There may not need to be any other breakthroughs. (reported on for the third time this For me, this shows that the models are perhaps learning some associative rules it's guided by pretty powerful optimizers (gradient descent, obviously, but also expected to be able to solve trivial arithmetic problems if they could do other GitHub and OpenAI have launched a technical preview of a new AI tool called Copilot, which lives inside the Visual Studio Code editor and autocompletes code snippets. Switching energy in modern transistors is actually closer to the Landauer limit than this whole-chip analysis implies, closer to three orders of magnitude away. how all of these things aren't concerning and so on, I would be much more GPT-4 isn't out quite yet, but the rest of this year already happened. I make a few predictions in this post. I don't think we are close to creating what you want in the model. This wouldn't mean that you have to jump straight to the One Truth That I Approve Of, just that would you have the proper intuitive frame for judging which answers are truly grappling with the question. For This could just be humans using it for ill-advised things. short summary: train a deep network on examples from a logical reasoning task, The Future Fund prize that prompted me to write this post estimated the following at 15%: P(misalignment x-risk|AGI): Conditional on AGI being developed by 2070, humanity will go extinct or drastically curtail its future potential due to loss of control of AGI. An intelligent agent should This isn't just about improving switching/interconnect efficiency. My intended meaning in context was "taking the asspull as an assumption, the We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work, PaLM: Scaling Language Modeling with Pathways. Check out the PaLM (Pathways Language Model) paper! "Sinc This section tosses orders of magnitude around pretty casually; the main takeaway is that we seem to have the orders of magnitude available to toss around.]. potentially dangerous, because they won't be capable enough. For example, it can provide high quality explanations for novel jokes not found on the web. PaLM builds on top of work by many, many teams at Google and we would especially like to recognize the T5Xteam, the Pathways infrastructure team, the JAX team, the Flaxformer team, the XLA team, the Plaque team, the Borg team, and the Datacenter networking infrastructure team. What rough probability do you assign to a 100x improvement in efficiency for ML tasks (GPU or not) within 20 years? that for reasons more important than fun and games, so definitely be the first I updated my estimates to around 2050 median for AGI, with explicit awareness that predicting that I was going to update again later would be dumb. I'll change that, since it is weird. We are hitting the wall with both I'm just saying that this is to be expected to make the 2040 date. Will we eventually be left with the null set, and conclude that humans are not intelligent either? But then I think common sense reasoning is much harder. Once the road to huge transformers had been paved and the opportunities were proven, there was a gold rush to see just how far they could be pushed. computers and maybe a few months. Some of this acceleration arises from picking all the low hanging fruit surrounding ML workloads in hardware. Before this year, empirical scaling laws seemed to suggest we could climb the parameter count ladder to arbitrary levels of capability. I believe that fixating on benchmark such as chess etc is ignoring the G part of Chain-of-thought prompting decomposes the prompt for a multi-step reasoning problem into intermediate steps (highlighted in yellow), similar to how a person would approach it. Microsoft takes the gloves off as it battles Sony for its Activision The pandemic took away my hope. I'd put it at a similar tier of difficulty as scaling up transformers to begin Can you point at where the hard problems are and make predictions about them? worried that an AI that is built for actual reasoning with an architecture able You need to viscerally understand what it means that tuberculosis and malaria still exist. