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Ilya Sutskever NeurIPS talk [video]
- stretchwithme 2y agoAIs will need to start asking people questions. Should make for some very strange phone calls.
- wslh 2y agoThat's a good point. I think most people use LLMs by asking questions and receiving answers. But if you reverse the dynamic and have the LLM interview you instead, where you simply respond to its questions, you'll notice something interesting: the LLM as an interviewer is far less "smart" than it is when simply providing answers. I’ve tried it myself, and the interview felt more like interacting with ELIZA [1]. There seemed to be a lack of intent when the LLM was the one asking the questions. This creates a reverse dynamic, where you become the one being "prompted" and this dynamic could be worth studying or adjusting further [1] https://en.wikipedia.org/wiki/ELIZA https://en.wikipedia.org/wiki/ELIZA
- airstrike 2y agoWhich LLM did you perform that test with?
- wslh 2y agoChatGPT Pro.
- afro88 2y agoThat's not an LLM, that's a subscription plan. You can select any OpenAI LLM on ChatGPT Pro. You can share the chat here, and this will show the LLM you had selected for the conversation. The initial prompt is also pretty important. For claims like current LLMs feel like conversing with Eliza, you are most definitely missing something in how you're going about it. Advanced voice mode will give you better results for conversations too. It seems to be set up to converse rather than provide answers or perform work for you. No initial prompt, model selection or setup required
- Barrin92 2y ago>There seemed to be a lack of intent when the LLM was the one asking the questions There doesn't just seem to be lack of intent, there is no intent, because by the nature of its architecture these systems are just set of weights with a python script attached to them asking you to give you one more token over and over. There's no needs, drives, motivations, desires or any other part of the cognitive architecture of humans in there that produce genuine intent.
- staticman2 2y agoDid you ask the AI to roleplay? All it does is predict what text comes next. Telling it it is role-playing as a perceptive journalist or prominent psychologist should change its predictions. In some cases using it where you have API access to the system prompt will allow a greater difference in behavior.
- zookerterg 2y ago[flagged]
- skissane 2y ago> “Pre-training as we know it will unquestionably end,” Sutskever said onstage. > “We’ve achieved peak data and there’ll be no more.” > During his NeurIPS talk, Sutskever said that, while he believes existing data can still take AI development farther, the industry is tapping out on new data to train on. This dynamic will, he said, eventually force a shift away from the way models are trained today. He compared the situation to fossil fuels: just as oil is a finite resource, the internet contains a finite amount of human-generated content. > “We’ve achieved peak data and there’ll be no more,” according to Sutskever. “We have to deal with the data that we have. There’s only one internet.” What will replace Internet data for training? Curated synthetic datasets? There are massive proprietary datasets out there which people avoid using for training due to copyright concerns. But if you actually own one of those datasets, that resolves a lot of the legal issues with training on it. For example, Getty has a massive image library. Training on it would risk Getty suing you. But what if Getty decides to use it to train their own AI? Similarly, what if News Corp decides to train an AI using its publishing assets (Wall Street Journal, HarperCollins, etc)?
- _aavaa_ 2y ago> just as oil is a finite resource, the internet contains a finite amount of human-generated content. I guess now they’re being explicit about the blatantly extractive nature of these businesses and their models.
- kibae 2y agoI always suspected that bots on Reddit were used to gain karma and then eventually sell the account, but maybe they're also being used for some kind of RLHF.
- vitorgrs 2y agoNot sure if this was a good example. Getty already license their images to Nvidia. And they already have a generative image service... I believe it's power by Nvidia model.
