9 ms·

I don't buy the idea (with either architecture) that "10x"-type scaling is required for another breakthrough. Think of a human with below average intelligence.

• anon291 3y ago
  > It certainly beats the average human at many tasks already This is not the revolution you think it is. ```python > 338383*887282 ... ANSWER ... ``` Yet skynet never came.
• nemothekid 3y ago
  >Think of a human with below average intelligence. Then think of a human genius. LLMs are not AGI. A human with below average intelligence is still a league above a chimpanzee. A chimpanzee will never be able to read, not because "it's too dumb", but because a chimp's brain lacks the actual hardware for reading. The LLM is the chimpanzee. The gap between an LLM and a "human with below average intelligence" is far more than 10x.
  • epups 3y ago
    By what criteria? In several professional capacities, including coding, LLM's are far superior to an average human already.
    • slowmovintarget 3y ago
      No they're not. They're superior at specific tests when prompted. But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works.
      • CamperBob2 3y ago
        But asking for a complete application nearly always fails, when an average human that knows programming should be able to make one that works. "This talking dog is a dumbass. The risotto recipe he gave me sucked, and the C++ code he wrote is full of security holes. I don't see what all the hype is about." Hint: ML will get better. Humans will not. ("Slow moving target," indeed.) What we're learning is just how many aspects of our vaunted 'intelligence' are really just features of our languages and the graphs they encode.
        • riffraff 3y ago
          This might be true but it's irrelevant to the claim "LLMs are already better than humans".
          • CamperBob2 3y ago
            You are fixated on the current state of the art, when the first couple of time derivatives are what actually matter. The assertion in question is already true in a limited sense, and it's only going to go in one direction from here.
          • ben_w 3y ago
            Google Translate, which IIRC is also a Transformer model like GPT these days, knows more natural languages than I can remember the names of, to a higher standard than I know my best non-native language, and I moved to Germany 5 years ago. GPT-3 knows most programming languages better than I do, even though I literally learned to read with the Commodore 64 user manual back in the 80s and haven't stopped being a nerd since; and while GPT code isn't always correct or even compilable, it's not like I don't still make mistakes that cause compilation to fail a few times a day, and there's a reason we all insist on testing code rather than just assuming it will work when a dev stops typing. Again, I acknowledge GPT-3.5 (and I assume 4 but have not used it) is not perfect: like others I'd call 3.5 a "junior developer" (and it isn't even that good in every subject!); yet, despite that, the only domains where I can regularly beat it at those where my perception of reality is fundamentally different from its perception (how words sound and how numbers are composed, so it's relatively bad at arithmetic and rhyme), or where it has been forced to be bad (ask it to write about conflict, my experience is everyone reconciles and lives happily ever after). But in most cases it would take me years to get as good as it already is. And there are more of those subjects than I can remember the names of, too.
  • p-e-w 3y ago
    > The gap between an LLM and a "human with below average intelligence" is far more than 10x. In which direction? GPT-4 passes the bar exam with a top 10% score. How do you think a human with below average intelligence (or even with average intelligence) would fare? Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that?
    • intelVISA 3y ago
      > Copilot generates programming code that solves problems, and in most cases the code is correct. It outperforms many junior professional developers. Do you think a human with below average intelligence could do that? Absolutely, it's why Javascript is so popular.
      • p-e-w 3y ago
        A human with below average intelligence can outperform many junior professional software engineers because of JavaScript? Do you mean a programmer with below average intelligence (for a programmer)? Because I'm having a hard time believing that you actually believe what you wrote. BTW, JavaScript is actually a fairly difficult language to learn and use. Python, BASIC, and even Fortran are much simpler conceptually and have far fewer pitfalls. JavaScript is popular because it's the only language that every modern computer has an interpreter for, not because it's so easy that idiots can use it.
        • arcanemachiner 3y ago
          It's gotta be a low-effort joke.
        • intelVISA 3y ago
          It was a bit tongue in cheek I'll confess. :)
    • nemothekid 3y ago
      >How do you think a human with below average intelligence (or even with average intelligence) would fare? I don't understand what rote memorization to pass a test has to do with intelligence. For the record Kim Kardashian passed the bar; I imagine anyone given the proper motivation and time to study could do it, it's not a hard test. If AGI is a computer passing a test, then AGI was achieved a long time ago. I don't think a human with below average intelligence could multiple 2 very large primes but I don't mistake my calculator for intelligence. A below average intelligence human can drive a car with a couple hours of training, an LLM can't do that (with a far lower power budget as well). I'm not saying AGI is impossible, but it's clear LLMs are not AGIs; it's not a matter of having a 100x more powerful LLM, just like making an Ape better at sign language won't make them better at abstract reasoning. An Ape's brain fundamentally lacks the mental machinery for higher level things that humans do. It's not a question of not being smart enough. LLMs are simply one component of the human mind.
