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This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Or, as the author puts it, "we are at the tail end of t

This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Or, as the author puts it, "we are at the tail end of the first wave of large language model-based AI... [it] ends somewhere in the next year or two with the kinds of limits people are running up against." I want to know only one thing, which is what gives him the confidence necessary to say that. If that one statement is untrue, the thesis of the piece completely fails, and I do not know how any one person alive today can be certain that that statement is true. Has Paul Kedrovsky or Eric Norlin spent $150M training a 2T parameter model that no one's heard about? Do they have access to classified OpenAI docs which state that GPT5 exists already, and it's no better than GPT4? Without this sort of information, "LLMs are not going to get smart enough for widespread practical use" is an unsubstantiated bet, and nothing more.

user3939382 3y ago
I asked ChatGPT how to add a JSDoc type to a Vue 2 prop and it gave me a wrong answer. There have been several times where I’ve asked it questions and it sprinkles in well disguised misinformation. These tools are impressive but they definitely have limitations.
- golol 3y ago
  ChatGPT (3.5) is not state of the art
- seanthemon 3y ago
  Curious, how does it do when you give it examples / use playground.openai? I find that no examples often leads to the same result as a befuddled junior, but with examples often it gains confidence. Also I find playground to give me much better code snippets than chat sometimes
  - cbozeman 3y ago
    You've identified the problem inherent in these models right now, though. There's a lot of "extraneous" information and details that appear useless and unrelated, but that long-time developers have tucked away in their brains (or really anyone who has done something at a high level for a long time), that turns out to be incredibly useful; generally these people don't require examples - they just know what the right answer is, because they've been exposed to a variety of problems over a long career or lifespan. That's where LLMs need to be to be truly "useful". I think if we get them to that point, we'll really have something useful on our hands.
    - dontupvoteme 3y ago
      That's where good money and societal upheaval will come up, private models for corporations trained on their entire codebase and documentation. Both young and old shall feel the pain then.
      - HPsquared 3y ago
        The codebase and documentation usually don't capture the full context of the development process. There is still a lot that isn't written down.
        dontupvoteme 3y ago
        I agree, but the MBAs will probably not.
    - seanthemon 3y ago
      Very insightful point - do you think that OpenAI and Microsoft are aiming at developers so that we may spill this knowledge by N-shotting the chat query? I.e if the AI doesn't understand some fundamental knowledge, often i'll teach it so that it can be more effective at helping me, inadvertently I spill some of this tucked knowledge.
      - cbozeman 3y ago
        I don't think they have any desire to use this knowledge for their own gain, since they explicitly say that your data will not be used to train their future models - both OpenAI is saying that about their trainable ChatGPT 3.5 Turbo model, and Azure OpenAI Service was clear on that as well. I think the future is extremely well-trained base models that are then fine-tuned on specific domain knowledge. I'm already seeing that with Meta's Llama 2 personally at home. My company has access to Azure OpenAI Service's GPT-4 trainable model that we've been working with on all our documentation, and the results look very promising so far. But, this idea of the All-Knowing Oracle that can answer any question you have is all well and good, but it's so far beyond our current processing power as to be a pointless endeavor at the moment. Will we get there eventually? Yeah, sure. According to Jim Keller as of around 2019/2020 when he was speaking on Lex Fridman's podcast, he said we still have room to go 1,000,000 times smaller - meaning chips. I think we probably need two more orders of magnitude in processing power of the strongest high-end GPUs before we're there. NVIDIA's H100 is a great achievement, but we need something about 100x as fast, and I think we'll still need dozens of them working in parallel to train the model that can answer all these questions. All of this though is a moot point, because it's the lawyers that have already slowed down progress. OpenAI is terrified of being sued. Altman couches it behind terms like, "AI Safety", "AI Alignment", etc., but it's fear. It's all stemming from fear. And it's all stemming from people just not "getting it". We're entering a new age of upheaval, and there's going to be rogue AIs that tell people to go kill themselves to reduce climate change. You know why? Because there are humans that tell people to go kill themselves to reduce climate change. These models are language models. We taught them how to think, and we taught them how to think like us, so it's no surprise to me whatsoever that they behave like us - meaning they occasionally lie and they occasionally go off the rails and go a little crazy. We have become gods and we have made a creation in our own image. Most of the time it's awesome, sometimes it's a little wild and wacky.
- jasfi 3y ago
  Are you using GPT-4? If not, it's understandable. If you don't pay for ChatGPT, you get GPT-3.5. You can also get access to GPT-4 if you use the playground.
  - andrepd 3y ago
    Why is it always the same reply? Yes, GPT4 is as useless as GPT3.5 on any non trivial task.
    - input_sh 3y ago
      My guess is people feel the need to self-justify their $20/month subscription.
      - aenis 3y ago
        My guess is most people into AI don't even remember that they are paying a $20/month for this. We do a lot of experiments involving gpt3.5, 4, claude-v2, titan-large, and palm2, and for what it's worth, on our real production workloads gpt4 shines. We can make Palm2 produce decent results with a lot of extra effort, and claude-v2 is passable but gpt4 does not disappoint. This is low-grade knowledge management stuff, and we are not using it as a information-retrieval system - but for basic 'cognitive' tasks where all the information needed is provided in the prompt. I'd not rely on it for info retrieval tasks such as the examples quoted above - its knowledge base is highly compressed, after all.
    - Kiro 3y ago
      > Why is it always the same reply? When there's one definitive answer to something that people keep repeating there's a slight chance that it's actually true. Shocking, I know.
      - andrepd 3y ago