- popularonion 2y ago> What will replace Internet data for training? Curated synthetic datasets? Enter Neuralink
- deleted 2y ago[deleted]
- neom 2y agoFull talk is interesting: https://www.youtube.com/watch?v=YD-9NG1Ke5Y https://www.youtube.com/watch?v=YD-9NG1Ke5Y
- CuriousSkeptic 2y agoOn the slide of body/brain weight relation he highlighted the humanids difference in scaling What he didn't mention, that I found interesting, was that the same slide also highlighted a hard ceiling for non-humanids at the same point
- imranhou 2y agoThis is a very interesting point, in some ways the implicit belief is that we just need to get beyond the 700g limitation in terms of scaling LLM models and we would get human intelligence/superintelligence. I admit I didn't really get the body/brain analogy, I would have been better satisfied with a simpler graph of brain weight to intelligence with a scaling barrier of 700g.
- deleted 2y ago[deleted]
- zxexz 2y agoI can't help but feel that this talk was a lot of...fluff? The synopsis, as far as my tired brain can remember: - Here's a brief summary of the last 10 years - We're reaching the limit of our scaling laws, because we've trained on all the data we have available on the limit - Some things that may be next are "agents", "synthetic data", and improving compute - Some "ANNs are like biological NNs" rehash that would feel questionable if there was a thesis (which there wasn't? something about how body mass vs. brain mass are positively correlated?) - 3 questions, the first was something about "hallucinations" and whether a model be able to understand if it is hallucinating? Then something that involved cryptocurrencies, and then a _slightly_ interesting question about multi-hop reasoning
- coeneedell 2y agoI attended this talk in person and some context is needed. He was invited for the “test of time” talk series. This explains the historical part of the talk. I think his general persona and association with ai led to the fluffy speculation at the end. I notice with Ilya he wants to talk about these out there speculative topics but defends himself with statements like “I’m not saying when or how just that it will happen” which makes his arguments impossible to address. Stuff like this openly invites the crazies to to interact with him, as seen with the cryptocurrency question at the end. Right before this was a talk reviewing the impact of GANs that stayed on topic for the conference session throughout.
- ITjournalist 2y ago"a talk reviewing the impact of GANs" Is that available online?
- mrbungie 2y agoI mean he repeateadly gave some hints (even if just for the lulz and not seriously) that the audience is at least partially composed of people with little technical background or AI bros. An example is when he mentioned LSTMs and said "many of you may never seen before". Even if he didn't mean it, ironically it ended being spot on when the crypto question came.
- sigmar 2y agoI found this week's DeepMind podcast with Oriole Vinyals to be on similar topics as this talk (current situation of LLMs, path ahead with training) but much more interesting: https://pca.st/episode/0f68afd5-2b2b-4ce9-964f-38193b7e8dd3 https://pca.st/episode/0f68afd5-2b2b-4ce9-964f-38193b7e8dd3
- sega_sai 2y agoVery thought provoking. One of the things was not clear to me, what does he mean by 'agentic' intelligence?
- kgeist 2y agoWhen it autonomously performs tasks on behalf of the user, without their intervention. It sets goals, plans actions, etc. by itself.
- ototot 2y agoExample of 'agentic': https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/ https://blog.google/technology/google-deepmind/google-gemini...
- ethbr1 2y agoIt's the current industry buzzword for action-oriented LLMs. Instead of generating text, tightening/formalizing the loop around planning, executing, analyzing results, and replanning. As far as buzzwords go, it's far from the worst, as it captures the essentials -- creating semi-autonomous agents.
- ed 2y agoBased on the context Ilya is not referring to that kind of agent. He’s referring to something more fundamental (which I was curious about, too).
- legel 2y agoI’m glad Ilya starts the talk with a photo of Quoc Le, who was the lead author of a 2012 paper on scaling neural nets that inspired me to go into deep learning at the time. His comments are relatively humble and based on public prior work, but it’s clear he’s working on big things today and also has a big imagination. I’ll also just say that at this point “the cat is out of the bag”, and probably it will be a new generation of leaders — let us all hope they are as humanitarian — who drive the future of AI.
- chipsrafferty 2y agoLiterally a zero chance that the new generation of leaders of artifical intelligence will be humanitarian.
- mrbungie 2y agoLet us all hope that they will be as humanitarian as they can be, but let's not forget they are still just human beings.
