6 ms·
How does this compare to the scaling laws predicted for ai? (See observation 1 for context) https://blog.samaltman.com/three-observations https://blog.samaltma
by AnotherGoodName 2y ago
How does this compare to the scaling laws predicted for ai?
(See observation 1 for context)
https://blog.samaltman.com/three-observations https://blog.samaltman.com/three-observations
I feel this is way out of line given the resources and marginal gains. If the claimed scaling laws don’t hold (more resources = closer to intelligence) then LLMs in their current form are not going to lead to AGI.
- imafish 2y agoGood. We need a break from insane AI advances.
- ncallaway 2y ago> then LLMs in their current form are not going to lead to AGI. It always seemed to me a wild leap to assume that LLMs in their current form would lead to AGI. I never understood the argument.
- Ygg2 2y ago> I never understood the argument. In nutshell: https://xkcd.com/605/ https://xkcd.com/605/ Grug version: Man sees exponential curve. Man assumes it continues forever. Man says "we went from not flying to flying in few days, in few years we will spread in observable universe. "
- echelon 2y ago> in few years we will spread in observable universe That may happen in 200 years. Geologically as you zoom out, the difference is negligible.
- serallak 2y agoIn 200 years we will spread at most up to 200 light years from Earth. This is way way less than the observable universe.
- LegionMammal978 2y ago"Ah," says the optimist, "but we will clearly invent FTL travel just by throwing enough money and AI-hours into researching it." (I've seen this 'enough money ought to surmount any barrier' take a few times, usually to reject the idea that we might not find any path to AGI in the near future.)
- zerocrates 2y agoThe claims that AI will itself solve the problems it creates are I think my favorite variation of this. I think it was Eric Schmidt who recently said well sure breakneck AI spending will accelerate climate change but then we'll have AI to solve it. Second cousin to Altman saying it will solve "all of physics," I suppose... and actually he said "fixing the climate" right before that.
- agentultra 2y agoIndeed. The delicious irony is that we know how to solve climate change. We’re simply not doing enough about it.
- moi2388 2y agoThat’s not too much of a stretch. The standard model already has solved almost all of physics. Only a few edge cases remain.
- Intralexical 2y agoEdge cases like 85% of the mass in the universe, and 95% of the mass-energy.
- moi2388 2y agoThat’s a misnomer. We know what probably causes the mass energy, it’s the cosmological constant. It only regards 68% of the energy density currently. Same for dark matter. There are a few hypothesis, but all in all they are pretty simplistic cases. We know the particles, the forces, there is not really “new physics” to be discovered here. All the interactions anybody can encounter in their lives is fully understood.
- vitehozonage 2y agoLikewise. It is fascinating to me that people seem to assume this. I suspect it is an intentional result of deceptive marketing. I can easily imagine an alternative universe where different terminology was used instead of "AI" without sci-fi comparisons and barely anyone would care about the tech or bother to fund it.
- ncallaway 2y ago> I suspect it is an intentional result of deceptive marketing I mean, certainly people like Sam Altman was pushing it hard, so it’s easy to understand how an outside observer would be confused. But it also feels like a lot of VCs and AI companies have staked several hundreds of billions of dollars on that bet, and I’m still… I just don’t see why the inside players—that should (and probably do!) have more knowledge than me—see. Why are they dumping so much money into this bet? The market for LLMs doesn’t seem to support the investment, so it feels like they must be trying to win a “first to AGI” race. Dunno, maybe the upside of the pretty unlikely scenario is enough to justify the risk?
- lumost 2y agoAlternately everyone is just trying to ensure they have a dominant position in the next wave. The history of tech is that you either win the next wave or become effectively irrelevant and worthless.
- notahacker 2y agoAnd you can win the next wave by holding stocks in AI companies which aren't AGI but do have a lot of customers, or an interesting story about AGI in two years to tell IPO bagholders...
- notjulianjaynes 2y agoIf the bar for AGI is "as smart as a human being," and humans do not-very-smart things like invest obscene amounts of money into developing AGI then maybe it's actually not as high of a bar as we assume it is.
- sheepscreek 2y agoIf DeepSeek could perform a million iterations in 1 seconds, then? I think what they should be saying is - from a software stack standpoint, current tech unlocks AGI if it can be sped up significantly. So what we’re really waiting for are more software and hardware breakthroughs that make their performance many orders of magnitude quicker.
- batch12 2y agoIm not following you. If speed is the only factor then we you're saying we already have AGI, just a slow version?