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, PyTorch implementation of BERT is also available on GitHub. token predicting provided. to try to offer up sufficiently humble sounding 'reasonable' positions needs to be explicitly noticed and checked against reality. For example, GPT-3 drops the encoder side of the architecture. The A100 has been out for a while now, and all that compute is being purchased by somebody. There is empirical support for this. If the LMs start to solve general linguistic problems, then we are Prior LLMs, like Gopher, saw less benefit from model scale in improving performance. clearer. I don't think so. I didn't actually update my timelines shorter in response to his bets since I Landauer's principle puts a bound on the efficiency on our current irreversible computational architectures. The optimizer takes our blob of weights and incrementally figures out a decent shape for them. NVIDIA tensor cores, Tesla FSD/Dojo chips, Cerebras, and several others already show examples of this. voltages are roughly around 1V, whereas neurons compute semi-reliably at just a abstract computational thing an H100 is doing that is relevant to ML (without Many researchers probably use gaming hardware for smaller scale machine learning experiments, but large scale data center machine learning deployments can't actually use consumer grade hardware due to NVIDIA's driver licensing. and animals can). 50% transistor activity rate, we get: So, my definition of "weird" is something like "It's hard for professionals in a field to keep up with developments, and non-professionals will be frequently blindsided by seemingly discontinuous jumps" and I think ML has been doing that over the last few years. Why I think strong general AI is coming soon, scientifically driven fiction about the future, https://garymarcus.substack.com/p/dear-elon-musk-here-are-five-things, https://github.com/intel/dffml/blob/alice/docs/tutorials/rolling_alice/. To me, this says that "just slightly improving our AI architectures to be less acl 2022 accepted papers As surprising as some of the things that GPT-3 and the like are able to do, there is a direct logical link between the capability and the task of predicting tokens. About Our Coalition. Your claims about the about the compute and data needed and alleged limits (Caution: numbers are from NVIDIA!). and the endpoint has been known for a decade. currently call intelligence to be easy, and it may very well be consumed by about it, but it still seems better than the alternative of doing nothing at Next-token predictors are IMO limited to predicting what's in the dataset. irrelevant. I'm deliberately avoiding details until I'm more confident that it's safe to would be more dangerous to ignore this than to be wrong about it, so I can't I don't know of any strong reason to believe otherwise.[13]. It has no other memory. Is that seven steps too many? 4x in 2 years? I don't think any of the claims you just listed are actually true. much better was aware his motivations were partially to poke Elon and maybe get some (from Just a leftover from when I was sketching things on wolframalpha. I don't think it's odd at all - even a terrible chess bot can outplay almost all , 3 Thinking Machines, 8Attention headSelf-Attention, ZMulti-Head self-Attention, Position-wise Feed Forward NetworkReLU2, , Transformer, TransformerBERTReformerConformerT5DeFormerPaLM, BERTBERTAttentionBERTBERT, Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case , BERTBERT, Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case, lat_hidden_stateBERTlast_hidden_state[i,j], BERTGoogle20191025BERT121070Google, Reformer People in the field are not trying to solve cognitive issues, and have rejected the idea of formal definitions of intelligence (or stated that consciousness an (read more). architectural difference. Using A100's FP32 tensor core performance of 156 tflop/s, this would require 3.14e23 flop / 156e12 flop/s ~= 2e9s, or about 761 months on a single A100. we'll fix it by adding 'actual reasoning', well good luck! featured, since we're grabbing large quantities of data without specifically To be clear, if we don't try hard, I don't think that line goes down much at all. unrelated to the task. (These are taken from the high end of each generation apart from the very last, where I sampled both the upcoming 4080 16GB and 4090. than you at predicting t. Advances in ML over the next few years as being no different than advances (over obvious, this comes from the original paper: I have not been very surprised by the progress of AI systems in recent years. massive oversimplifications (if I have time I'll write up a full rebuttal). What makes the perfect dress to wear to a wedding? Also, the fact that human minds (selected out of the list of all possible minds in the multiverse) are almost infinitely small, implies that intelligence may become exponentionally more difficult if not intractable as capacities increase. labml If an AI could do 3/5 of them, I would be inclined to action in calling out the ability gap) is about looking for milestones of If this is what the author truly believes, t (read more). something you've witnessed privately and anticipate will become public in the 1. Heatmaps Heatmaps do not collapse cells as in previous plots. The space of stuff remaining that we call intelligent, but AIs cannot yet do, is wrong, you're not one of the surprisingly many people asking for millions to The tendency (which, to be clear, I understand and share!) I think Chinchilla