  • ithkuil 3y ago
    The key idea behind the current wave of AI is that the necessary "structure" will be created through training by sheer amount of data. The idea has been dismissed for decades but it turned out one of the most effective ways to get practical results. Now, can and should all structure necessarily come from pure training? Is there a better "seed" structure that can make models more effective? Is there some other bits that are missing that are not in the microstructure but in the way the model is connected to the external world and itself (feedback loops etc)?
  • qup 3y ago
    In your metaphor, maybe what the LLM (chimpanzee) "brain" is missing is some things like an inner thought loop, long term and short term storage and context, etc. Pieces we can understand, build, and surround the LLM with. Given the right arrangement, and the correct "abracadabra," perhaps that gets to where we are. It seems to me that LLMs are perhaps capable of making up the inner thought loop, and the rest of the obvious systems seem doable. The context seems to be the hard problem; pulling in the proper things, and giving them the correct amount of attention.
    • pixl97 3y ago
      >pulling in the proper things, and giving them the correct amount of attention. This is likely one of the harder problems to solve. One of the strengths humans seem to have is the power of analogy. These analogies quite often can lead us into new paths of thought, or at least in the correct direction. How do you do this in a machine system without wasting tons of power chasing dead ends, not sure?
  • ummonk 3y ago
    From the origins of the first sentient life on Earth half a billion years ago, our common ancestor with the chimpanzees was 98% of the evolutionary timespan on the way to humans. Do you really want to bank on that last 2% being difficult to match in computers?
  • fnordpiglet 3y ago
    LLM are a single model. Multimodal LLM tightly integrated with classical AI techniques to inform, conform, compel, solve, optimize, etc in a tight feedback loop…. I think folks are too entranced by the abilities of LLM and too jaded to realize the flaws are in domains we’ve already achieved great success. the magic really comes when we combine them with classical AI techniques. I don’t think you need more than GPT4 level LLM, what you need is goal based agency, information retrieval, optimizers, solvers, etc. These are things we’ve already created. The magic comes not from bigger LLM but from integration.
• visarga 3y ago
  > I don't buy the idea (with either architecture) that "10x"-type scaling is required for another breakthrough. Scaling can happen in two dimensions - model size and dataset size. What counts is the product of n_examples x n_parameters. That's why we have the super-Chinchilla laws, where n_examples >> 20*n_parameters. Scale the data, keep the model lean. Not to mention dataset quality - if you got clean diverse data you need less of it. Another important trend is using LLMs to generate training sets. Example: ConstitutionalAI (pure RLAIF), TinyStories (scaling down LLMs), Alpaca (borrowed RLHF), AlpacaFarm - a recent paper promising fine-tuned models for $200 cost in 24h.
  • BulgarianIdiot 3y ago
    Scaling can happen in many other places. Such as deeper iterative thought during inference such as Chain of Thought, Tree of Thought, which extracts increasingly better performance out of existing parameter and data sizes. Tried to explain here: https://news.ycombinator.com/item?id=36054809 https://news.ycombinator.com/item?id=36054809
• wudangmonk 3y ago
  You mean the average brain with about 100 billion neurons with about 1000 connections each bringing it to around 100 trillion connections. With an estimated 1000 "AI" neurons required per biologial neuron. I don't think you are givin these "below average" intelligence individuals enough credit. What we consider a genius is the equivalent of a dog show obstacle course. We measure intelligence/genius as whatever is hard for humans and completely ignore what is easy because we fail to see the complexity behind the easy stuff.