        Well but it's not. Also, your reasoning would imply e.g. Qanon is true based on the number of people repeating the same thing, so that's pretty weak.
- jstummbillig 3y ago
  Everything has limitations. The linked piece is very much a statement about limited potential, not the current state. In this context, this stance is akin to saying "I asked a 6yo this math question and it gave me a wrong answer, there's definitely limitations". Sure – but who would use that observation to assess where the future potential cap of the kid is?
- ryoshu 3y ago
  True. I can't do a full table scan on an non-indexed column in a RDBMS with billions of rows on a single machine without it grinding to a halt. Tools have limitations.
  - dclowd9901 3y ago
    Disingenuous: given the proper processing, you’d get a 100% deterministic and reliable answer. How do you get that out of an LLM? What tool is any good if it doesn’t work 100% of the time predictably?
    - dontupvoteme 3y ago
      Right now one generation is expensive on these big mainframe models. Query many times on the right model(s) for the question and the correct answer will be there 99.999% of the time as the other hallucinations will be thrown out.
    - saberience 3y ago
      How many tools do you know of that work 100% of the time. As someone that’s worked in software 20 years now, I don’t think there is anything from Linux to eMacs to visual studio to Java which doesn’t have bugs, issues, crashes, shitty stuff, and yet we expect a revolutionary and amazing technology to generate code perfectly? It’s funny those the LLM haters keep raising the bar to a level that no other software can reach. Chatgpt is a tool like any other and just like a hammer, it can be misused, or it can be incredibly useful if used well. I personally find chatgpt mind boggling and astounding and use it every day multiple times for both coding and non coding purposes. But it’s totally normal and reasonable to me to expect bugs, do you really expect visual studio to run on large solutions and never have crashes or memory issues or slowness? If so you’re going to be disappointed.
      - ted_bunny 3y ago
        Poor is the carpenter!
- dontupvoteme 3y ago
  You're asking a model which is just a big unfocused and highly alignment taxed mush of the entire internet across all language, regardless of if its natural or computer, then (maybe) sliced across 8 experts. It can also simulate a Zizek vs Wittgenstein argument over Russian literature. the fact that it can usually write executing computer code is nothing short of magic to me. What one would want is something like LLaMA2-Coding-Vue2, maybe with a LoRA for the library or concept. I wrote a big word salad about all the other things you could do but suffice it to say there's no reason anyone should be limited to a single pass on a monolithic without automatic context window augmentation nor automatic code checking & regeneration & model escalation (e.g. query out to a 200B coding model or something).
  - gorbypark 3y ago
    I think this concept will be the future of coding. You'll have something like CodeLLaMA-javascript trained specifically for the language, then you'll have LoRA-like files distributed with/for each library. I can image something like the definitely-typed project for typescript types, but instead of doing `yarn add @types/someLibrary` you'll be able to do `yarn add @loramodels/someLibrary`. I'm javascript centric these days, but the same concept should work for most languages with a package manager. Some of these features could even be integrated into say yarn/npm directly, you currently have a devDependencies section of a package.json..I could imaging having something along the lines of a "llmDependencies" section to define which main model and version to use and which "library" models to use.
- GolDDranks 3y ago
  I don't encounter a lot of "small", one-copy-paste-size problems in my daily work that I couldn't quickly solve myself, so I haven't found a lot of use for ChatGPT while coding, yet. (I reckon this is changing though.) However a few times there have been some mechanical refactoring-style grunt work I've delighted to have been able to let ChatGPT do. However, the rate ChatGPT is giving me subtly wrong results is just high enough that I end up cross-checking everything, and then it takes me a bit more time than it would've otherwise taken. Give it a year or two, maybe?
  - layer8 3y ago
    > Give it a year or two, maybe? Maybe? It’s not clear what would bring a qualitative improvement, barring massive amounts of new training data.
    - GolDDranks 3y ago
      Based on the current front page of HackerNews, it's clear that quantitative improvement is happening all the time. I think that enough incremental progress will eventual feel like qualitative improvement. And it doesn't even need to be smarter models; improving prompts, IO systems etc. around the model might help. For example, I said I don't feel the need for help for copy-paste-sized problems, but inputing the whole repo seems to be achievable with some clever scaffolding with the IO around the model.
intended 3y ago
I can take a bet that it haha already failed - the hype cycle has already made a promise that LLMs can’t keep. Hallucinations to the normal person are a bug. The issue is that only humans can hallucinate. We know there is a “reality”. For an LLM, everything it does is a hallucination. That’s why you have more POCs than production goods. Your “hallucination rate” is unknown. Yesterday Ars has an article that described LLMs as a new type of CPU for a new type of architecture. Others want “LawLLM” or “healthLLM”. These are simply not going to happen. If you even get it to 80% accuracy- it’s a 1/5 chance you have a relevant answer. The issue isn’t a technical one. It’s expectations.
- dontupvoteme 3y ago
  Why not "simply" multigen every (important) query and take the statistical average? Hallucinations are random, the truth isn't. This is absurdly expensive with GPT4, cheaper with 3, and dirt cheap locally with LLaMA
  - xmprt 3y ago
    That only works if the generated outputs are completely independent and not correlated. I'd be interested in research that shows whether multigen actually reduces hallucination rates.
    - dontupvoteme 3y ago
      True, I'm just throwing multigen out there as a wild ass solution However you could do multigen across different models, e.g. GPT/Claude/LLaMA which should not correlate entirely
  - thatjoeoverthr 3y ago
    Just try it!
    - dontupvoteme 3y ago
      I'll give this a shot after work I think. The question I have is what's a prompt which reliably hallucinates but still produces the correct answer some of the time? I know it gets some python functions "wrong", but i think they were actually "right" in the version it was trained on, so software seems out.
coldtea 3y ago
>This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Sounds like a pretty good guesstimation. Well, not cease, just fizzle out.
