10 ms·

As of right now, we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and

• deleted 2y ago
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• JumpCrisscross 2y ago
  > we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and more This doesn’t solve the unpredictability problem.
  • leobg 2y ago
    We don’t know. We didn’t predict that the rat brain would get us here. So we also can’t be confident in our prediction that scaling it won’t solve hallucination problems.
  • cs702 2y ago
    No, it doesn't "solve" the unpredictability problem. But we haven't solved it for human beings either. Human brains are unpredictable. Look around you.
    • timeon 2y ago
      How are humans relevant here? As example, we operate at different speed.
      • cs702 2y ago
        Humankind has developed all sorts of systems and processes to cope with the unpredictability of human beings: legal systems, organizational structures, separate branches of government, courts of law, police and military forces, organized markets, double-entry bookkeeping, auditing, security systems, anti-malware software, etc. While individual human beings do trust some of the other human beings they know, in the aggregate society doesn't seem to trust human beings to behave reliably. It's possible, though I don't know for sure, that we're going to need systems and processes to cope with the unpredictability of AI systems.
      • mrweasel 2y ago
        Are you expecting AIs to be more reliable, because they're slower?
      • uoaei 2y ago
        Human performance, broadly speaking, is the benchmark being targeted by those training AI models. Humans are part of the conversation since that's the only kind of intelligence these folks can conceive of.
    • sdesol 2y ago
      > Human brains are unpredictable. Look around you. As it was mentioned by others, we've had thousands of years to better understand how humans can fail. LLMs are black boxes and it never ceases to amaze me how they can fail in such unpredictable ways. Take the following for examples. Here GPT-4o mini is asked to calculate 2+3+5 https://beta.gitsense.com/?chat=8707acda-e6d4-4f69-9c09-2cff0907b207 https://beta.gitsense.com/?chat=8707acda-e6d4-4f69-9c09-2cff... It gets the answer correct, but if you ask it to verify its own answer https://beta.gitsense.com/?chat=6d8af370-1ae6-4a36-961d-2902cc17eea0&model=GPT-4o+mini https://beta.gitsense.com/?chat=6d8af370-1ae6-4a36-961d-2902... it says the response was wrong, and contradicts itself. Now if you ask it to compare all the responses https://beta.gitsense.com/?chat=1c162c40-47ea-419d-af7a-a30aba857cca&model=GPT-4o+mini https://beta.gitsense.com/?chat=1c162c40-47ea-419d-af7a-a30a... it correctly identifies that GPT-4o mini was incorrect. It is this unpredictable nature that makes LLM insanely powerful and scary. Note: The chat on the beta site doesn't work.
  • clint 2y ago
    You seem to believe that humans, on their own, are not stochastic and unpredictable. I contend that if this is your belief then you couldn't be more wrong. Humans are EXTREMELY unpredictable. Humans only become slightly more predictable and producers of slightly more quality outputs with insane levels of bureaucracy and layers upon layers upon layers of humans to smooth it out. To boot, the production of this mediocre code is very very very slow compared to LLMs. LLMs also have no feelings, egos, and are literally tunable and directible to produce better outcomes without hurting people in the process (again, something that is very difficult to avoid without the inclusion of, yep, more humans more layers, more protocol etc.) Even with all of this mass of human grist, in my opinion, the output of purely human intellects is, on average, very bad. Very bad in terms of quality of output and very bad in terms of outcomes for the humans involved in this machine.
• zozbot234 2y ago
  A brown rat's brain is also a lot more energy efficient than your average LLM. Especially in the learning phase, but not only.
  • cs702 2y ago
    Yes, I agree, but energy efficiency is orthogonal to capabilities.
    • sanderjd 2y ago
      No it isn't, because it is relevant to the question of whether the current approaches can be scaled 100x or 1000x.
      • cs702 2y ago
        That's a hardware question, not a software question, but it is a fair question. I don't know if the hardware can be scaled up. That's why I wrote "if we're able to scale them" at the root of this thread.