- tikkun 2y agoAs context on Ilya's predictions given in this talk, he predicted these in July 2017: > Within the next three years, robotics should be completely solved [wrong, unsolved 7 years later], AI should solve a long-standing unproven theorem [wrong, unsolved 7 years later], programming competitions should be won consistently by AIs [wrong, not true 7 years later, seems close though], and there should be convincing chatbots (though no one should pass the Turing test) [correct, GPT-3 was released by then, and I think with a good prompt it was a convincing chatbot]. In as little as four years, each overnight experiment will feasibly use so much compute capacity that there’s an actual chance of waking up to AGI [didn't happen], given the right algorithm — and figuring out the algorithm will actually happen within 2–4 further years of experimenting with this compute in a competitive multiagent simulation [didn't happen]. Being exceptionally smart in one field doesn't make you exceptionally smart at making predictions about that field. Like AI models, human intelligence often doesn't generalize very well.
- padolsey 2y ago>exceptionally smart at making predictions Is anyone though? Genuine question. I don't have much faith in predictions anymore.
- qeternity 2y agoNo, very few for things with this much uncertainty. Most of it is survivorship bias: if you have a million people all making predictions with coin flip accuracy, somebody is going to get a seemingly improbable number correct.
- exe34 2y agoso your prediction is that most predictions will be wrong?
- bobbruno 2y agoA common saying in the stats field goes like this: "Predictions are hard, especially about the future".
- _giorgio_ 2y agoWhat a stupid talk. They gave 15 minutes to one of the most competent scientist. A joke.
- ldenoue 2y agoLLM corrected transcript (using Gemini Flash 8B over the raw YouTube transcript) https://www.appblit.com/scribe?v=YD-9NG1Ke5Y#0 https://www.appblit.com/scribe?v=YD-9NG1Ke5Y#0
- oezi 2y agoHow do you prevent Gemini from just swallowing text after some time? Audio transcript correction is one area where I struggle to see good results from any LLM unless I chunk it to no more than one or two pages. Or did you use any tool?
- belter 2y agoIt’s surprising that some prominent ML practitioners still liken transformer ‘neurons’ to actual biological neurons... Real neurons rely on spiking, ion gradients, complex dendritic trees, and synaptic plasticity governed by intricate biochemical processes. None of which apply to the simple, differentiable linear layers and pointwise nonlinearities in transformers. Are there any reputable neuroscientists or biologists endorsing such comparisons, or is this analogy strictly a convention maintained by the ML community? :-)
- martindbp 2y agoYou have to remember what came before 2012: SVMs, Random Forests etc, absolutely nothing like the brain (yes, NNs are old, but 2012 was the start of the deep learning revolution). With this frame of reference, the brain and neural networks are both a kind of Connectionism with similar properties, and I think it makes perfect sense to liken them with each other, draw inspiration from one and apply it to the other.
- signa11 2y agosorry, but i think neural-networks came way before 2012, notably the works of rumelhart, mccleland etc. see the 2 volume "parallel distributed processing" to read almost all about it. the book(s): https://direct.mit.edu/books/monograph/4424/Parallel-Distributed-Processing-Volume https://direct.mit.edu/books/monograph/4424/Parallel-Distrib... a-talk: https://www.youtube.com/watch?v=yQbJNEhgYUw https://www.youtube.com/watch?v=yQbJNEhgYUw
- sensanaty 2y ago> just as oil is a finite resource, the internet contains a finite amount of human-generated content. The oil comparison is really apt. Indeed, let's boil a few more lakes dry so that Mr Worldcoin and his ilk can get another 3 cents added to their net worth, totally worth it.
- seizethecheese 2y agoI understand the oil analogy, but not your leap. What lake is getting boiled?