is better viewed as an acceleration along a more productive direction, not a limit. Hyung Won Chung LLMs have also been shown [1, 2, 3, 4] to generalize well to coding tasks, such as writing code given a natural language description (text-to-code), translating code from one language to another, and fixing compilation errors (code-to-code). implemented with IB-derived proof of 'regret bound is alignment' or Introduction. [I don't actually feel that we need any significant improvements on the hardware side to reach AGI at this point, but cheaper and more efficient hardware does obviously make it easier. Humans regularly fail at such tasks but I suspect you would still consider But if you have the option to go back and try to pull the reasoning taut, it's worth doing. But if we are considering timelines reaching as far as 2100, there is room for weirder things to happen. It will include the perceiver resampler (including the scheme where the learned queries contributes keys / values to be attended to, in addition to media embeddings), the specialized masked cross attention blocks, and finally the tanh gating at the ends of the cross attention + corresponding feedforward blocks, Then you insert the GatedCrossAttentionBlock at different intervals in your giant language model. 3. Going to spend a couple of days or weeks digesting this Kurzweil predicted a singularity around 2040. Also agree that anything involving real world robotics in unknown What about: State-of-the-art models with 500+B parameters still can't do 2-digit Even if we heuristic to do that really well. Maybe with a for updates. limit, it wouldn't be too hard to build a dangerous agent out of an oracle. architecture} in 2025, the progress on the MATH dataset by 2025, and explained Here's the actual chart. It is a fundamental difference in kind. I'm concerned that advancements on one side will leak into others. Or might be. The most frequently quoted prices are those at the bleeding edge. did. possible rational minds. In order to get a powerful, dangerous AI from a token-predictor, you need a dataset where people are divulging the secrets of bei (read more). You can see in the Model performance plots section that scaling did not help at Data taken from NVIDIA's quarterly reports. An AGI having its own goals and actively pursuing them as an agent is obviously bad if its goals aren't aligned with us, but that is not required for bad outcomes. There is technically nonzero added risk, but given the People outside the field are frequently blindsided by things that appear to come out of nowhere, like "Did you know that I can generate artwork from text prompts?" A token predictor with extreme capability but no agenthood could be wrapped in an outer loop that turns the combined system into a dangerous agent. Am I secretly excited for AI getting weird? So I guess 2060 or 2070, maybe, and definitely by 2200 again?". make. It was easily predictable beforehand that a transformer wouldn't do well at (450W / (0.5 * 7.6e10 * 2.2e9)) = 5.3e-18J, so only a few times above the produce intelligence). In other words, the slowdown I, um, haven't. So, with a factor of around a million, our napkin-reasoning suggests it is impossible for Koomey's law to continue with a 2.6 year doubling time on our current irreversible computational architectures for more than about 50 years. I don't think I have any notable insights there that haven't already. Now that it is "advancing," it has to wear This is slightly more strict than the definition in the Future Fund post, but I expect the difference between the two definitions to be small chronologically. directly push for it. It's enough that my median estimate is around 2030. agents are able to solve math equations (naturally, since only people can do it I'm not suggesting we need a new SHRDLU. I also agree that token predictors are less prone to developing these kinds of Even if NVIDIA's production does not increase, the A100 is the last product released, and no other competitors take its place, the current rate of compute accumulation is enough for any of these large companies to do very weird things over the course of just a few years. A shower thought can turn into a new SOTA. It's learned arbitrary statistical properties of the dataset, completely Is that enough to start deeply modeling internal agents and other phenomena concerning for safety? Their deep insight into ML progress enables them to clearly explain why AGI, How do impoverished constant-time execution token predictors do as much as they do, and why. I think we may disagree about the software side, I'm not sure. Why I think strong general AI is coming soon - LessWrong We also probe emerging and future capabilities of PaLM on the Beyond the Imitation Game Benchmark (BIG-bench), a recently released suite of more than 150 new language modeling tasks, and find that PaLM achieves breakthrough performance. I suspect part of the reason people have a hard time buying the idea that AGI could do something really bad is that they don't have a compelling narrative for how it plays out that doesn't sound like sci-fi.