- dontupvoteme 3y ago
  It hasn't even begun to get good, we only got yesterday decent local code generation models, we haven't even begun on the fine tuning and tooling for using them.
  - coldtea 3y ago
    When the "fine tuning" and focus on tooling begins, it usually means the "free" massive improvements have tapered off.
    - dontupvoteme 3y ago
      If it's anything like Stable Diffusion, it's actually where it becomes terrifyingly good at a specific thing. Like putting a field of artists out of a job, or copying a style so good that you can complete a persons piece before they do on a livestream. We're using a massive mush of the internet model which is taxed at 50% for alignment. That's going to be very dumb in the long run.
      - coldtea 3y ago
        >Like putting a field of artists out of a job, To be frank, between stock art and photos, pre-AI template based tools, and the massive oversupply of graphic design and photography work, the field was already massively redudant and kind of out of a job to begin with...
        dontupvoteme 3y ago
        I was thinking of people who... well, take commissions to make lewd images of various themes (e.g. furry stuff) or new specific characters or such... not my cup of tea but i know from researching what is possible that you only need a few images of new season anime girl blue hair edition #9237191 from the Japanese sites to do a lora and be able to make mostly anything you want when it's done on the booru models. And you can pose people with controlnet i think. that's also how those super cool QR code images are made. Those guys apparently used to make pretty good cash from twitter, usually using pen names so they wouldn't' be associated with their regular work The scary bit here is you can also "clone" a person to make any image you want of them. Obviously there's a lot of problems coming from that in the future, but also neat applications, e.g. some guy made selfie pictures of himself in the past with this for internet dating.
usrbinbash 3y ago
> This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Which is countered by...the assertion that it won't? LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots. > I want to know only one thing, which is what gives him the confidence necessary to say that. I want to know only one thing, what gives the confidence to say otherwise?
- rhn_mk1 3y ago
  It's countered by not making the assertion and not being able to make conclusions. You only need a lack of confidence for that. It's orders of magnitude easier to not have knowledge compared to having it.
- beefield 3y ago
  > LLMs won't get intelligent. That's a fact based on their MO. I kind of agree. However, I see a real possibility that in the near future LLM behaviour would be practically indistinguishable from intelligent/sentient behavior. And at that point we (or at least I) are facing some really interesting/difficult questions, namely how do you know an intelligent looking thing actually is intelligent (or sentient). How do you prove me you/LLM are/aren't a philosophical zombie? How we are supposed to treat very much intelligent/sentient looking things when we are not sure if they are sentient/intelligent or not? Let's face it, lots of people are dumb as rock (too often very much me included). Why we should be able to treat something badly just because we think we know they can't be intelligent, even if they walk , look and quack like intelligent duck? I personally have started to think that the behavior of humans should be judged by the behaviour, not the target. If you want to behave like an asshole towards a teddy bear, then you most likely are an asshole.
  - savolai 3y ago
    It seems to me that the real question here is what is true human intelligence. Ai has made it plain to see, by being able to replicate it so convincingly, that much of what we have considered intelligence has been pattern matching or acting as complex parrots. There is much more to the abilities of human body-mind-emotional-experiential being, but it is only slowly becoming mainstream. (Edit: Of course there are also many analytical skills that AI cannot match at this point. My point is that we shouldn’t overlook any area of human capacity.) One salient question in this is: will we reach a level of intelligence where we become beings capable of actual collaboration that doesn’t waste so much effort in conflicts, or one that is capable of living in harmony within its environment? What capabilities of awareness, trauma work, emotional maturity and self reflection does this require? What resources hidden inside humanity that we have forgotten do we need to wield? Does AI have something to contribute to this process happening?
thrwjud 3y ago
Every deep learning tech had an exponential growth phase, followed by a slowdown followed by a platou the nothing could break until a fundamentally new architecture came along. People get excited about the first part, project it into the second and start companies by the time we're well into the third.
- ambrozk 3y ago
  Yes, but how do they know where we are in that growth phase? If you confidently tell me that "GPT-4 is the plateau," I want to know how you know that, specifically. "Well, because all deep learning technologies eventually slow down" is not a good argument. You need to show me the diminishing returns, or give me a good theoretical argument for why we're reaching them.
  - thrwjud 3y ago
    The more compute you need to get state of the art performance the closer to the plateau you are. If you didn't need the compute researchers would be getting better results with smarter training. Given that the gpt family of models need more energy to train than Nevada needs to keep the lights on they are very much on the flat part of the logistic growth curve.
    - ambrozk 3y ago
      This isn't true. In fact, it's the reverse of true. If you think a bit more carefully about your argument, you'll realize that you've asserted that the single most revolutionary advance of modern deep networks (i.e. network architectures whose performance scales neatly with their parameter counts & training epochs) automatically portends "the plateau of forward progress."
jug 3y ago
There has actually been research that found that there are strong diminishing returns in terms of at least expanding parameter sizes. While I think there are still breakthroughs to be made in terms of window sizes and workarounds like Mixture of Experts, I'm not sure how much farther we will get here in the long term in terms of raw performance of the LLM itself. FWIW, Sam Altman agrees and has a surprisingly similar quote as found here "I think we’re at the end of the era where it’s gonna be these giant models, and we’ll make them better in other ways": https://techcrunch.com/2023/04/14/sam-altman-size-of-llms-wont-matter-as-much-moving-forward https://techcrunch.com/2023/04/14/sam-altman-size-of-llms-wo...
