvements.
  - coldtea 3y ago
littlestymaar 3y ago
> Well humans have about 100 trillion synapses. We already know that the higher the parameter count, the lower the training data required. Do you have any reference to back this claim, because it sounds is very curious to me. My understanding was pretty much the opposite, that current LLM technology require a bigger training set as you increase the parameter count. I'm no NN expert in any way though.
- famouswaffles 3y ago
  You can look at any training run comparing multiple parameter sizes on the dataset. https://arxiv.org/abs/2204.02311 https://arxiv.org/abs/2204.02311 If you're increasing parameter size, it's a no brainer to increase data too as that will still also increase performance. The point is that for any arbitrary performance x, the data required to reach it reduces with size.
  - littlestymaar 3y ago
    > If you're increasing parameter size, it's a no brainer to increase data too as that will still also increase performance. It also increases the cost by a lot, so it's not a no-brainer at all. If they could beat the state of the art with only a fraction of the training cost, I suspect that they'd do so… > The point is that for any arbitrary performance x, the data required to reach it reduces with size. This is the claim you're making, but it's not substantiated.
    - famouswaffles 3y ago
      >It also increases the cost by a lot, so it's not a no-brainer at all. Okay?.. Parameter size increases also increase cost a lot. Far more than more training data. Costs that stay well beyond training. Training on 1T tokens vs 500b won't change how resources it takes to run. Not the cases with parameter sizes. >If they could beat the state of the art with only a fraction of the training cost, I suspect that they'd do so… Not sure what this has to do with anything lol >This is the claim you're making, but it's not substantiated. I'm sorry but can you perhaps just read the paper sent ? Google trained 3 differently sized models of the same architecture (8b, 62b, 540b) on the same dataset of 780b tokens and evaluated all 3 on various tasks.
bionhoward 3y ago
Gpt4 is 1% of the brain’s param count, which means we’re basically already there. It’s exponential growth!
nerpderp82 3y ago
This is why all the Alpha%wildcard% projects use self play, that along with a fitness function, or score maximizer generates their training data rather than have to show it records of games. You just let it play the game.
- reaperman 3y ago
  Not all of them. AlphaStar for StarCraft was largely supervised learning, to mimic top human players. Once it was sufficiently mimicking existing replays, then it switched to Reinforcement Learning. That's why you see it do "nonsensical" things like destroying the "unbuildable plates" at the bottom of its natural ramp. This 100% wouldn't happen if it had only learned through self-play.

chaxor 3y ago

It is a bogus comparison, because typical language models used already have textual representations, and output textual representations; which is a very small fraction of 'the brain'. An astounding portion of the brain's neurons really go towards proprioception, smell, taste, motor function, etc. Which are not at all even slightly part of most models today. Wernicke's area, a small sliver of frontal cortex, and a dash of dopaminergic circuitry is maybe the best 'coverage' made in these models if you want to be exceedingly facile/ham-handed about the analogies here. That's a very small portion of cortex, and much closer than what you may think in terms of capability/ TEPS and unit count.