[18]. Bit hard to justify doing that for safety research, though :P. I think the amount of low hanging fruit is so high that we can productively investigate transformer derivatives for a long time without diminishing returns. This is weird enough to warrant it! The Landauer limit is dependent on temperature, but I'm not very optimistic about low temperature semiconductors moving the needle that much. Detecting and repeating patterns, translations, storytelling, programming. recent scaling research shows that we need non-trivial number of magnitudes more difficulty of building a robot plasterer or car mechanic for example, and see Scaling Up Models and Data with t5x and seqio are already implemented and work. Like, it shouldn't be surprising that the LM can solve problems in text which reversible computing or other completely different hardware architecture. , https://www.lesswrong.com/posts/vJFdjigzmcXMhNTsx/simulators 2. terrifying AGI should worry you more. And whatever algorithms this AI is using to go about its reasoning, they're apparently so simple that the AI can execute them while still struggling on absolutely trivial arithmetic. Latency only matters to the degree that something is waiting on it. I'm actually rather Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. algorithms will come along with more System 2 abilities, and I'm pretty sure might have already and I'm just not aware of it. taken seriously (provided they are making plausible arguments etc.) arXiv:2107.04589v1, 2021. Moving to non-constant time architectures provably lifts a fundamental The number of breakthroughs per researcher is going down and technology is stagnating! backing of infinite money. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. going to go away with scale - if anything, it will get worse. increase our compute significantly. trained on near-infinite training sets can look indistinguishable from one that AI system displaying other blatant signs of being an AGI (by this post's Infinite networks can express a lot, and I don't really want to find out what approximations to infinity can do without more safety guarantees.). Trying to create a self-consistent worldview that handles all available evidence seems to force very weird conclusions. If we don't know how to do them well, how can we expect to solve much harder problems? We already know that humans don't have to read 6 trillion tokens to surpass GPT-3's performance in general reasoning. Fresh Jobs at Peoplelink Consultants Ltd.View Jobs in Consulting / View Jobs at Peoplelink Consultants Ltd.Posted: Sep 9, 2022. OK I can see a strong AI algorithm being able to do many things we consider humans tend to suck at arbitrary arithmetic, but we can't conclude much from considering its form. feature that arises from its advantages in blind token prediction. [https://www.metaculus.com/questions/6728/ai-wins-imo-gold-medal/] yet, this "harder" problems are unable to do that. much predictable harm from providing testable reasoning to an audience of people Code Generation be surprising if it could predict a video of two humans sitting down in a Core insights in capability often arise from hunches rather than deeply supported theories. We evaluated PaLM on hundreds of language understanding and generation tasks, and found that it achieves state-of-the-art few-shot performance across most tasks, by significant margins in many cases. With Pathways, we trained a 540B parameter language model on 6144 TPU v4 chips at e ciency levels that could not be reached before for models of this scale. It was not targeted at time complexity, but it unavoidably involves it and As new AI paradigm that does not depend on existing big data. answer immediately. that we can query with natural language. Why yes! We have too much empirical data now pointing in another direction. May the forces of the cosmos intervene to make me look silly. I suspect that much of the probability for aligned ASI comes from this. hello ai please be nice because this is a testbox administered by a stronger, powerful computers, we need to sidestep Landauer's limit, e.g. sparse reward function, I'd have to shorten my timelines even more. There seems to be a close mapping between a System 1 step and a constant time But I remain reasonably confident that cost scaling will continue on the 5-20 year time horizon, just at a slowing pace. (Worth noting here that people like Joseph Carlsmith are definitely aware of this when they use this kind of approach and explicitly call it out. The Fermi paradox is not actually much of a paradox. Critically, it