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My main argument against the AI doomsayers has so far been that the current scaling laws simply make runaway singularity style scenarios algorithmically impossi

• p-e-w 3y ago
  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. Then think of a human genius. Now consider how incredibly similar their brains are, despite the massive performance gap. It's not like one has 10x the number of neurons/synapses/connections etc. of the other. They're both healthy human brains, and you need powerful technology to even distinguish them structurally. Considering this, it seems perfectly possible that a model like GPT-4 is just a hair's breadth away from vastly superhuman performance. It certainly beats the average human at many tasks already. The gap between a moron and a genius is a lot larger than that between GPT-4 and a superhuman.
  • 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.
  • 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.
• vlovich123 3y ago
  I don’t think I follow this argument. AI has been dropping in costs and complexity the more engineering time is spent on it. It seems like the bottleneck is humans creating new AI techniques right? If an AI is capable of developing new AI techniques unsupervised, isn’t that by definition the singularity? Heck doesn’t even need to be unsupervised. If it can even do most of the heavy lifting for a human I feel like that would put us into runaway territory. Granted we’re a long way away from that and likely we’d need an AI that could come up with its own hypotheses for new AI models to make this truly minimal cost and that feels even farther away. But I don’t think I follow the claim that each improvement step change requires to much massive extra scaling as it seems not match what we’ve seen over the past few years (granted I could be misinformed - I’m applying a 10k view spectator and maybe my own mental model here is flawed).
  • doctor_eval 3y ago
    I consider the singularity to be the point at which the certainty of our predictions about our future becomes close to zero. By this definition I reckon we are already in the singularity. It’s not necessarily bad. The problem with the singularity is that that we can’t tell if it’s bad or not.
    • amoss 3y ago
      Certainty about predictions of the future has always been close to zero. If you take a person from an appropriate time and ask them what the future will look like in 1000, 100 or even 20 years then their predictions will bear little resemblence to what actually occurs. As humans we have a tendancy to make linear future predictions based on past observations. Over the timescales that matter significant effects occur from previously unseen kinds of events that become important via interactions with other events. The important measurement would be what the length of the event horizon - the length of time before our predictions rapidly decrease to zero certainty. In a singularity we would expect that length of time to decrease close to zero. What has it been historically? I would claim that 5-10 years is a difficult period to make meaningful predictions about. I think that 20 years has proven to be very difficult but possible in the past. I am unaware of any 100+ year predictions that have landed with better than random chance. Interestingly I think we are entering a period where 5 years will be the upper bound, and even predictions over shorter 2-3 year timespans are going to become difficult.
• deleted 3y ago
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• godelski 3y ago
  > if for each step of improvement you need 10x parameters and 100x training, you quickly run into a brick wall Btw, PALM2 has far fewer parameters than V1. > The largest model in the PaLM 2 family, PaLM 2-L, is significantly smaller than the largest PaLM model but uses more training compute. Our evaluation results show that PaLM 2 models significantly outperform PaLM on a variety of tasks, including natural language generation, translation, and reasoning. These results suggest that model scaling is not the only way to improve performance. From the leak we know that the large version is 340B parameters, compared to the original 540B parameters. From Table 2 in the document we see that the small version (unknown size) is on par with version 1. ML typically follows a cycle. Improve, distill, repeat. It is unfortunate that the big labs lead these efforts because many smaller labs try to work on the distill part in parallel (out of necessity) but works get rejected (due to lack of SOTA) or ignored. Like all research, we need to be careful and nuanced in our evaluations. There's a lot of hype and many trying to take advantage of the confusion and sell snake oil. I think AI/ML is and will continue to change our world, but we have to be careful to not let the salesmen dictate the conversations. PaLM https://arxiv.org/abs/2305.10403 https://arxiv.org/abs/2305.10403 PaLM 2 https://arxiv.org/abs/2204.02311 https://arxiv.org/abs/2204.02311
• BulgarianIdiot 3y ago
  Your premise is wrong. This has nothing to do with 10x-ing parameters. One could argue the current parameter sizes are good enough as we observe "large breadth, shallow depth" behavior from LLM and to some extent, diffusion models. This suggests the problem is the depth of inference, which is single pass "hot takes" for all language models right now, due to cost of inference and our limited understanding of what makes a model's response high quality. Yes, you don't need more parameters to increase the depth. You need to iterate, instead. Loop. Imagine programming if looping was not allowed, nor recursion, or not even defining functions and calling them. Everything you write runs at most once during program execution and that's it. This is what an AI model is right now during inference. One big flat, single-pass, directed acyclic graph. And soon it won't be. Research into Chain of Thought, Tree of Thought reveals this dimension. This means you can take existing models and make them perform much more complex tasks with much better precision, though various ways of letting them iterate. Think of how you'd perform if you always had exactly 5 seconds to answer a question. Now imagine if you have 5 minutes. 5 hours. 5 days. Lo and behold, turns out an AI isn't different in that aspect. We also need more iterations of training (on the same amount of data), we need larger context windows, and we need new architectures, like Meta's MEGABYTE, for example. Parameter count and data size could hypothetically have already hit a hard wall (they haven't) and AI will keep exponentially improving regardless. There's too much low hanging fruit and more grows by the nanosecond.