- brandall10 3y ago
  GPT-4 is already based on a MOE approach. Just noting as this was placed in the "breakthroughs to be made".
- FrustratedMonky 3y ago
  How can research predict this wave is coming to an end, when research also didn't think this wave would happen either. It seems like there are always people saying 'it can't be done'. Then it happens. If there was a way to predict the future, then wouldn't that research need to know how something would be implemented, in order to know it can't be?
  - StackOverlord 3y ago
    For the first time with GPT4, OpenAI as been able to predict model progress with accuracy: > A large focus of the GPT-4 project has been building a deep learning stack that scales predictably. The primary reason is that, for very large training runs like GPT-4, it is not feasible to do extensive model-specific tuning. We developed infrastructure and optimization that have very predictable behavior across multiple scales. To verify this scalability, we accurately predicted in advance GPT-4’s final loss on our internal codebase (not part of the training set) by extrapolating from models trained using the same methodology but using 10,000x less compute: > Now that we can accurately predict the metric we optimize during training (loss), we’re starting to develop methodology to predict more interpretable metrics. For example, we successfully predicted the pass rate on a subset of the HumanEval dataset, extrapolating from models with 1,000x less compute: > We believe that accurately predicting future machine learning capabilities is an important part of safety that doesn’t get nearly enough attention relative to its potential impact (though we’ve been encouraged by efforts across several institutions). We are scaling up our efforts to develop methods that provide society with better guidance about what to expect from future systems, and we hope this becomes a common goal in the field. Source: https://openai.com/research/gpt-4 https://openai.com/research/gpt-4
nine_k 3y ago
The current crop of LLMs is like Paleozoic megafauna, or like Egyptian Pyramids. It takes a few relatively simple approaches and stretches them wildly, using colossal computing resources. Live systems in nature seem to solve similar problems with way less compute available. There should be better architectures. Also, as somebody said, every exponential growth curve is a lower part of a sigmoid. LLMs will plateau at some level. That level may be impressively high though.
- __loam 3y ago
  I'm not convinced we're going to get much further than this, at least without a see change in how this stuff is done. We saw a pretty big amount of diminishing returns with GPT-4, which cost 2 orders of magnitude more money to train than 3.5. Is the performance more than 2 orders of magnitude better than 3.5? How much compute is too much to be spending on this bullshit. 30% of humanity's total computing resources? 70%? GPT was a big innovation in parallelizing the training process. I think the optimism that we're on the beginning of a sigmoid curve here ought to be questioned.
  - danenania 3y ago
    The performance doesn't have to scale with the training cost. It's ok if we have diminishing returns. The only question is if we can keep getting improvements. If we could get a 10x improvement over GPT-4 (while maintaining safety/alignment), the potential benefits to productivity across the entire world economy are so great that it justifies nearly unlimited investment.
    - __loam 3y ago
      This is delusional.
      - deleted 3y ago
        [deleted]
- the8472 3y ago
vidarh 3y ago
There's a second assumption: That "worker displacement" remains an unsolved problem. That assumes a society that chooses to do nothing to in the face of reducing the need for labour, turning something that could be substantial boon into a downside. In other words: It assumes Marx was right about the central failure-mode of capitalism (an inability or unwillingness to find ways to distribute work and proceeds of work in ways that prevents productivity gains from eventually causing social upheaval). There's no reason what they describe as "so-so" tech can not also be a significant societal advantage, but it requires structural change to ensure it does not deprive a significant number of people of a livelihood.
asynchronous 3y ago
I’d agree with the sentiment that we are not at the end of the LLM development, simply because looking at other similar things like Stable Diffusion, the community has made so many crazy enhancements in such a short amount of time. With more time to refine these enhancements I think we can close a lot of gaps.
IKantRead 3y ago
I work in this space and think that it's far more rational to accept the axiom that LLM progress will not be significant than to bank product work on assuming it will increase drastically. I do think we still have yet to squeeze the most value out of current LLMs, but most people's radical AI dreams are completely out-of-touch with reality for anyone working closely on these problems. My biggest fear in this space is that disappointment in the inability of these tools to live up to the hype will cause people to irrationally abandon exploring the spaces where they do work.
- 6D794163636F756 3y ago
  I think people assume that the current LLM release cycle is similar to other software releases which is to say, MVP followed by full product. ChatGPT is an actual product though, it's not the beta version of the technology. I think the dramatic increases in the product have likely mostly happened before launch but that you're right and we will see minor improvements.
- ambrozk 3y ago
  It may be rational and it may be irrational. All I'm saying is I want to see an argument. My impression of the AI space generally is that there are many, many obvious ideas which are simply waiting to be picked up off the ground and tested, and that it's not clear to anyone how much better (what are we even quantifying this with -- log loss?) the base LLM capabilities have to be before they're suitable for making tools that automate large quantities of work. Even if you assume that 240B is the limit for how many parameters number of engineers who have the ability to fine-tune GPT-4 or whatever Google is about to come out with is vanishingly small compared to the number of engineers who are participating in the open-source ML community, and the number of engineers in the open-source ML community is vanishingly small compared to the number of engineers in the long-tail of app developers who will ultimately adopt LLMs to their use-cases. Even assuming that GPT-4 is the best an LLM can possibly be, (which, again, I've seen no argument for), the widening of LLM availability and the building of practical tooling is a strong reason to believe that the utility of LLMs to concrete products will dramatically increase in the next 4-5 years.