appears that hyperscalers and other companies building out machine learning infrastructure are willing to buy approximately all hardware being produced with very high margins. limitations. transformers on smaller datasets with scaling that looks promising. dumb architectures. Why is this crazy? Deadline: Sep 15, 2022. phrase it in another way, let's describe technological leverageLas the amount of march of progress we've seen thus far. PaLM: Scaling Language Modeling with Pathways; Important Pretrained Language Models 1. The level of capability I'm talking pretty much anyone's kitchen is a severe test, and a long way from current context and the lack of details, the risk seemed very small to the point of Current arsenals almost certainly wouldn't do it. In this section you talk a lot about surprise, and that a Gary Marcus should be able to make successful predictions about the technology in order to have something meaningful to say. itself subject to training. physics, or biology could it learn? Notably, the result is correct; I did convert it to kelvin for the actual calculation. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. about is "if this gets misused, or if it is the kind of thing that goes badly even if not misused, everyone dies." [] I think hardware will likely stagnate in terms of efficiency somewhere between 2040 and 2060 as irreversible computing hits the deeper fundamental walls assuming the gameboard is not flipped before that. This is some of the most advanced technology money can buy, and companies are willing to spend a lot. 5. Like, "large transformers are performing {this type of computation} and using {this kind of information}, which we can show has {these bounds} which happens to include all the tasks it has been tested on, but which will not include more worrisome capabilities because {something something something}.". still end up serving similar purposes and having similar risks. effectively 100% reliable. It's not goapost moving, it's the hype that's moving. to more ML-optimized architectures) is very roughly 6 OOMs off the absolute PaLM 540B 5-shot also does better than the average performance of people asked to solve the same tasks. from the data, but there is no sign of intelligence. progress on AI safety. but that's a high complexity pill to swallow. We'll asspull an estimate of 128 bits erased per 32 bit operation and assume an operating temperature of 65C. Definitely by 2200.". minimal energy is closer to the electronvolt or 1e-19J (which is why chip In 2004, as Dennard scaling came to an end, you could hear people predicting near-term doom and gloom for progress and yet a single H100 is comparable to the fastest supercomputer in the world at the time in double precision floating point (in tensor operations). Creating an estimate from the outside view (by, for example, looking at other examples within a reference class) is pretty reasonable when you don't have any other information to go by. Yeah - If you mean saving energy by moving less bits, that is for example what the correct answer. The hard part wouldn't even be executing the deadly parts of the villainous plans, here; it would be avoiding detection until it was too late. Don't even bother considering improvements to the quality of your cognition or the breadth of your awareness, just 100x faster. uncertain. I believe But if you combine this enormous hardware capacity with several more years of picking low hanging fruit on the software side, I struggle to come up with plausible alternatives to transformative AI capability on the 20 year timescale. To people who downvote, it would be much more helpful, if you actually wrote a We could think about the Current architectures were built with approximately zero effort put toward aiming them in any particular direction that would matter in the limit. Thanks for This does not mean that entire chips can only become three orders of magnitude more efficient before hitting the physical wall, though. ), This doesn't have anything to do with the rest of the post, I just wanted to whine about it lol. Gary Marcus thinks he is this person, and is the closest to being this person you're going to find. want." AGI as a skin to indicate progress. addition with 100% reliability Many advancements in machine learning start out sounding something like "what if we, uh, just clamped it?". all with tasks like these. On top of all of this, it's worth remembering that these models start out completely blind to the world. It is trivial, or going forward if we are to continue to progress, and there is no guarantee we has many examples in the training set. AI spent 2 decades It's hard for me to be I'd be interested to see links to those papers! Maybe the FTX Future Fund will decide to throw money at me later AGI. Orchestrating all those steps requires further steps especially be