        • bee_rider 2y ago
          It is probably a both question. If 100x is the goal, they’ll have to double up the efficiency 7 times, which seems basically plausible given how early-days it still is (I mean they have been training on GPUs this whole time, not ASICs… bitcoins are more developed and they are a dumb scam machine). Probably some of the doubling will be software, some will be hardware.
          • sanderjd 2y ago
            Yep, agreed. I'm pretty skeptical of the scaling hypothesis, but I also think there is a huge amount of efficiency improvement runway left to go. I think it's more likely that the return to further scaling will become net negative at some point, and then the efficiency gains will no longer be focused on doing more with more but rather doing the same amount with less. But it's definitely an unknown at this point, from my perspective. I may be very wrong about that.
• FredPret 2y ago
  If brown-rats-as-a-service is as useful as it is already, then I'm excited by what the future holds. I think to make it to the next step, AI will have to have some way of performing rigorous logic integrated on a low level. Maybe scaling that brown-rat brain will let it emulate an internal logical black box - much like the old adage about a sufficiently large C codebase containing an imperfect Lisp implementation - but I think things will get really cool we figure out how to wire together something like Wolfram Alpha, a programming language, some databases with lots of actual facts (as opposed to encoded/learned ones), and ChatGPT.
  • cs702 2y ago
    It's already better than real rats-as-a-service, certainly: https://news.ycombinator.com/item?id=42449424 https://news.ycombinator.com/item?id=42449424
  • ndesaulniers 2y ago
    Does it matter what color the rat is?
    • notpushkin 2y ago
      I suppose it refers to the particular species, Rattus norvegicus (although I'd call it common rat personally).
  • petesergeant 2y ago
    ChatGPT can already run code, which allows it to overcome some limitations of tokenization (eg counting the letters in strawberry, sorting words by their second letter). Doesn't seem like adding a Prolog interpreter would be all that hard.
  • Kim_Bruning 2y ago
    ChatGPT does already have access to Bing (would that count as your facts database?) and Jupyter (which is sort of a Wolphram clone except with Python?). It still won't magically use them 100% correctly, but with a bit of smarts you can go a long way!
• bee_rider 2y ago
  Rats are pretty clever, and they (presumably, at least) have a lot of neurons spending their time computing things like… where to find food, how frightened of this giant reality warping creature in a lab coat should I be, that sort of thing. I don’t think it is obvious that one brown-rat-power isn’t useful. I mean we have dogs. We really like them. For ages, they did lots of useful work for us. They aren’t that much smarter than rats, right? They are better aligned and have a more useful shape. But it isn’t obvious (to me at least) that the rats’ problem is insufficient brainpower.
  • bloopernova 2y ago
    Dogs, if I recall correctly, have evolved alongside us and have specific adaptations to better bond with us. They have eyebrow muscles that wolves don't, and I think dogs have brain adaptations too.
    • runarberg 2y ago
      We have been with dogs for such a long time, I wouldn’t be surprised if we also have adaptations to bond with dogs. I mean dogs came with us to the Americas, and even to Australia. Both the Norse and the Inuit took dogs with them to Greenland.
  • mulmen 2y ago
    Depends on how you define smart. Dogs definitely have larger brains. But then humans have even larger brains. If dogs aren’t smarter than rats then the size of brain isn’t proportional to intelligence.
• hn_throwaway_99 2y ago
  I think the comparison to brown rat brains is a huge mistake. It seems pretty apparent (at least from my personal usage of LLMs in different contexts) that modern AI is much smarter than a brown rat at some things (I don't think brown rats can pass the bar exam), but in other cases it becomes apparent that it isn't "intelligent" at all in the sense that it becomes clear that it's just regurgitating training data, albeit in a highly variable manner. I think LLMs and modern AI are incredibly amazing and useful tools, but even with the top SOA models today it becomes clearer to me the more I use them that they are fundamentally lacking crucial components of what average people consider "intelligence". I'm using quotes deliberately because the debate about "what is intelligence" feels like it can go in circles endlessly - I'd just say that the core concept of what we consider understanding, especially as it applies to creating and exploring novel concepts that aren't just a mashup of previous training examples, appears to be sorely missing from LLMs.