user3939382 3y ago

I asked ChatGPT how to add a JSDoc type to a Vue 2 prop and it gave me a wrong answer. There have been several times where I’ve asked it questions and it sprinkles in well disguised misinformation. These tools are impressive but they definitely have limitations.

golol 3y ago
ChatGPT (3.5) is not state of the art
seanthemon 3y ago
Curious, how does it do when you give it examples / use playground.openai? I find that no examples often leads to the same result as a befuddled junior, but with examples often it gains confidence. Also I find playground to give me much better code snippets than chat sometimes
- cbozeman 3y ago
  You've identified the problem inherent in these models right now, though. There's a lot of "extraneous" information and details that appear useless and unrelated, but that long-time developers have tucked away in their brains (or really anyone who has done something at a high level for a long time), that turns out to be incredibly useful; generally these people don't require examples - they just know what the right answer is, because they've been exposed to a variety of problems over a long career or lifespan. That's where LLMs need to be to be truly "useful". I think if we get them to that point, we'll really have something useful on our hands.
  - dontupvoteme 3y ago
    That's where good money and societal upheaval will come up, private models for corporations trained on their entire codebase and documentation. Both young and old shall feel the pain then.
    - HPsquared 3y ago
      The codebase and documentation usually don't capture the full context of the development process. There is still a lot that isn't written down.
      - dontupvoteme 3y ago
        I agree, but the MBAs will probably not.
  - seanthemon 3y ago
    Very insightful point - do you think that OpenAI and Microsoft are aiming at developers so that we may spill this knowledge by N-shotting the chat query? I.e if the AI doesn't understand some fundamental knowledge, often i'll teach it so that it can be more effective at helping me, inadvertently I spill some of this tucked knowledge.
    - cbozeman 3y ago
      I don't think they have any desire to use this knowledge for their own gain, since they explicitly say that your data will not be used to train their future models - both OpenAI is saying that about their trainable ChatGPT 3.5 Turbo model, and Azure OpenAI Service was clear on that as well. I think the future is extremely well-trained base models that are then fine-tuned on specific domain knowledge. I'm already seeing that with Meta's Llama 2 personally at home. My company has access to Azure OpenAI Service's GPT-4 trainable model that we've been working with on all our documentation, and the results look very promising so far. But, this idea of the All-Knowing Oracle that can answer any question you have is all well and good, but it's so far beyond our current processing power as to be a pointless endeavor at the moment. Will we get there eventually? Yeah, sure. According to Jim Keller as of around 2019/2020 when he was speaking on Lex Fridman's podcast, he said we still have room to go 1,000,000 times smaller - meaning chips. I think we probably need two more orders of magnitude in processing power of the strongest high-end GPUs before we're there. NVIDIA's H100 is a great achievement, but we need something about 100x as fast, and I think we'll still need dozens of them working in parallel to train the model that can answer all these questions. All of this though is a moot point, because it's the lawyers that have already slowed down progress. OpenAI is terrified of being sued. Altman couches it behind terms like, "AI Safety", "AI Alignment", etc., but it's fear. It's all stemming from fear. And it's all stemming from people just not "getting it". We're entering a new age of upheaval, and there's going to be rogue AIs that tell people to go kill themselves to reduce climate change. You know why? Because there are humans that tell people to go kill themselves to reduce climate change. These models are language models. We taught them how to think, and we taught them how to think like us, so it's no surprise to me whatsoever that they behave like us - meaning they occasionally lie and they occasionally go off the rails and go a little crazy. We have become gods and we have made a creation in our own image. Most of the time it's awesome, sometimes it's a little wild and wacky.
jasfi 3y ago
Are you using GPT-4? If not, it's understandable. If you don't pay for ChatGPT, you get GPT-3.5. You can also get access to GPT-4 if you use the playground.
- andrepd 3y ago
  Why is it always the same reply? Yes, GPT4 is as useless as GPT3.5 on any non trivial task.
  - input_sh 3y ago
    My guess is people feel the need to self-justify their $20/month subscription.
    - aenis 3y ago
      My guess is most people into AI don't even remember that they are paying a $20/month for this. We do a lot of experiments involving gpt3.5, 4, claude-v2, titan-large, and palm2, and for what it's worth, on our real production workloads gpt4 shines. We can make Palm2 produce decent results with a lot of extra effort, and claude-v2 is passable but gpt4 does not disappoint. This is low-grade knowledge management stuff, and we are not using it as a information-retrieval system - but for basic 'cognitive' tasks where all the information needed is provided in the prompt. I'd not rely on it for info retrieval tasks such as the examples quoted above - its knowledge base is highly compressed, after all.
  - Kiro 3y ago
    > Why is it always the same reply? When there's one definitive answer to something that people keep repeating there's a slight chance that it's actually true. Shocking, I know.
    - andrepd 3y ago
      Well but it's not. Also, your reasoning would imply e.g. Qanon is true based on the number of people repeating the same thing, so that's pretty weak.
jstummbillig 3y ago
Everything has limitations. The linked piece is very much a statement about limited potential, not the current state. In this context, this stance is akin to saying "I asked a 6yo this math question and it gave me a wrong answer, there's definitely limitations". Sure – but who would use that observation to assess where the future potential cap of the kid is?
ryoshu 3y ago
True. I can't do a full table scan on an non-indexed column in a RDBMS with billions of rows on a single machine without it grinding to a halt. Tools have limitations.
- dclowd9901 3y ago
  Disingenuous: given the proper processing, you’d get a 100% deterministic and reliable answer. How do you get that out of an LLM? What tool is any good if it doesn’t work 100% of the time predictably?
  - dontupvoteme 3y ago
    Right now one generation is expensive on these big mainframe models. Query many times on the right model(s) for the question and the correct answer will be there 99.999% of the time as the other hallucinations will be thrown out.
  - saberience 3y ago
    How many tools do you know of that work 100% of the time. As someone that’s worked in software 20 years now, I don’t think there is anything from Linux to eMacs to visual studio to Java which doesn’t have bugs, issues, crashes, shitty stuff, and yet we expect a revolutionary and amazing technology to generate code perfectly? It’s funny those the LLM haters keep raising the bar to a level that no other software can reach. Chatgpt is a tool like any other and just like a hammer, it can be misused, or it can be incredibly useful if used well. I personally find chatgpt mind boggling and astounding and use it every day multiple times for both coding and non coding purposes. But it’s totally normal and reasonable to me to expect bugs, do you really expect visual studio to run on large solutions and never have crashes or memory issues or slowness? If so you’re going to be disappointed.
    - ted_bunny 3y ago
      Poor is the carpenter!
dontupvoteme 3y ago
You're asking a model which is just a big unfocused and highly alignment taxed mush of the entire internet across all language, regardless of if its natural or computer, then (maybe) sliced across 8 experts. It can also simulate a Zizek vs Wittgenstein argument over Russian literature. the fact that it can usually write executing computer code is nothing short of magic to me. What one would want is something like LLaMA2-Coding-Vue2, maybe with a LoRA for the library or concept. I wrote a big word salad about all the other things you could do but suffice it to say there's no reason anyone should be limited to a single pass on a monolithic without automatic context window augmentation nor automatic code checking & regeneration & model escalation (e.g. query out to a 200B coding model or something).
- gorbypark 3y ago
  I think this concept will be the future of coding. You'll have something like CodeLLaMA-javascript trained specifically for the language, then you'll have LoRA-like files distributed with/for each library. I can image something like the definitely-typed project for typescript types, but instead of doing `yarn add @types/someLibrary` you'll be able to do `yarn add @loramodels/someLibrary`. I'm javascript centric these days, but the same concept should work for most languages with a package manager. Some of these features could even be integrated into say yarn/npm directly, you currently have a devDependencies section of a package.json..I could imaging having something along the lines of a "llmDependencies" section to define which main model and version to use and which "library" models to use.
GolDDranks 3y ago
I don't encounter a lot of "small", one-copy-paste-size problems in my daily work that I couldn't quickly solve myself, so I haven't found a lot of use for ChatGPT while coding, yet. (I reckon this is changing though.) However a few times there have been some mechanical refactoring-style grunt work I've delighted to have been able to let ChatGPT do. However, the rate ChatGPT is giving me subtly wrong results is just high enough that I end up cross-checking everything, and then it takes me a bit more time than it would've otherwise taken. Give it a year or two, maybe?
- layer8 3y ago
  > Give it a year or two, maybe? Maybe? It’s not clear what would bring a qualitative improvement, barring massive amounts of new training data.
  - GolDDranks 3y ago
    Based on the current front page of HackerNews, it's clear that quantitative improvement is happening all the time. I think that enough incremental progress will eventual feel like qualitative improvement. And it doesn't even need to be smarter models; improving prompts, IO systems etc. around the model might help. For example, I said I don't feel the need for help for copy-paste-sized problems, but inputing the whole repo seems to be achievable with some clever scaffolding with the IO around the model.