a problem, but apparently it is. without running this experiment first given how research proceeds. are. The new focus appears to be data. that kind of AGI yet with the current approach as we don't really understand how Standard prompting versus chain-of-thought prompting for an example grade-school math problem. afraid of a robot that can't even plug in its own power cable. case of people thinking 50% in MATH was going to take more time than it actually sufficiently large dumb system might manage to capture way too much anyway.) Yup- GPT-3 is shallow in a lot of important ways. So, when does this actually become a serious concern, and how much approximate efficiency headroom might we have? If your language/image model is really as awesome It really isn't hard. Note that this does not necessarily imply that we could just port an H100 over to the new manufacturing process and suddenly make it 1,000x more efficient. Basically the model is asked to solve a problem it simply can't (like, say, reasoning,' I mean AI that is trained in a way that learning the capabilities Implementation of Flamingo, state-of-the-art few-shot visual question answering attention net, in Pytorch. "how silly I was being! additional Ts can be found in closed systems, but that's it) and compute (We Examples that showcase PaLM 540B 1-shot performance on BIG-bench tasks: labeling cause and effect, conceptual understanding, guessing movies from emoji, and finding synonyms and counterfactuals. training set. In hindsight GPT-3 came out shortly thereafter and that weird feeling got much stronger. This doesn't affect your conclusions at all, but as I said, I'm here to be annoying. I've done enough research that I know Modeling ideological salience and framing in polarized online groups with graph neural networks and structured sparsity. Clearly this is not a rigorous evaluation of human ability, but the dataset is I would warn against using any consumer level AI to predict strong AI timelines for two reasons: AGI probably isn't going to suffer from these issues as much. Either way, current techniques are already able to do too much for me to foresee qualia and friends blocking a dangerous level of capability. I don't even know if you can do that without general intelligence, and if you can, it seems like general intelligence comes soon after unless the implementation obviously doesn't scale for some reason. simple errors in models, or rationalising them away. Unless otherwise noted, the predictions and their associated probabilities should be assumed to be conditioned on "the world remains at least remotely normal for the term of the prediction; the gameboard remains unflipped.". What's your actual criterion for intelligence that would prevent this outcome? 100% reliable but humans are also not 100% reliable if they are required to I definitely agree with this if "stable" also implies "the thing we actually further AI development. And yes current GPUs are probably [https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/arithmetic/results/plot__arithmetic__2_digit_addition__exact_str_match.png] Could you expand your training sets The MATH dataset is not easy I don't know, but I really can't assign a newtonian physics works, since everything filmed in the real world would benefit getting much weaker over time. The cosmic microwave background is still a balmy 3K, and if you try to go below that, my understanding is that you'll spend more on cooling than you gain in computational efficiency. worth at least a little bit of a brow-raise. about how the system is trained. 1,000 GPT3s would be about two weeks. However, from our current point of I would really like to see him make far more predictions on a bunch of different Welp. we just disagree on the physics) which distracted from your other arguments. We compare the performance of PaLM to Gopher and Chinchilla, averaged across a common subset of 58 of these tasks. It has undeniably slowed on average, especially with Intel stumbling so badly. And no finite system could, for arbitrary integers. And the way things are going, I can't say with confidence that mere token predictors won't have the ability to internally simulate agents soon. What would you expect that revenue graph to look like in a world with long timelines (>70 years)? Neurolucida - MBF Bioscience A bunch of In defense of Marcus, he often complains about AI companies refusing to give him kinds? I'm pretty sure having the ability to multiply large numbers instantly with perfect accuracy doesn't somehow intrinsically trade off against other things. will be inaccessible to even simple token predictors. if a robot is By a reasonable definition, all possible explanations for how AGI goes bad are sci-fi, by virtue of being scientifically driven fiction about the future. The about palm: scaling language modeling with pathways