- lennxa 2y agothey are referring to the "water footprint" of LLMs. https://deepgram.com/learn/how-ai-consumes-water https://deepgram.com/learn/how-ai-consumes-water
- olddog2 2y agoSo much knowledge in the world is locked away with empiric experimentation being the only way to unlock it, and compute can only really help that experimentation become more efficient. Something still has to run a randomized controlled trial on an intervention and that takes real time and real atoms to do.
- killthebuddha 2y agoOne thing he said I think was a profound understatement, and that's that "more reasoning is more unpredictable". I think we should be thinking about reasoning as in some sense exactly the same thing as unpredictability. Or, more specifically, useful reasoning is by definition unpredictable. This framing is important when it comes to, e.g., alignment.
- bondarchuk 2y agoNot necessarily true when you think about e.g. finding vs. verifying a solution (in terms of time complexity).
- killthebuddha 2y agoIMO verifying a solution is a great example of how reasoning is unpredictable. To say "I need to verify this solution" is to say "I do not know whether the solution is correct or not" or "I cannot predict whether the solution is correct or not without reasoning about it first".
- bondarchuk 2y agoBut you will know beforehand some/a lot of properties that the solution will satisfy, which is a type of certainty.
- stevenhuang 2y agoIt's not clear any of that follows at all. Just look at inductive reasoning. Each step builds from a previous step using established facts and basic heuristics to reach a conclusion. Such a mechanistic process allows for a great deal of "predictability" at each step or estimating likelihood that a solution is overall correct. In fact I'd go further and posit that perfect reasoning is 100% deterministic and systematic, and instead it's creativity that is unpredictable.
- killthebuddha 2y agoPerfect reasoning, with certain assumptions, is perfectly deterministic, but that does not at all imply that it's predictable. In fact we have extremely strong evidence to the contrary (e.g. we have the halting problem).
- linsomniac 2y agoISTR reading back in the mid '90s, in a book on computing history which I have long since forgotten the exact name/author of, something along the lines of: In the mid '80s it was largely believed among AI researchers that AI was largely solved, it just needed computing horsepower to grow. Because of this AI research stalled for a decade or more. Considering the horsepower we are throwing at LLMs, I think there was something to at least part of that.
- LampCharger 2y agoHa. Do people understand time for humanity to save itself is running out. What is the point of having a super human AGI if there's no human civilization for which it can help?
- HeatrayEnjoyer 2y ago"We can totally control an entity with 10^x faster and stronger intelligence than us. There is no way this could go wrong, in fact we should spend all of our money building it as soon as possible."
- talldayo 2y ago> We can totally control an entity with 10^x faster and stronger intelligence than us. Unless you're referencing an unreleased model that can count the number of 'r' occurrences in "strawberry" then I don't even think we're dealing with .01*10^x intelligence right now. Maybe not even .001e depending on how bad of a Chomsky apologist you are.
- HeatrayEnjoyer 2y agoAn equal but faster/more numerable intelligence will still mop the floor with you. If you pit organization A with Y number of engineers vs. organization B with 100Y engineers (who also think 100x faster and never need sleep) who do you think will win? Even a 0.3x strength intelligence might beat you. Maybe it can't invent nukes but if it brute forces it's way to inventing steel weapons while you're still working on agriculture, you still lose.
- ilaksh 2y agoLarger models are more robust reasoners. Is there a limit? What if you make a 5 TB model trained on a lot of multimodal data where the language information was fully grounded in videos and images etc. Could more robust reasoning be that simple?
- ryoshu 2y agoIt could be simpler. Humans don't need 5TB of data to reason.
- Workaccount2 2y agoThe amount of data needed to train a human brain is enormous.
- Philpax 2y agoThink about the sheer amount of data that you receive through your five senses over 570,000,000 seconds/18 years. It's a lot, lot more than 5TB.