intended 3y ago

I can take a bet that it haha already failed - the hype cycle has already made a promise that LLMs can’t keep. Hallucinations to the normal person are a bug. The issue is that only humans can hallucinate. We know there is a “reality”. For an LLM, everything it does is a hallucination. That’s why you have more POCs than production goods. Your “hallucination rate” is unknown. Yesterday Ars has an article that described LLMs as a new type of CPU for a new type of architecture. Others want “LawLLM” or “healthLLM”. These are simply not going to happen. If you even get it to 80% accuracy- it’s a 1/5 chance you have a relevant answer. The issue isn’t a technical one. It’s expectations.

dontupvoteme 3y ago
Why not "simply" multigen every (important) query and take the statistical average? Hallucinations are random, the truth isn't. This is absurdly expensive with GPT4, cheaper with 3, and dirt cheap locally with LLaMA
- xmprt 3y ago
  That only works if the generated outputs are completely independent and not correlated. I'd be interested in research that shows whether multigen actually reduces hallucination rates.
  - dontupvoteme 3y ago
    True, I'm just throwing multigen out there as a wild ass solution However you could do multigen across different models, e.g. GPT/Claude/LLaMA which should not correlate entirely
- thatjoeoverthr 3y ago
  Just try it!
  - dontupvoteme 3y ago
    I'll give this a shot after work I think. The question I have is what's a prompt which reliably hallucinates but still produces the correct answer some of the time? I know it gets some python functions "wrong", but i think they were actually "right" in the version it was trained on, so software seems out.

coldtea 3y ago

>This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Sounds like a pretty good guesstimation. Well, not cease, just fizzle out.