reaperman 3y ago
It's not clear how many classical calculations a single human neuron is equivalent to. There's a strong analog component in multiple domains (strength, frequency, timing) and each neuron can connect to up to 15,000 other neurons. Assuming the brain's neurons are (probably unrealistically) fairly 'digital' we get an estimation of the human brain being equivalent to 1 exaflop (this is the currently accepted lower bound, and rather disputed as being too low). Current TPUv4 pods currently provide approximately 9 exaflops. I don't think we're currently reaching human-level learning rates. There’s currently no accepted “upper bound” on estimates of FLOP equivalency to a human brain. https://www.youtube.com/watch?v=HB5TrK7A4pI https://www.youtube.com/watch?v=HB5TrK7A4pI is a recently posted video to HN Frontpage which was summarized as such: > Though we have been building and programming computing machines for about 60 years and have learned a great deal about composition and abstraction, we have just begun to scratch the surface. > A mammalian neuron takes about ten milliseconds to respond to a stimulus. A driver can respond to a visual stimulus in a few hundred milliseconds, and decide an action, such as making a turn. So the computational depth of this behavior is only a few tens of steps. We don't know how to make such a machine, and we wouldn't know how to program it. > The human genome -- the information required to build a human from a single, undifferentiated eukariotic cell -- is about 1GB. The instructions to build a mammal are written in very dense code, and the program is extremely flexible. Only small patches to the human genome are required to build a cow or a dog rather than a human. Bigger patches result in a frog or a snake. We don't have any idea how to make a description of such a complex machine that is both dense and flexible. > New design principles and new linguistic support are needed. I will address this issue and show some ideas that can perhaps get us to the next phase of engineering design. > Gerald Sussman Massachusetts Institute of Technology
- chaxor 3y ago
  My understanding is that TEPS were used to determine computing for these types operations, rather than FLOPS, as they were more useful specifically for that comparison. There metrics put them in the same order of magnitude; however, as stated before, these miss the point by quite a bit, since much of the 'computations' humans do are quite irrelevant (taste, smell, etc) to producing language or solving algorithmic problems, etc. For example, the cerebellum is 50-80% of what people keep quoting here (Number of neurons in the brain) and is not activated much in language processing. Wernicke's area spans just a few percent of the cortical neurons. The amount of pre processing we do by providing text is actually quite enormous, so that already removes a remarkable amount of complexity from the model. So, despite the differences between biology and ANNs, it's not unreasonable what were seeing right now.
- fdgddggfddfg 3y ago
  Look this is great thinking... I don't want to diminish that but think of a brain like an fpga (parallel logic) not a synchronous chip with memory and fetch decode execute style steps.... We do things in a massively parallel way and that is why and how we can do things quickly and efficiently!
mr_toad 3y ago
> An astounding portion of the brain's neurons really go towards proprioception, smell, taste, motor function, etc Object recognition leads to abstraction. Motion perception to causality. I wouldn’t be surprised if proprioception is key to human self-awareness. These are key logical concepts that are used in language, they are not isolated.
- pgt 3y ago
  Yes, I don't think consciousness can exist without feedback and delay. You can still experience a motionless room, but that's because the previous "frames" are still bouncing around in your brain. If you remove the historical replay (delayed feedback), it's something else.
- chaxor 3y ago
  I certainly recognize that possibility; but I also realize that systems can be extremely useful, and have a great 'understanding' (for some definition of 'understanding') of linguistic and visual data, without any need for 'sentience', 'conscious', or any other completely ill-defined ideas anyone wants to throw around. There are a few cases where overlaps in sensory cortex above visual, audio, and linguistic processing (the main systems every decent AI already has as inputs, which are a very small fraction of the brain) would be very helpful, but clearly not absolutely necessary, in improving the capability of a world model - for example, know that a metal container half full water will slosh differently than a full or empty one. That requires proprioception, motor skills, as well as visual inputs etc. So cases such as this will be slightly less performant, but they're typically not relevant for tasks we are interested in automating.

coldtea 3y ago

>a 50 billion parameter model will far outperform a 5 billion one trained on the same data. and a 500b one would far outperform that 50 billion one. I'm not so sure. I'm pretty sure there are diminishing returns at play after some point. Plus haven't we already seen models with much less billions of parameters perform the same or very close to ChatGPT with had a much higher count (Llama and its siblings)?

famouswaffles 3y ago
>a 50 billion parameter model will far outperform a 5 billion one trained on the same data. and a 500b one would far outperform that 50 billion one. I'm not so sure. I'm pretty sure there are diminishing returns at play after some point. We can speculate about just how far this scaling can go or how far is even necessary but all i've said there is true. We have models trained and evaluated on all those sizes. >Plus haven't we already seen models with much less billions of parameters perform the same or very close to ChatGPT with had a much higher count (Llama and its siblings)? Only by training on far more data. Llama 13b has to be trained on over 3x more data just to reach the original GPT-3 model from 2020 (not 3.5).
- deleted 3y ago
  [deleted]
- coldtea 3y ago
  >We can speculate about just how far this scaling can go or how far is even necessary but all i've said there is true. We have models trained and evaluated on all those sizes. The part about "far outperforming", which is the main claim, is wrong though. We saw models much smaller being developed that fare quite well, and are even competitive, with the larger ones. You already said "only by training on far more data", which is different than "more parameters" being the only option.
  - famouswaffles 3y ago
    >You already said "only by training on far more data", which is different than "more parameters" being the only option. I never said more parameters was the only way to increase performance. I said the training data required to reach any arbitrary performance x reduces with parameter size. It's literally right there in what I wrote. >a 50 billion parameter model will far outperform a 5 billion one TRAINED ON THE SAME DATA.