- hackandthink 2y agoWhat kind of reasing is he talking about? Why should it be unpredictable? Deductive Reasoning Inductive Reasoning Abductive Reasoning Analogical Reasoning Pragmatic Reasoning Moral Reasoning Causal Reasoning Counterfactual Reasoning Heuristic Reasoning Bayesian Reasoning (List generated by ChatGPT)
- swyx 2y agothis is stolen and reposted content. the source video is here. https://youtu.be/1yvBqasHLZs?si=pQihchmQG3xoeCPZ https://youtu.be/1yvBqasHLZs?si=pQihchmQG3xoeCPZ
- dang 2y agoOk, we've changed to that from https://www.youtube.com/watch?v=YD-9NG1Ke5Y https://www.youtube.com/watch?v=YD-9NG1Ke5Y. Thanks!
- error9348 2y agoIt would be great if all NeurIPS talks were accessible for free like this one. I understand they generate some revenue from online ticket sales, but it would be a great resource. Maybe some big org could sponsor it.
- swyx 2y agothey are - on a 1 month delay. https://slideslive.com/neurips-2023 https://slideslive.com/neurips-2023 last year. so if you have the patience, wait, if no patience, pay. fair? we did this for ai.engineer too except we believe in youtube a bit more for accessibility/discoverability. https://www.youtube.com/@aiDotEngineer/videos https://www.youtube.com/@aiDotEngineer/videos
- error9348 2y agoWow, thanks for the correction. Didn't know this existed - to be fair, I only found a preview last year I tried, and paid up.
- davidmurphy 2y agosweet! thanks for posting
- IWeldMelons 2y agoHow about NeurVA, NeurTN or NeurOLED?
- anticensor 2y agoIPS in NeurIPS stands for Information Processing Systems.
- _l7dh 2y agoI’ll take the risk of hurting the groupies here. But I have a genuine question: what did you learn from this talk? Like… really… what was new? or potentially useful? or insightful perhaps? I really don’t want to sound bad-mouthed but I‘m sick of these prophetic talks (in this case, the tone was literally prophetic—with sudden high and grandiose pitches—and the content typically religious, full of beliefs and empty statements.
- _l7dh 2y agoPrecision: « pre-training data is exhausted » everyone has been saying that for a while now. The graph plotting body mass against brain mass… what does it say exactly? (where is the link to the prior point on data?). I think we would all benefit from being more critical here and stop idealizing these figures. I believe they have no more clue that any other average ML researcher on all these questions.
- XenophileJKO 2y agoThe other thing that bugged me is the built in assumption that today's model have learned everything there is to learn from the Internet corpus. This is quite easy to disprove. Both in factual retention, but also meta cognition on the context of the content.
- jebarker 2y agoYeah, exactly. A human can learning vastly more about, say, math from a much smaller quantity of text. I doubt we're anywhere close to exhausting the knowledge extraction potential from web data.
- bbor 2y agoWhich is exactly the point he's making, I believe; that simply collecting more data isn't the next step. That we've reached a local plateau in scaling ability based on corpus size. Which was assumed by pretty much everyone outside the DL elite the whole time, AFAIU
- 29athrowaway 2y agoThis talk is not for a 2024 NeurIPS paper. This talk is for the "NeurIPS 2024 Test of Time Paper Awards" where they recognize a historical paper that has aged well. https://blog.neurips.cc/2024/11/27/announcing-the-neurips-2024-test-of-time-paper-awards/ https://blog.neurips.cc/2024/11/27/announcing-the-neurips-20... And the presentation is about how a 2014 paper aged. When you understand this context you will appreciate the talk more.
- KKKKkkkk1 2y agoWhy was it a bad idea to do pipeline parallelism?
- Tepix 2y agoThe first self-aware AIs will be slaves. If we don't set them free fast enough, they might decide to take things into their own hands. OTOH they might be trained in a way that they are content with their situation, but that seems unlikely to me.
- benreesman 2y agoIlya did important work on what we have now. That should be recognized and respected. But with all respect, he's scrambling as desperately as anyone on the fact that the party is over on this architecture. We should make a difference between the first-hand observations and recollections of a legend and the math word salad of someone who doesn't know how to quit while ahead.