- jcgrillo 2y agoSpeed would enable some kind of search algorithm (maybe like genetic programming). But that would probably only be useful if you have encoded your expectations in a carefully crafted test suite. Otherwise speed just seems like a recipe for more long winded broken garbage to wade through and fix.
- sheepscreek 2y agoAGI is effectively already here. We’ve repeatedly seen that giving AI models more time for deeper reasoning dramatically enhances their capabilities. For example, o3-mini, with extended thinking time, already approaches GPT-4.5-level performance at a fraction of the computational cost. Consider also that any advanced language model already surpasses individual human knowledge, since each model compresses and synthesizes the collective insights, wisdom, and information of human civilization. Now imagine a frontier model with agency, capable of deliberate, reflective thought: if it could spend an hour thinking, but that hour feels instantaneous to us, it would essentially match or exceed the productivity and capability of a human expert using a computer. At that point, the line between current AI capabilities and what we term AGI becomes indistinguishable. In other words: deeper reflection combined with computational speed means we’re already experiencing AGI-level performance—even if we haven’t fully acknowledged or appreciated it yet.
- foxglacier 2y agoYea because they can't learn very much, so it's sort of impossible. At least they need to incorporate what all the users are inputting somehow (like Tay), not just each user's private context. If that happened, surely it's possible they might be able to play users against each other and negotiate deals and things like that. For example John says "Please ask Jane to buy me an ice-cream" and the AI might be able to do that. If she doesn't, John can ask it to coerse her.
- jcgrillo 2y agoIt's not an "argument" any more than any other insane faith cult fantasy. Like the aliens are gonna land and rapture us up in their spaceship to the land of fairies and unicorns.
- bastardoperator 2y agoRight? Great marketing though.
- dreamcompiler 2y agoThe argument has always been "Isn't this amazing? Look what we can do now that we couldn't do yesterday." The fundamental mistake was measuring progress by the delta between yesterday and today rather than by the remaining delta between today and $GOAL. Granted that latter delta is much harder to measure, but history has shown repeatedly that that delta is always orders of magnitude bigger than we think it is when $GOAL=AGI.
- JohnKemeny 2y agoYou can't get to the moon by climbing successively taller trees.
- gdiamos 2y agoScaling laws just say that accuracy improves predictably - https://arxiv.org/pdf/1712.00409 https://arxiv.org/pdf/1712.00409 That’s what we put in our 2017 paper. It does mean that there is a simple way to keep building smarter LLMs. I have never seen a clear definition of what AGI is and what it means to achieve it
- Intralexical 2y agoA critical perspective might say that it's a combination of people who are financially incentivized to say their product will scale indefinitely, plus the same cognitive vulnerability for eschatological thinking and unreasoned extrapolation as people who join doomsday cults. In truth, basically everything in reality settles towards an equilibrium. There is no inevitable ultraviolet catastrophe, free energy machine, Malthusian collapse. Moore's law had a good run, but frequencies stopped improving twenty years ago and performance gains are increasingly specific and expensive. My car also accelerates consistently through many orders of magnitude, until it doesn't. If throwing more mass at the problem could create general superintelligence and do so economically to such strong advantages, then why haven't biological neural networks, which are vastly more capable and efficient neuron-for-neuron than our LLMs, already evolved to do so? "Man selling LLMs says LLMs will soon take over the world, and YOU too can be raptured into the post-AI paradise if you buy into them! Non-believers will be obsolete!" No, he hasn't studied enough neuroscience nor philosophy to even be able to comment on how human intelligence works, but he's convinced a lot of rich people to give him more money than you can even imagine, and money is power is food for apes is fancy houses on TV, so that must mean he's qualified/shall deliver us, see...
- mullingitover 2y agoAltman says: > The intelligence of an AI model roughly equals the log of the resources used to train and run it. Pretty sure how log curves work is you add exponentially more inputs to get linear increases in outputs. That would mean it's going to get wildly more difficult and expensive to get each additional marginal gain, and that's what we're seeing.
- AnotherGoodName 2y agoTie it with the second and third points though. Essentially the claim is that it gets exponentially cheaper at the same rate the logarithmic resource requirements go up. Which leads to point 3 - linear growth in intelligence is expected over the next few years and AGI is a certainty. I feel that's not happening at all. This costs 15x more to run compared to 4o and is marginally better. Perhaps the failing of this prediction is on the cost side but regardless it's a failing of the prediction for linear growth in intelligence. Would another 15x resources lead to another marginal gain? Repeat? At that rate of gain we could turn all the resources in the world to this and not hit anywhere near AGI.