somenameforme 3y ago

You run into the typical neural net problem with this logic. OpenAI (or at least Sam Altman) have already publicly acknowledged that the diminishing returns they're seeing in terms of model size are sufficient to effectively declare that 'the age of giant models is already over.' [1] It seems many people were unaware of his comments on this topic. Neural networks in literally every other field always repeat the exact same pattern. You can get from 0-80 without breaking a sweat. 80-90 is dramatically harder but you finally get there. So everybody imagines getting from 90-100 will be little more than a matter of a bit more compute and a bit more massaging of the model. But it turns out that each fraction of a percent progress you make starts becoming exponentially more difficult - and you eventually run into an asymptote that's nowhere near what you are aiming for. A prediction based on the typical history of neural nets would be that OpenAI will be able to continue to make progress on extremely specific metrics, like scoring well on some test or another, largely by hardcoding case-specific workarounds and tweaks. But in terms of general model usage, we're unlikely to see any real revolutionary leaps in the foreseeable future. If we see model accuracy increase I'd expect it to be thanks not to model improvement, but instead by doing something like adding a second layer where the software cross references the generated output against a 'fact database' and regenerates its answer when some correlation factor is insufficiently high. Of course that'd completely cripple the model's ability to ever move 'beyond' its training. It'd be like if mankind was forced to double check that any response on astronomy we made confirmed that the Earth is indeed the center of the universe, with no ability to ever change that ourselves. [1] - https://www.wired.com/story/openai-ceo-sam-altman-the-age-of-giant-ai-models-is-already-over/ https://www.wired.com/story/openai-ceo-sam-altman-the-age-of...

oezi 3y ago
There is an argument that Altman's statement is just trying to distract competitors from outspending OpenAI. Prior to GPT-4 there was no indications that there are diminishing returns (at least on a log scale). The tremendous progress over the last year makes me vary of your statement that progress will stop coming from model size improvements.
- coldtea 3y ago

littlestymaar 3y ago

> Well humans have about 100 trillion synapses. We already know that the higher the parameter count, the lower the training data required. Do you have any reference to back this claim, because it sounds is very curious to me. My understanding was pretty much the opposite, that current LLM technology require a bigger training set as you increase the parameter count. I'm no NN expert in any way though.

famouswaffles 3y ago
You can look at any training run comparing multiple parameter sizes on the dataset. https://arxiv.org/abs/2204.02311 https://arxiv.org/abs/2204.02311 If you're increasing parameter size, it's a no brainer to increase data too as that will still also increase performance. The point is that for any arbitrary performance x, the data required to reach it reduces with size.
- littlestymaar 3y ago
  > If you're increasing parameter size, it's a no brainer to increase data too as that will still also increase performance. It also increases the cost by a lot, so it's not a no-brainer at all. If they could beat the state of the art with only a fraction of the training cost, I suspect that they'd do so… > The point is that for any arbitrary performance x, the data required to reach it reduces with size. This is the claim you're making, but it's not substantiated.
  - famouswaffles 3y ago
    >It also increases the cost by a lot, so it's not a no-brainer at all. Okay?.. Parameter size increases also increase cost a lot. Far more than more training data. Costs that stay well beyond training. Training on 1T tokens vs 500b won't change how resources it takes to run. Not the cases with parameter sizes. >If they could beat the state of the art with only a fraction of the training cost, I suspect that they'd do so… Not sure what this has to do with anything lol >This is the claim you're making, but it's not substantiated. I'm sorry but can you perhaps just read the paper sent ? Google trained 3 differently sized models of the same architecture (8b, 62b, 540b) on the same dataset of 780b tokens and evaluated all 3 on various tasks.

bionhoward 3y ago

Gpt4 is 1% of the brain’s param count, which means we’re basically already there. It’s exponential growth!

nerpderp82 3y ago

This is why all the Alpha%wildcard% projects use self play, that along with a fitness function, or score maximizer generates their training data rather than have to show it records of games. You just let it play the game.

reaperman 3y ago
Not all of them. AlphaStar for StarCraft was largely supervised learning, to mimic top human players. Once it was sufficiently mimicking existing replays, then it switched to Reinforcement Learning. That's why you see it do "nonsensical" things like destroying the "unbuildable plates" at the bottom of its natural ramp. This 100% wouldn't happen if it had only learned through self-play.