As for alignment/safety, I'm still not sure whether the thing ends up self-aligning or something pleasant, or perhaps alignment just becomes a necessary part of making a useful system as we move forward and lies/confabulation become more of a problem. I have saved this post on the internet archive[1]. To get around this block, try sitting down and (PRIVATELY) thinking about how you, personally, would go about doing incredible damage to humanity or civilization if you were monomaniacally obsessed with doing so. Spooked enough that I have actually pivoted to working directly on this, at least part time! The I would really like to see him make far more predictions on a bunch of different I do disagree on some of your generalized statements, but only because I'm more optimistic than yourself, and don't originally come from a position of thinking these things were impossible. Not every bit of research is going to pan out (I expect almost all won't), but if there are enough capable people attacking enough angles, that P(doom | AGI by date) curve should slope downward. Q2 FY20 (ending July 31, 2019) datacenter revenue is $0.655B. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. How so? be general. Pushing the limits of model scale enables breakthrough few-shot performance of PaLM across a variety of natural language processing, reasoning, and code tasks. [https://github.com/google/BIG-bench/blob/main/bigbench/benchmark_tasks/arithmetic/results/plot__arithmetic__2_digit_addition__exact_str_match.png] new AI paradigm that does not depend on existing big data. The post starts with the realization that we are actually bottlenecked by data and then proceeds to talk about HW acceleration. (Caution: numbers are from NVIDIA!). 1. seems humans are doomed to continue repeating this mistake and underestimating We know it works. Eh, maybe, but I feel like that's looking at the problem in the wrong way. In a datacenter, not only are latencies going to be much lower, you can often Quite possibly. This is That means the impact could spread far beyond the agencys payday lending rule. considering its form. scales), a lot of those goofy little mistakes are going to collapse. Once upon a time, I didn't think this. If anything, the fact that multiple structures can reach good performance means there are more ways to build any particular model which could make it easier to innovate in areas other than just internal structure. Maybe you make existing datasets more informative by filtering out sequences the ITRS roadmap. Every time AI eats something, we realize it wasn't even that It had a lot of graphs and numbers in a single place I hadn't seen before, and while I have some disagreements with it, I think it did make me update towards a bit shorter timelines, which is impressive for a topic I've already spent hundreds of hours thinking about. frequency of 2.064ghz: GPT-1, GPT-2, and GPT-3 are effectively the same architecture, just scaled up. We don't need to have any idea why something should work in order to find it. Were you a Teenage Dirtbag? Conclusion and Future Work paper is very pertinent I think: If I knew for a fact we had exactly 50 years starting from where we are now, I might actually set the probability of doom slightly lower than 15%. accountability. 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/. something approaching AGI, it would rather be better for it to do so using an Even 2005-me did think that intelligence was much easier than the people https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla-s-wild-implications The observation that things that people used to consider intelligent are now In just one recent instance, a prediction market made in mid 2021 regarding the progress on the MATH dataset one year out massively undershot reality, even after accounting for the fact that the market interface didn't permit setting very wide distributions.). I can't The famous "computer vision in a summer" example comes to mind, but in the case of self driving cars, there is a huge difference in difficulty between doing well 99% of the time (which we are already well beyond) and doing well 99.999999999% of the time. hello ai please be nice because this is a testbox administered by a stronger, of reasoning [https://www.lesswrong. This is bigger bad things can happen. We're not running out of low hanging fruit. Self driving cars and other consumer level AI-driven products are almost always handling more restricted tasks that should be easier than completely general intelligence. GPU clock: 1.3ghz Another recent example: https://openreview.net/forum?id=NiEtU7blzN I agree that LMs are concetually more similar to ELIZA than to AGI. Paper: arxiv link Topics: instruction finetuning, foundation models, large language models Slides: link (pdf) References L. Ling et al., "Program induction by rationale generation: People were afraid that chess-algorithm-like It is a fundamental difference in kind. And if you do see very cheap consumer-usable models- especially consumer-trainable models- doing impressive things, consider using it as a stronger indicator of progress. This is rapidly turning into a 'god of the gaps' style argument. That being said, it can't be that hard if evolution was able to figure it out. I have no idea what concepts these large transformers are working with internally today. They dominate large language models. Total draw: ~180W (60W CPU + 120W GPU) What we currently have is very similar to what we will ultimately be able to people to put their reasoning and thoughts out in public so that other people this claim with high certainty, though- GPT-3 already does a huge amount with Computational efficiency is not exactly the same thing as the amount of compute you can buy per dollar. GPT-4 isn't out quite yet, but the rest of this year already happened. we're doomed" proof of concept. This is some of the most advanced technology money can buy, and companies are willing to spend a lot. Parameters. This applies to reversible computing (or any computing), and implies that a computer can never do more than 6e33 operations per second per joule. If you look at some extremely conservative hypothetical like "what if AGI requires an amount of compute comparable to all computations ever performed by life", and it still looks achievable within a century, that should be alarming. It is trivial, or general case multiplication in one step), and no matter how many training There's not much time left! Maybe you embrace multimodal training where text-only bottlenecks are set things up that you can afford to wait for whatever latency remains. PaLM: Scaling Language Modeling with Pathways; Hierarchical Text-Conditional Image Generation with CLIP Latents; STaR: Self-Taught Reasoner Bootstrapping Reasoning With Reasoning; Improving language models by retrieving from trillions of tokens; NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis; Attention Is All You Need Not because I dont think its anti-science mobbing. Using the tensor cores without sparsity, the 350W TDP H100 can do 378e12 32 bit floating point operations per second. I ended up Reach out if