dontupvoteme 3y ago
It hasn't even begun to get good, we only got yesterday decent local code generation models, we haven't even begun on the fine tuning and tooling for using them.
- coldtea 3y ago
  When the "fine tuning" and focus on tooling begins, it usually means the "free" massive improvements have tapered off.
  - dontupvoteme 3y ago
    If it's anything like Stable Diffusion, it's actually where it becomes terrifyingly good at a specific thing. Like putting a field of artists out of a job, or copying a style so good that you can complete a persons piece before they do on a livestream. We're using a massive mush of the internet model which is taxed at 50% for alignment. That's going to be very dumb in the long run.
    - coldtea 3y ago
      >Like putting a field of artists out of a job, To be frank, between stock art and photos, pre-AI template based tools, and the massive oversupply of graphic design and photography work, the field was already massively redudant and kind of out of a job to begin with...
      - dontupvoteme 3y ago
        I was thinking of people who... well, take commissions to make lewd images of various themes (e.g. furry stuff) or new specific characters or such... not my cup of tea but i know from researching what is possible that you only need a few images of new season anime girl blue hair edition #9237191 from the Japanese sites to do a lora and be able to make mostly anything you want when it's done on the booru models. And you can pose people with controlnet i think. that's also how those super cool QR code images are made. Those guys apparently used to make pretty good cash from twitter, usually using pen names so they wouldn't' be associated with their regular work The scary bit here is you can also "clone" a person to make any image you want of them. Obviously there's a lot of problems coming from that in the future, but also neat applications, e.g. some guy made selfie pictures of himself in the past with this for internet dating.

usrbinbash 3y ago

> This entire piece is based on one massive, unsupported assertion, which is that LLM progress will cease. Which is countered by...the assertion that it won't? LLMs won't get intelligent. That's a fact based on their MO. They are sequence completion engines. They can be fine tuned to specific tasks, but at their core, they remain stochastic parrots. > I want to know only one thing, which is what gives him the confidence necessary to say that. I want to know only one thing, what gives the confidence to say otherwise?

rhn_mk1 3y ago
It's countered by not making the assertion and not being able to make conclusions. You only need a lack of confidence for that. It's orders of magnitude easier to not have knowledge compared to having it.
beefield 3y ago
> LLMs won't get intelligent. That's a fact based on their MO. I kind of agree. However, I see a real possibility that in the near future LLM behaviour would be practically indistinguishable from intelligent/sentient behavior. And at that point we (or at least I) are facing some really interesting/difficult questions, namely how do you know an intelligent looking thing actually is intelligent (or sentient). How do you prove me you/LLM are/aren't a philosophical zombie? How we are supposed to treat very much intelligent/sentient looking things when we are not sure if they are sentient/intelligent or not? Let's face it, lots of people are dumb as rock (too often very much me included). Why we should be able to treat something badly just because we think we know they can't be intelligent, even if they walk , look and quack like intelligent duck? I personally have started to think that the behavior of humans should be judged by the behaviour, not the target. If you want to behave like an asshole towards a teddy bear, then you most likely are an asshole.
- savolai 3y ago
  It seems to me that the real question here is what is true human intelligence. Ai has made it plain to see, by being able to replicate it so convincingly, that much of what we have considered intelligence has been pattern matching or acting as complex parrots. There is much more to the abilities of human body-mind-emotional-experiential being, but it is only slowly becoming mainstream. (Edit: Of course there are also many analytical skills that AI cannot match at this point. My point is that we shouldn’t overlook any area of human capacity.) One salient question in this is: will we reach a level of intelligence where we become beings capable of actual collaboration that doesn’t waste so much effort in conflicts, or one that is capable of living in harmony within its environment? What capabilities of awareness, trauma work, emotional maturity and self reflection does this require? What resources hidden inside humanity that we have forgotten do we need to wield? Does AI have something to contribute to this process happening?

thrwjud 3y ago

Every deep learning tech had an exponential growth phase, followed by a slowdown followed by a platou the nothing could break until a fundamentally new architecture came along. People get excited about the first part, project it into the second and start companies by the time we're well into the third.

ambrozk 3y ago
Yes, but how do they know where we are in that growth phase? If you confidently tell me that "GPT-4 is the plateau," I want to know how you know that, specifically. "Well, because all deep learning technologies eventually slow down" is not a good argument. You need to show me the diminishing returns, or give me a good theoretical argument for why we're reaching them.
- thrwjud 3y ago
  The more compute you need to get state of the art performance the closer to the plateau you are. If you didn't need the compute researchers would be getting better results with smarter training. Given that the gpt family of models need more energy to train than Nevada needs to keep the lights on they are very much on the flat part of the logistic growth curve.
  - ambrozk 3y ago
    This isn't true. In fact, it's the reverse of true. If you think a bit more carefully about your argument, you'll realize that you've asserted that the single most revolutionary advance of modern deep networks (i.e. network architectures whose performance scales neatly with their parameter counts & training epochs) automatically portends "the plateau of forward progress."

jug 3y ago

There has actually been research that found that there are strong diminishing returns in terms of at least expanding parameter sizes. While I think there are still breakthroughs to be made in terms of window sizes and workarounds like Mixture of Experts, I'm not sure how much farther we will get here in the long term in terms of raw performance of the LLM itself. FWIW, Sam Altman agrees and has a surprisingly similar quote as found here "I think we’re at the end of the era where it’s gonna be these giant models, and we’ll make them better in other ways": https://techcrunch.com/2023/04/14/sam-altman-size-of-llms-wont-matter-as-much-moving-forward https://techcrunch.com/2023/04/14/sam-altman-size-of-llms-wo...