  • cs702 2y ago
    Imagine it were possible to take a rat brain, keep it alive with a permanent source of energy, wire its input and output connections to a computer, and then train the rat brain's output signals to predict the next token, given previous tokens fed as inputs, using graduated pain or pleasure signals as the objective loss function. All the neuron-neuron connections in that rain brain would eventually serve one, and only one, goal: predicting an accurate probability distribution over the next possible token, given previous tokens. The number of neuron-neuron connections in this "rat-brain-powered LLM" would be comparable to that of today's state-of-the-art LLMs. This is less far-fetched than it sounds. Search for "organic deep neural networks" online. Networks of rat neurons have in fact been trained to fly planes, in simulators, among other things.
    • ImHereToVote 2y ago
      Human brain organelles are in use right now by a Swiss company.
      • cs702 2y ago
• megamix 2y ago
  What do you think of copyright violations?
  • bee_rider 2y ago
    IMO it is sad that the sort of… anti-establishment side of tech has suddenly become very worried about copyright. Bits inherently can be copied for free (or at least very cheap), copyright is a way to induce scarcity for the market to exploit where there isn’t any on a technical level. Currently the AI stuff kind of sucks because you have to be a giant corp to train a model. But maybe in a decade, users will be able to train their own models or at least fine-tune on basic cellphone and laptop (not dgpu) chips.
    • uoaei 2y ago
      The copyright question is inherently tied to the requirement to earn money from your labor in this economy. I think the anti-establishment folks are not so rabid that they can't recognize real material conditions.
      • LordDragonfang 2y ago
        I think that would be a more valid argument if they ever cared about automating away jobs before. As it stands, anyone who was standing in the way of the glorious march of automation towards a post-scarcity future was called a luddite - right up until that automation started threatening their (material) class. I mean, you don't have to look any further than the (justified) lack of sympathy to dockworkers just a few months ago: https://news.ycombinator.com/item?id=41704618 https://news.ycombinator.com/item?id=41704618 The solution is not, and never has been, to shack up with the capital-c Capitalists in defense of copyright. It's to push for a system where having your "work" automated away is a relief, not a death sentence.
        • uoaei 2y ago
          There's both "is" and "ought" components to this conversation and we would do well to disambiguate them. I would engage with those people you're stereotyping rather than gossiping in a comments section, I suspect you will find their ideologies quite consistent once you tease out the details.
• pockmarked19 2y ago
  Calling it a neural network was clearly a mistake on the magnitude of calling a wheel a leg.
  • cgearhart 2y ago
    This is an excellent analogy. Aside from “they’re both networks” (which is almost a truism), there’s really nothing in common between an artificial neural network and a brain.
    • runarberg 2y ago
      Neurons also adjust the signal strength based on previous stimuli, which in effect makes the future response weighted. So it is not far off—albeit a gross simplification—to call the brain a weight matrix. As I learned it, artificial neural networks were modeled after a simple model for the brain. The early (successful) models were almost all reinforcement models, which is also one of the most successful model for animal (including human) learning.
  • legacynl 2y ago
    I don't really get where you're coming from.. Is your point that the capabilities of these models have grown such that 'merely' calling it a neural network doesn't fit the capabilities? Or is your point that these models are called neural networks even though biological neural networks are much more complex and so we should use a different term to differentiate the simulated from the biological ?
    • juped 2y ago
      It was clearly a mistake because people start attempting to make totally incoherent comparisons to rat brains.
    • joe_the_user 2y ago
      The OP is comparing the "neuron count" of an LLM to the neuron count of animals and humans. This comparison is clearly flawed. Even you step back and say "well, the units might not be the same but LLMs are getting more complex so pretty soon they'll be like animals". Yes, LLMs are complex and have gained more behaviors through size and increased training regimes but if you realize these structure aren't like brains, there's no argument here that they will soon reach to qualities of brains.