  • Henk0 3y ago
    This. So much this. I'm completely dumbfounded by obviously highly intelligent people consistently not getting this, and dismissing current generation AI systems as not being intelligent because they can't reliably solve massively complex problems in one go. Like anyone would expect a human programmer or researcher to just intuitively come up with a complex program, or the correct answer for a hard problem every time, instantly Human thinking and problem solving involves a lot of trial and error, iterative thinking, and sharing and discussing the problem with other humans. Processes that AI researchers are just now beginning to explore, with results like increasing reasoning ability by 900% in a recent paper. Every thinking human runs a near constant loop of thought, with no conscious control of which thought will appear next (we're very good at fooling ourselves that we have control though) We do have super-intelligences already, but they're severely handicapped by lacking a bunch of these - apparently fairly straightforward to implement - abilities, plus a few senses and the ability to directly effect change in the physical world (which really isn't needed if they can get access to human agents who will do their bidding, wittingly or unwittingly), and to self-improve. With regards to self-improvement, the increasing coding skills combined with iterative 'thought' loops should get there in very little time considering the current rate of progress There's also the idea that a single AI model should be able to do everything our human brains do, when our brains actually contain a number of specialised subunits that handle different aspects of our behavioural repertoire. It reasonable to allow for the same thing with an AI system, where specialised sub-networks handle input, output and other subtasks. AI systems also have the advantage of being able to add any arbitrary number of subunits to increase its capacity to solve various problems We seem to suffer from a species-wide narcissism with regards to our own intelligence and capabilities, and there's this huge focus on the number of connections in the human brain – most of which deal with things that are by no means necessary to act on the world unless one has a meat body and the need to navigate social situations, make friends and mate. Fact is, we have terrible short-term memory (worse than chimpanzees), slow processing time, lots of cognitive heuristics, many of which cause more harm than good in the modern world. We are emotional and easily fooled. Even the most intelligent people historically have believed in what we now consider fairy tales. We are slow to take in information, bad at storing it, and generally bad at transmitting it. A few of us can generate great ideas – building on accumulated knowledge from our forebears and peers – but most of us are just not that great at coming up with anything original or useful I've been actively looking for good arguments against AGI being much closer than we should be comfortable with, and reasons why we should not fear systems that surpass us in intelligence. All I've come across so far is some combination of the above, often expressed with a dismissive attitude, disparaging current LLM:s as parrots (that can apparently reason on the level of university level humans, but much more quickly), and pejorative terms like fearmongerers and doomers to describe those of us who really don't think its a good idea to pursue more intelligent systems. My guess is these people will act surprised when the arms race inevitably leads to some very bad unintended consequences. I don't see a way to stop it though, so I'm just strapped in for the ride along with the rest of humankind Again, if you have good arguments against any of the points above, please do share them with me
• ummonk 3y ago
  Do you believe that Moore’s Law (or rather Huang’s Law) will stop being true before AI exceeds human capabilities by orders of magnitude? We don’t need a runaway singularity for AI to just render human intelligence obsolete.
• BorisTheBrave 3y ago
  I think you've misunderstood. Megabyte scales better with context window length. I don't know if they're saying the training data / compute are any more efficient.
• primax 3y ago
  Honestly, I welcome the talent working on AI now. They were working on how to make me spend 5 more seconds on Facebook, or how to click on a Google ad. AI has potentially huge positive productivity potential.
  • lostmsu 3y ago
    For all you know they might now be working on how to make you spend the rest of your life in a coal mine for AI overlords.
    • mkaic 3y ago
      If the AI gets that smart, I'd hope it could just build robots to mine the coal for it.
  • digging 3y ago
    Why do you think the best AI isn't going to be used to work on how to make you spend 5 more seconds on Facebook?
• i2cmaster 3y ago
  [flagged]
• iinnPP 3y ago
  This assumes only the LLM matters. I don't think it does. I think GPT3.5 is plenty, if not overkill. I can't imagine I am alone in my thought. I've even seen some other experiments in the same line of reasoning. Meta seems to be thinking the same thing, or at least closer to it than the majority.
• lazzlazzlazz 3y ago
  10x scaling won't take long, even putting aside improvements in our understanding of training processes.