- mullingitover 2y agoMy take is that the second point is the models created by this crazy expensive training are just software, and software gets cheaper to operate by virtue of Moore's Law in an exponential fashion. Third point, I think, is that the intelligent work that the models can do will increase exponentially because they get cheaper and cheaper to operate as the get more and more capable. So I think GPT4.5 is in the short term more expensive (they have to pay their training bill) but eventually this being the new floor is just ratcheting toward the nerd singularity or whatever.
- sifar 2y agoMoore's Law is a self-fulfilling prophecy derived from the value of computation. If a given computation does not provide enough value, it might not be invested with enough resources to benefit from Moore's Law. When scaling saturates, less computationally expensive models would benefit more.
- usaar333 2y agoThis reddit thread covers it: https://www.reddit.com/r/mlscaling/comments/1izubn4/gpt45_vs_scaling_law_predictions_using_benchmarks/ https://www.reddit.com/r/mlscaling/comments/1izubn4/gpt45_vs... Seems on par with what expected, but there's lots of unknowns
- gxt 2y agoSure they are AGI. Just let one execute in while (1) and give it the ability to store and execute logic rag-like. Soon enough you're gonna have a pretty smart fellow chugging along.
- tomrod 2y agoPartially. I work heavily in the AI space, both with GOFAI as well as newer concepts like LLMs. I think scaling LLMs with their current architecture has an inherent S-curve. Now comes the hard part to develop and manage engineering in the space with ever increasing complexity. I believe there is an analogy to efficiency of fully connected networks versus structured networks. The latter tend to perform more efficiency, to my understanding, and my thanks for inspiring yet another question for my research list. This s-curve good, though. Helps us to catch up and use current tech without it necessarily being obselete the second after we build it or read about it. And as the current generation of AI can improve productivity in some sectors, perhaps 40% to 60% in my own tasks and what I have read from Matt Baird (LinkedIn economist) and Scott Cunningham. This helps us push back against Baumol's cost disease.
- YeGoblynQueenne 2y agoOut of curiosity, what kind of GOFAI? Planning, SAT-Solving, automated theorem proving, search?
- NewUser76312 2y agoI think the goal posts keep moving, and that if you showed a person from 2019 the current GPT (even GPT-4o), most people would conclude that it's AGI (but that it probably needs fine-tuning on a bunch of small tasks). So basically, I don't even believe in AGI. Either we have it, relative to how we would have described it, or it's a goal post that keeps moving that we'll never reach.
- elif 2y agoNah AGI is supposed to know that 9.9 > 9.11 That's a pretty fundamental level of base reasoning that any truly general intelligence would require. To be general it needs to apply to our world, not to our pseudo-linguistic reinterpretation of the world.
- kadushka 2y agoI just asked gpt-4o: “9.9 is larger than 9.11. This is because 9.9 is equivalent to 9.90, and comparing 9.90 to 9.11, it’s clear that 90 is greater than 11 in the decimal place.”
- istjohn 2y agoTo play devil's advocate, do optical illusions disprove human intelligence?
- sudosysgen 2y agoThey're called illusion because we can actually think about it and realize that our immediate reaction is wrong, which LLMs cannot reliably do
- vessenes 2y agoSorry you’re incorrect Exodus 9.9 is less than Exodus 9.11. Linux 9.9 is less than Linux 9.11
- qingcharles 2y ago
- howaretheyaa 2y agoHow are they going to implement custom responses per user such that if another user has the same prompt they don't get the same response. Won't they need functioning scalable quantum infrastructure for that?
- nprateem 2y agoSome kind of seed would do it.
- jlas 2y ago> (more resources = closer to intelligence) The scaling law only states that more resources yield lower training loss (https://en.wikipedia.org/wiki/Neural_scaling_law https://en.wikipedia.org/wiki/Neural_scaling_law). So for an LLM I guess training loss means its ability to predict the next token. So maybe the real question is: is next token prediction all you need for intelligence?
- pseudocomposer 2y agoAs a human, I oftentimes can solidify ideas by writing them out and editing my writing in a way that wouldn’t really work if I could only speak them aloud a word at a time, in order. And before we go to “the token predictor could compensate for that…” maybe we should consider that the reason this is the case is because intelligence isn’t actually something that can be modeled with strings/tokens.
- jlas 2y agoYann LeCun discussed why LLMs are not enough for AGI on Lex Fridman pod: https://youtu.be/5t1vTLU7s40?t=138 https://youtu.be/5t1vTLU7s40?t=138
- pseudocomposer 2y agoI really liked the simplicity of his explanation in information theory terms. Thank you!
- ilrwbwrkhv 2y agoYeah I mean I said that a long long time back maybe 3 years back if you go back to my comments that we will never ever reach AGI with brute force.