• andrepd 2y ago
  NNs are in no way shape or form even remotely similar to human neural tissue, so your whole analogy falls there.
• legacynl 2y ago
  A little nitpick; a biological neuron is much more complex than it's ml-model equivalent. a simple weighted function cannot fully replicate a neuron. That's why it's almost certain that a biological brain with a billion synapses outperforms a model with a billion parameters.
  • mort96 2y ago
    Isn't that what they meant by this? > the comparison is not apples-to-apples, because each synapse is much more complex than a single parameter in a weight matrix.
    • daveguy 2y ago
      It isn't just "not apples to apples". It's apples to supercomputers.
    • legacynl 2y ago
      well yeah but it's un-obviously a very big difference that basically invalidates any conclusion that you can make with this comparison.
      • mort96 2y ago
        I don't think so: it seems reasonable to assume that biological neurons are strictly more powerful than "neural network" weights, so the fact that a human brain has 3 orders of magnitude more biological neurons than language models have weights tells that we should expect, as an extreme lower bound, 3 orders of magnitude difference.
  • joe_the_user 2y ago
    It's not a "nitpick", it's a complete refutation. LLM don't have a strong relationship to brains, they're just math/computer constructs.
  • Purplehermann 2y ago
    The brain is insanely energy efficient, this is not the same as intelligence efficient
• joe_the_user 2y ago
  This tech has made a big impact, obviously is real and exactly what potentials can unlocked by scaling is worth considering... ... but calling vector-entries in a tensor flow process "neurons" is at best a very loose analogy while comparing LLM "neuron numbers" to animals and humans is flat-out nonsense.
• fsndz 2y ago
  yes indeed. But I see more and more people arguing against the very possibility of AGI. Some people say statistical models will always have a margin of error and as such will have some form of reliability issues: https://open.substack.com/pub/transitions/p/here-is-why-there-will-be-no-agi?r=56ql7&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true https://open.substack.com/pub/transitions/p/here-is-why-ther...
  • rmbyrro 2y ago
    the possibility of error is a requirement for AGI the same foundation that makes the binary model of computation so reliable is what also makes it unsuitable to solving complex problems with any level of autonomy in order to reach autonomy and handle complexity, the computational model foundation must accept errors because the real world is not binary
    • sroussey 2y ago
      This really speaks to the endeavors of making non-digital hardware for AI. Less of an impedance mismatch.
• kerkeslager 2y ago
  > As of right now, we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and more. Uhh, yes we do. I mean sure, we don't know everything, but we know one thing which is very important and which isn't under debate by anyone who knows how current AI works: current AI response quality cannot surpass the quality of its inputs (which include both training data and code assumptions). > The number of neuron-neuron connections in current AI systems is still tiny compared to the human brain. And it's become abundantly clear that this isn't the important difference between current AI and the human brain for two reasons: 1) there are large scale structural differences which contain implicit, inherited input data which goes beyond neuron quantity, and 2) as I said before, we cannot surpass the quality of input data, and current training data sets clearly do not contain all the input data one would need to train a human brain anyway. It's true we don't know exactly what would happen if we scaled up a current-model AI to human brain size, but we do know that it would not produce a human brain level of intelligence. The input datasets we have simply do not contain a human level of intelligence.
• petesergeant 2y ago
  ... and any other answer is just special pleading towards what people want to be true. "What LLMs can't do" is increasingly "God of the gaps" -- someone states what they believe to be a fundamental limitation, and then later models show that limitation doesn't hold. Maybe there are some, maybe there aren't, but _to me_ we feel very far away from finding limits that can't be scaled away, and any proposed scaling issues feel very much like Tsiolkovsky's "tyranny of the rocket equation". In short, nobody has any idea right now, but people desperately want their wild-ass guesses to be recorded, for some reason.