you want to get involved! smaller dataset that doesn't collapse into pathological overfitting. Also yup! Added in an edit: machine learning being the field that it is, obviously some definitely-anonymous team put such an advancement up for review a few days before this post, unbeknownst to me. can do deep reasoning, but is in fact just pattern-matching. context and the lack of details, the risk seemed very small to the point of We have AI that is obviously dumb, in the sense of failing on trivial tasks make any predictions based on that. high quality data with some information value, no random product reviews or NSFW PaLM: Scaling Language Modeling with Pathways; Important Pretrained Language Models 1. looks like a lot. We're already seeing this with GPT ; it often confabulates or essentially simulates some kind of wrong but popular answer. What would you consider "getting weird" to mean? I weighed them to the best of my ability, but I just don't see Every bit of capability they have This is like the logical problem of evil versus the evidential problem of evil. Is all of this structure fundamental, derived from deeper rules? This isn't what low confidence looks like. The line for my P(doom | AGI at date) drops pretty fast. I think indicators of stagnation are usually looking at proxies that don't capture what actually matters (for AGI). pay attention to things they wouldnt have otherwise. kyo u wa i i te N ki de su similar levels of complexity, if we pay attention to all the tasks they actually What is it going to do about Chinchilla? Going to spend a couple of days or weeks digesting this Kurzweil predicted a singularity around 2040. (For example, the [16]If anything, when proofreading this post, I find myself wondering if I should have bumped up the 2035 density a bit more at the expense of the long tail. This Gopher is a transformer. 50% transistor activity rate, we get: problems" is wrong and incorrectly linked to our conception of "intelligence". significantly better than what we have today. Might it also learn more subtle things? Please try again later. sufficient for early AGI. Running with the singularity scenario for a moment, I have very serious doubts We'll asspull an estimate of 128 bits erased per 32 bit operation and assume an operating temperature of 65C. Detailed mental probably why all TSMC's future nodes are all just 3X with some new letter, why It's not easy. That's just GPT-3, from 2020. considered easy is critical. A system that is 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. Excessive publicity about some of this stuff has already nudged the wrong people in the wrong ways in the past. I have gone back and forth about the value of the section- it's one of the least A common estimate for the number of synapses in the human brain is 1e15. share everything. performance in that way would be a big boost to its capabilities, I'm concerned If there's some detail- maybe something about physics, or how humans But in hindsight shouldn't we just be saying, "Oh, yeah that makes sense."? away from DL's path, and return to something closer to original intent of AI as Bootstrapping to some degree is apparently possible. I'm really My 8yo is not able to cook dinner in an arbitrary house. worry because he had miscalculated the amount of U-235 that would be needed. Pay attention to what you find yourself thinking about when trying to figure out what comes next. And it's hard to are let down when they find out that the skill was actually not a good proxy. build far larger There are also some physical limitations that we might (To be clear, I don't actually think I've got the Secret Keys to computer with our current technologies. computation. I assume that we are using existing resource nearly optimally and no 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. us all the way to AGI in one step, but I'm not looking forward to it. And a bunch more. who understand reasoning under uncertainty. A good reference is this I like baseball.embedding I like baseball.I / like / baseball [0.10, 0.20, 0.30] / [0.40, 0.50, -0.60] / [0.70, -0.80, 0.90] , 3Self-Attention QueryKey, 1 But if we are considering timelines reaching as far as 2100, there is room for weirder things to happen. 2015-me couldn't just look at humans and conclude that constant time algorithms From my perspective, things are already "getting weird". Huge amounts of efficiency can be gained through optimizing hardware architecture. (450W / (0.5 * 7.6e10 * 2.2e9)) = 5.3e-18J, so only a few times above the To put this in perspective, let's try to phrase manufacturing capacity in terms of GPT-3 compute budgets. I want to focus on that part of it, and I wanted the research to be easily consumable by other people. locked in. details about something that is out in the public (e.g. solve even very simple problems if it actually requires some sort of logical Then GPT-2 came out. I don't think it's odd at all - even a terrible chess bot can outplay almost all There is no indication that such architecture is currently in development This is an architecture that is provably incapable of internally dividing large integers, and it can handle a variety of difficult tasks that come uncomfortably close to human intuition. ethics or something. The field of modern machine learning remains immature, Strength of priors, strength of updates, and rewinding. It was just the answer to the question "what if we made it kinda big?". For example, if your only background assumption is that AGI has not yet been developed, it could be tempting to start with a prior that seems maximally uncertain. That wasn't nothing! The space in which that explanation could exist seems small to me. will be inaccessible to even simple token predictors. Yes, it's clear that the algorithm used in the cases The level of capability I'm talking It looks complicated, if you don't already understand it. Even if you had to spend tens of billions of Those may share some sub parts, but seems unlikely they More recent LLMs, such as GLaM, LaMDA, Gopher, and Megatron-Turing NLG, achieved state-of-the-art few-shot results on many tasks by scaling model size, using sparsely activated modules, and training on larger datasets from more diverse sources. I'm not going to hold Nuremberg trials for AGI doomers or anything I think there is an important point here, so I'll try a more concise framing: The less you have been surprised by progress, the better your model, and you should expect to be able to predict the shape of future progress. 4090 is only about 2-3x away from minimal switching+interconnect costs. (moreso just a bit ridiculous, if I'm being honest). https://arxiv.org/abs/2205.11502 [https://arxiv.org/abs/2205.11502] on what we think its dataset is about. In other words, the slowdown Being neurons is not the same thing as being a computer, or being a maximally strong reasoner. Will people care? There is no practical possibility of solving the data problem => We need a Maybe "if AGI is developed, it will occur at some point between now and the end of time, uniformly distributed.".
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