brandall10 3y ago
GPT-4 is already based on a MOE approach. Just noting as this was placed in the "breakthroughs to be made".
FrustratedMonky 3y ago
How can research predict this wave is coming to an end, when research also didn't think this wave would happen either. It seems like there are always people saying 'it can't be done'. Then it happens. If there was a way to predict the future, then wouldn't that research need to know how something would be implemented, in order to know it can't be?
- StackOverlord 3y ago
  For the first time with GPT4, OpenAI as been able to predict model progress with accuracy: > A large focus of the GPT-4 project has been building a deep learning stack that scales predictably. The primary reason is that, for very large training runs like GPT-4, it is not feasible to do extensive model-specific tuning. We developed infrastructure and optimization that have very predictable behavior across multiple scales. To verify this scalability, we accurately predicted in advance GPT-4’s final loss on our internal codebase (not part of the training set) by extrapolating from models trained using the same methodology but using 10,000x less compute: > Now that we can accurately predict the metric we optimize during training (loss), we’re starting to develop methodology to predict more interpretable metrics. For example, we successfully predicted the pass rate on a subset of the HumanEval dataset, extrapolating from models with 1,000x less compute: > We believe that accurately predicting future machine learning capabilities is an important part of safety that doesn’t get nearly enough attention relative to its potential impact (though we’ve been encouraged by efforts across several institutions). We are scaling up our efforts to develop methods that provide society with better guidance about what to expect from future systems, and we hope this becomes a common goal in the field. Source: https://openai.com/research/gpt-4 https://openai.com/research/gpt-4

nine_k 3y ago

The current crop of LLMs is like Paleozoic megafauna, or like Egyptian Pyramids. It takes a few relatively simple approaches and stretches them wildly, using colossal computing resources. Live systems in nature seem to solve similar problems with way less compute available. There should be better architectures. Also, as somebody said, every exponential growth curve is a lower part of a sigmoid. LLMs will plateau at some level. That level may be impressively high though.

__loam 3y ago
I'm not convinced we're going to get much further than this, at least without a see change in how this stuff is done. We saw a pretty big amount of diminishing returns with GPT-4, which cost 2 orders of magnitude more money to train than 3.5. Is the performance more than 2 orders of magnitude better than 3.5? How much compute is too much to be spending on this bullshit. 30% of humanity's total computing resources? 70%? GPT was a big innovation in parallelizing the training process. I think the optimism that we're on the beginning of a sigmoid curve here ought to be questioned.
- danenania 3y ago
  The performance doesn't have to scale with the training cost. It's ok if we have diminishing returns. The only question is if we can keep getting improvements. If we could get a 10x improvement over GPT-4 (while maintaining safety/alignment), the potential benefits to productivity across the entire world economy are so great that it justifies nearly unlimited investment.
  - __loam 3y ago
    This is delusional.
    - deleted 3y ago
      [deleted]
the8472 3y ago

vidarh 3y ago

There's a second assumption: That "worker displacement" remains an unsolved problem. That assumes a society that chooses to do nothing to in the face of reducing the need for labour, turning something that could be substantial boon into a downside. In other words: It assumes Marx was right about the central failure-mode of capitalism (an inability or unwillingness to find ways to distribute work and proceeds of work in ways that prevents productivity gains from eventually causing social upheaval). There's no reason what they describe as "so-so" tech can not also be a significant societal advantage, but it requires structural change to ensure it does not deprive a significant number of people of a livelihood.

asynchronous 3y ago

I’d agree with the sentiment that we are not at the end of the LLM development, simply because looking at other similar things like Stable Diffusion, the community has made so many crazy enhancements in such a short amount of time. With more time to refine these enhancements I think we can close a lot of gaps.

IKantRead 3y ago

I work in this space and think that it's far more rational to accept the axiom that LLM progress will not be significant than to bank product work on assuming it will increase drastically. I do think we still have yet to squeeze the most value out of current LLMs, but most people's radical AI dreams are completely out-of-touch with reality for anyone working closely on these problems. My biggest fear in this space is that disappointment in the inability of these tools to live up to the hype will cause people to irrationally abandon exploring the spaces where they do work.

6D794163636F756 3y ago
I think people assume that the current LLM release cycle is similar to other software releases which is to say, MVP followed by full product. ChatGPT is an actual product though, it's not the beta version of the technology. I think the dramatic increases in the product have likely mostly happened before launch but that you're right and we will see minor improvements.
ambrozk 3y ago
It may be rational and it may be irrational. All I'm saying is I want to see an argument. My impression of the AI space generally is that there are many, many obvious ideas which are simply waiting to be picked up off the ground and tested, and that it's not clear to anyone how much better (what are we even quantifying this with -- log loss?) the base LLM capabilities have to be before they're suitable for making tools that automate large quantities of work. Even if you assume that 240B is the limit for how many parameters number of engineers who have the ability to fine-tune GPT-4 or whatever Google is about to come out with is vanishingly small compared to the number of engineers who are participating in the open-source ML community, and the number of engineers in the open-source ML community is vanishingly small compared to the number of engineers in the long-tail of app developers who will ultimately adopt LLMs to their use-cases. Even assuming that GPT-4 is the best an LLM can possibly be, (which, again, I've seen no argument for), the widening of LLM availability and the building of practical tooling is a strong reason to believe that the utility of LLMs to concrete products will dramatically increase in the next 4-5 years.