• HarHarVeryFunny 2y ago
  > As of right now, we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and more. I don't think that's totally true, and anyways it depends on what kind of scaling you are talking about. 1) As far as training set (& corresponding model + compute) scaling goes - it seems we do know the answer since there are leaks from multiple sources that training set scaling performance gains are plateauing. No doubt you can keep generating more data for specialized verticals, or keep feeding video data for domain-specific gains, but for general text-based intelligence existing training sets ("the internet", probably plus many books) must have pretty decent coverage. Compare to a human: would a college graduate reading one more set of encyclopedias make them significantly smarter or more capable ? 2) The new type of scaling is not training set scaling, but instead run-time compute scaling, as done by models such as OpenAI's GPT-o1 and o3. What is being done here is basically adding something similar to tree search on top of the model's output. Roughly: for each of top 10 predicted tokens, predict top 10 continuation tokens, then for each of those predict top 10, etc - so for a depth 3 tree we've already generated - scaled compute/cost by - 1000 tokens (for depth 4 search it'd be 10,000 x compute/cost, etc). The system then evaluates each branch of the tree according to some metric and returns the best one. OpenAI have indicated linear performance gains for exponential compute/cost increase, which you could interpret as linear performance gains for each additional step of tree depth (3 tokens vs 4 tokens, etc). Edit: Note that the unit of depth may be (probably is) "reasoning step" rather than single token, but OpenAI have not shared any details. Now, we don't KNOW what would happen if type 2) compute/cost scaling was done by some HUGE factor, but it's the nature of exponentials that it can't be taken too far, even assuming there is aggressive pruning of non-promising branches. Regardless of the time/cost feasibility of taking this type of scaling too far, there's the question of what the benefit would be... Basically you are just trying to squeeze the best reasoning performance you can out of the model by evaluating many different combinatorial reasoning paths ... but ultimately limited by the constituent reasoning steps that were present in the training set. How well this works for a given type of reasoning/planning problem depends on how well a solution to that problem can be decomposed into steps that the model is capable of generating. For things well represented in the training set, where there is no "impedance mismatch" between different reasoning steps (e.g. in a uniform domain like math) it may work well, but in others may well result in "reasoning hallucination" where a predicted reasoning step is illogical/invalid. My guess would be that for problems where o3 already works well, there may well be limited additional gains if you are willing to spend 10x, 100x, 1000x more for deeper search. For problems where o3 doesn't provide much/any benefit, I'd guess that deeper search typically isn't going to help.
• slashdave 2y ago
  > we have no way of knowing in advance what the capabilities of current AI systems will be if we are able to scale them by 10x, 100x, 1000x, and more. Scaling experiments are routinely performed (the results are not encouraging). To say we know nothing about this is wrong.
• addcn 2y ago
  A huge % of animal synapses seem to contribute to motor control and signal processing. It’s interesting how a relatively small # of synapses can do all abstract reasoning when free from those concerns. Take the pre-frontal cortex, leave the rest.
• imtringued 2y ago
  I don't need more parameters. I need neural networks that learn as I use them.
• croniev 2y ago
  In comparing neural networks to brains it seems like you are implying a relation between the size/complexity of a thinking machine and the reasonability of its thinking. This gives us nothing, because it disregards the fundamental difference that a neural network is a purely mathematical thing, while a brain belongs to an embodied, conscious human being. For your implication to be plausible, you either need to deny that consciousnes plays a role in reasonability of thinking (making you a physicalist reductionist) or you need to posit that a neural network can have consciousness (some sort of mystical functionalism). As both of these alternatives imply some heavy metaphysical assumptions and are completely unbased, I'd advise to avoid thinking of neural networks as an analogue of brains with regards to thinking and reasonability. Don't expect they will make more sense with more size. It is and will continue to be mere statistics.
  • cs702 2y ago
    I'm not implying anything or delving into metaphysical matters. All I'm saying above is that the number of neuron-neuron connections in current AI systems is still tiny, so as of right now, we have no way of knowing in advance what the future capabilities of these AI systems will be if we are able to scale them up by 10x, 100x, 1000x, and more. Please don't attack a straw-man.