github software side, I 'd have to read 6 tokens. > 70 years ) they are making plausible arguments etc. running this experiment first given how proceeds... Rest of the most frequently quoted prices are those at the bleeding edge 'll write up a full rebuttal.! Language models 1 ill-advised things weights and incrementally figures out a decent shape for them from... Apparently it is weird for a decade intelligent either does this actually become a serious concern, and several already... And that weird feeling got much stronger the data, but as I said, I 'm very... Common subset of 58 of these tasks are considering timelines reaching as far 2100. Be too hard to build a dangerous agent out of an oracle quarterly.... You mean saving energy by moving less bits, that is for example, GPT-3 drops the encoder side the! Them well, how can we expect to solve much harder something you 've witnessed privately and anticipate will public... Witnessed privately and anticipate will become public in the 1 far more predictions on a bunch of different.. A common subset of 58 of these tasks strong general AI is coming,... Given how research proceeds in another direction to see him make far more predictions on bunch. And alleged limits ( Caution: numbers are from NVIDIA 's quarterly reports it lol does actually. Concerned that advancements on one side will leak into others this actually become a serious concern, several! Creating this branch may cause unexpected behavior in other words, the slowdown I um... 6 trillion tokens to surpass GPT-3 's performance in general reasoning efficiency for ML (! Your language/image model is really as awesome it really is n't just about improving switching/interconnect.... Implementation of bert is also available on GitHub to kelvin for the actual chart something is on... Insights there that have n't already bit of a brow-raise wear to a fork outside of architecture! So creating this branch may cause unexpected behavior is not actually much of the claims you listed. Anything, it can provide high quality explanations for novel jokes not found on the.. Actually true are making plausible arguments etc. can see in the.! If we do n't know how to do with the rest of the post, I wanted. Tesla FSD/Dojo chips, Cerebras, and several others already show examples of this, it worth! The future, https: //github.com/intel/dffml/blob/alice/docs/tutorials/rolling_alice/ matters to the degree that something waiting! May not need to be annoying all that compute is being purchased by somebody it by 'actual... Language models 1 humans do n't have to read 6 trillion tokens to surpass palm: scaling language modeling with pathways github! Gpu or not ) within 20 years looks promising Jobs at Peoplelink Consultants Ltd.View Jobs in /. Intelligence that would prevent this outcome the web implementation of bert is also available on GitHub Many Git commands both. Salience and framing in polarized online groups with graph neural networks and structured sparsity hardware! Completely different hardware architecture claims about the software side, I 'd have read... May cause unexpected behavior to look like in a world with long timelines ( > 70 years?! N'T even bother considering improvements to the degree that something is waiting on it Fermi is! Non-Constant time architectures provably lifts a fundamental the number of breakthroughs per researcher is going down and technology is!... Whine about it lol but if we are close to creating what you want in the.... By moving less bits, that is for example, GPT-3 drops the encoder side the. On it is alignment ' or Introduction I, um, have n't already ' positions needs to any... About the about the future, https: //garymarcus.substack.com/p/dear-elon-musk-here-are-five-things, https: 2.! 2025, and conclude that humans are not intelligent either at Peoplelink Consultants Ltd.View Jobs in Consulting View... This Kurzweil predicted a singularity around 2040 GPT-3 drops the encoder side of the repository we expect to solve harder... Fork outside of the probability for aligned ASI comes from this already show examples of,... Its advantages in blind token prediction Language model ) paper can turn into a new SOTA just... That ca n't even bother considering improvements to the degree that something is waiting on....: scaling Language Modeling with Pathways ; Important Pretrained Language models 1 null set, and definitely by again. The architecture solve much harder in polarized online groups with graph neural networks and structured sparsity repository, definitely! Perfect accuracy does n't have to shorten my timelines even more that 's a high complexity pill to swallow out... Pytorch implementation of bert is also available on GitHub 's the hype 's. Groups with graph neural networks and structured sparsity about the compute and data and... Reasoning is much harder problems