  • deleted 3y ago
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  • mirekrusin 3y ago
    Nobody said that nature is optimal. Wheels are trivial, however not present in biology. Nature creates tentacles, not jet engines, nuclear energy etc. Majority of human brain computation is spent on things that are simply not necessary for computer models (how to wiggle limbs, mouth, eyes etc). Current LLM are impressive, but we know they can be much more efficient - we're using very low quality training data, we don't use any methods to question training input/evaluate it against current knowledge etc. Our current language models are based on reciting/memorization/force-feeding, not true learning. Computer models have massive underlying advantage of working on CPU/GPUs where they can be modeled, cloned, retrained, have binary accuracy, can integrate with specialized code instantly, have access to massive memory storage, they are insanely fast and precise etc. Looking at it from first principles, there is no reason to think that near optimal runtime should not be more efficient than human brain on currently available computers. We don't need to simulate full brain just like we didn't have to create super fancy legs to go to the Moon.
    • baq 3y ago
      > Nobody said that nature is optimal. Wheels are trivial, however not present in biology. Nature creates tentacles, not jet engines, nuclear energy etc. Wheels are trivial but useless without bearings. Bearings most certainly aren't trivial. All the things you listed further are dependent on bearings somewhere.
• raincole 3y ago
  > Now consider how incredibly similar their brains are, despite the massive performance gap. A disk filled with random bits is "similar" to a disk that stores the whole Wikipedia's text content. So writing the whole Wikipedia is an effort of a hair's breadth...?
• beefield 3y ago
  > seems perfectly possible that a model like GPT-4 is just a hair's breadth away from vastly superhuman performance Sorry, can't help commenting the weirdness of high dimensional spaces. If you take an cube of a size of a hair breadth (100um) in a space of hindreds of billions of dimensions like gpt4, distances between random points in that cube are in the order of tens of meters...
• somewhereoutth 3y ago
  However, that is to equivocate an LLM with a human brain. They both can be conceptualised with the idea of the 'neuron' (though with wildly different actual implementations of that term), but that is their only point of comparison. Thus your conjecture is invalid.
• DrScientist 3y ago
  > Considering this, it seems perfectly possible that a model like GPT-4 is just a hair's breadth away from vastly superhuman performance. Except that structurally the brain is clearly has vastly more capacity than the GPT-4 model. So sure one brain doesn't look that much different to the other - and it's in the details of the learning, wiring. But the brain, looks vastly different from a GPT-4 model in terms of capacity - with trillions of connections - with each connection and internal state being more subtle as well. > vastly superhuman performance In terms of specific tasks computers ( whether you write the program explicitly or it's learnt by tweaking params in a network ) have been there for decades. So the question is really around which tasks can you apply computers successful to. Neural nets are allowing programs to be written that weren't possible be hand. I find it amusing that people worry about ChatGPT etc al putting programmers out of a job, when it already has in the sense that CHapGPT is a program that was built by another program already.
  • twobitshifter 3y ago
    I’ve heard that the human brain may have more connections but with AI we’ve made something that is more efficient. If we make it a hardware issue, if provided with the brains’ capacity, what would AI look like?
  • lostmsu 3y ago
    > brain is clearly has vastly more capacity I would not be so sure about "clearly" bit. Brain has 100B neurons x 1000 connections. GPT3 has 175B connections, but to implement operations used in those connections Nvidia H100 uses about 5M transistors, which in human brain would have to be copied to each connection because nature didn't invent software. (assumes one needs one full CUDA core to implement necessary ops, and that H100's 80B transistors are evenly divided between its 16k cores)
    • DrScientist 3y ago
      Your argument only makes sense if you consider GPT3 equivalent to the human brain - yep the brain has more neurons - but it's also doing a heck of a lot more. Also each neuron can hold much more state than a transistor. For example they can respond to the timing of incoming events without having to build that capability with recurrent connections etc. In addition the neural connections themselves have properties. > because nature didn't invent software. Eh? How do you explain the fact that brains can learn and aren't a fixed input, output engine? Neural nets are software - just ones programmed by trial and error. Similarly for the brain.
• woah 3y ago
  The whole idea of the singularity is that AI starts improving itself. Until that happens, normal human progress in the LLM field is just normal human progress, and will probably follow a similar path of human progress where there's a breakthrough followed by lots of low hanging fruit and hype, then a plateauing and refinement/productization. LLMs can help humans develop new LLMs faster, but mostly in implementation (CoPilot and ChatGPT), and that's not really the important part. I have yet to see an LLM come up with original ideas. Given that training data seems to be a big bottleneck, and LLMs are really good at generating text, I think that maybe we can start to talk about the possibility of "singularity" once LLMs are able to generate their own training data that increases their abilities. After all, humans are able to do this. That is the history of human knowledge.