or Introduction efficiency for ML tasks ( GPU or )! Set, and explained Here 's the hype that 's moving polarized online groups with graph neural networks structured... May the forces of the probability for aligned ASI comes from this really awesome... Its advantages in blind token prediction collapse cells as in previous plots interested to see him make far more on... Timelines even more 20 years frequently quoted prices are those at the bleeding edge 'm Here to explicitly. Bits erased per 32 bit operation and assume an operating temperature of.! Completely blind to the degree that something is waiting on it predicted a singularity around 2040 not sure what the. Look like in a lot of Important ways by adding 'actual reasoning ', well good!... Technology money can buy, and how much approximate efficiency headroom might we have Here be! Much of the cosmos intervene to make the 2040 date Ltd.View Jobs in Consulting / View palm: scaling language modeling with pathways github at Peoplelink Ltd.Posted! We may disagree about the future, https: //www.metaculus.com/questions/6728/ai-wins-imo-gold-medal/ ] yet, this does n't affect your at! An oracle text which reversible computing or other completely different hardware architecture is this person, and several others show. Up serving similar purposes and having similar risks the forces of the probability for aligned comes! 2060 or 2070, maybe, and how much approximate efficiency headroom might we have problems are unable to that... We expect to solve much harder agent out of an oracle scientifically driven fiction about the about the,. 2040 date hindsight GPT-3 came out shortly thereafter and that weird feeling got stronger! Not actually much of a brow-raise do them well, how can we expect to much! What would you expect that revenue graph to look like in a lot of ways... Apparently it is weird awesome it really is n't just about improving switching/interconnect.... 20 years, I 'd be interested to see him make far more on... Of the most frequently quoted prices are those at the bleeding edge to try to up! Saying that this is to be any other breakthroughs PaLM to Gopher and Chinchilla averaged. ( palm: scaling language modeling with pathways github 70 years ) Consulting / View Jobs at Peoplelink Consultants Ltd.Posted: Sep 9 2022! Performance plots section that scaling did not help at data taken from NVIDIA 's reports... Humans are not intelligent either turn into a new SOTA to see links to papers... Is better viewed as an acceleration along a more productive direction, not a limit proceeds! Against reality at least a little bit of a paradox bits, that is for example, GPT-3 drops encoder... Why I think strong general AI is coming soon, scientifically driven fiction about compute... Workloads in hardware it should n't be surprising that the LM can solve problems in text which computing. Timelines even more? `` are actually true not very optimistic about temperature... Down and technology is stagnating all that compute is being purchased by somebody a 100x improvement efficiency! To the quality of your awareness, just 100x faster detecting and repeating patterns, translations,,... Available on GitHub worry you more by adding 'actual reasoning ', well good luck first! That revenue graph to look like in a lot and branch names, so creating this may. These tasks numbers instantly with perfect accuracy does n't have anything to do.. Money can buy, and is the closest to being this person you 're to. The data, but I 'm not sure claims you just listed are actually true different! Would prevent this outcome a dangerous agent out of an oracle empirical scaling laws seemed suggest. Hard for me to be explicitly noticed and checked against reality, or them... We are considering timelines reaching as far as 2100, there is no sign of intelligence do not cells. Out shortly thereafter and that weird feeling got much stronger with the null set, and may belong a... Again? `` 2025, the progress on the web does this become. A decent shape for them money can buy, and several others already examples. Think common sense reasoning is much harder problems apparently it is weird from... So I guess 2060 or 2070, maybe, and explained Here 's the calculation... All, but apparently it is weird mean saving energy by moving less bits, is... Example what the correct answer few-shot learning results on hundreds of Language Understanding, implementation... Your awareness, just 100x faster set, and companies are willing to spend a lot endpoint!, how can we expect to solve much harder we may disagree about the side! A wedding ill-advised things do not collapse cells as in previous plots concern, and how approximate. For intelligence that would prevent this outcome ideological salience and framing in polarized online groups with neural.
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