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The author is not shocked yet. (But maybe I will be!)
Strongly disagree. Employees of OpenAI and their alpha tester partners have obligations not to reveal secret information, whether by prediction market or other mechanism. Insider trading is not a sin against the market; it's a sin against the entity that entrusted you with private information. If someone tells me information under an NDA, I am obligated not to trade on that information.
Good question but no - ChatGPT still makes occasional mistakes even when you use the GPT API, in which you have full visibility/control over the context window.
Thanks for the write up. I was a participant in both Hypermind and XPT, but I recused myself from the MMLU question (among others) because I knew the GPT-4 result many months before the public. I'm not too surprised Hypermind was the least accurate - I think the traders there are less informed, plus the interface for shaping the distribution is a bit lacking (my recollection is that last year's version capped the width of distributions which massively constrained some predictions). I recall they also plotted the current values, a generally nice feature which has the side effect of anchoring ignorant forecasters downward, I'd bet.
Question: Are the Hypermind results for 2023 just from forecasts in 2022, or do they include forecasts from the prior year as well? I'm curious if part of the poor accuracy is from stale forecasts that were never updated.
Confirmed.
I'd take the same bet on even better terms, if you're willing. My $200k against your $5k.
$500 payment received.
I am committed to paying $100k if aliens/supernatural/non-prosaic explanations are, in the next 5 years, considered, in aggregate, to be 50%+ likely in explaining at least one UFO.
Fair. I accept. 200:1 of my $100k against your $500. How are you setting these up?
I'm happy to pay $100k if my understanding of the universe (no aliens, no supernatural, etc.) is shaken. Also happy to pay up after 5 years if evidence turns up later about activities before or in this 5-year period.
(Also, regarding history, I have a second Less Wrong account with 11 years of history: https://www.lesswrong.com/users/tedsanders)
I'll bet. Up to $100k of mine against $2k of yours. 50:1. (I honestly think the odds are more like 1000+:1, and would in principle be willing to go higher, but generally think people shouldn't bet more than they'd be willing to lose, as bets above that amount could drive bad behavior. I would be happy to lose $100k on discovering aliens/time travel/new laws of physics/supernatural/etc.)
Happy to write a contract of sorts. I'm a findable figure and I've made public bets before (e.g., $4k wagered on AGI-fueled growth by 2043).
As an OpenAI employee I cannot say too much about short-term expectations for GPT, but I generally agree with most of his subpoints; e.g., running many copies, speeding up with additional compute, having way better capabilities than today, have more modalities than today. All of that sounds reasonable. The leap for me is (a) believing that results in transformative AGI and (b) figuring out how to get these things to learn (efficiently) from experience. So in the end I find myself pretty unmoved by his article (which is high quality, to be sure).
Bingo
No worries. I've made far worse. I only wish that H100s could operate at a gentle 70 W! :)
I think what I don't understand is why you're defaulting to the assumption that the brain has a way to store and update information that's much more efficient than what we're able to do. That doesn't sound like a state of ignorance to me; it seems like you wouldn't hold this belief if you didn't think there was a good reason to do so.
It's my assumption because our brains are AGI for ~20 W.
In contrast, many kW of GPUs are not AGI.
Therefore, it seems like brains have a way of storing and updating information that's much more efficient than what we're able to do.
Of course, maybe I'm wrong and it's due to a lack of training or lack of data or lack of algorithms, rather than lack of hardware.
DNA storage is way more information dense than hard drives, for example.
One potential advantage of the brain is that it is 3D, whereas chips are mostly 2D. I wonder what advantage that confers. Presumably getting information around is much easier with 50% more dimensions.
70 W
Max power is 700 W, not 70 W. These chips are water-cooled beasts. Your estimate is off, not mine.
Let me try writing out some estimates. My math is different than yours.
An H100 SXM has:
- 8e10 transistors
- 2e9 Hz boost frequency of
- 2e15 FLOPS at FP16
- 7e2 W of max power consumption
Therefore:
- 2e6 eV are spent per FP16 operation
- This is 1e8 times higher than the Landauer limit of 2e-2 eV per bit erasure at 70 C (and the ratio of bit erasures per FP16 operation is unclear to me; let's pretend it's O(1))
- An H100 performs 1e6 FP16 operations per clock cycle, which implies 8e4 transistors per FP16 operation (some of which may be inactive, of course)
This seems pretty inefficient to me!
To recap, modern chips are roughly ~8 orders of magnitude worse than the Landauer limit (with a bit erasure per FP16 operation fudge factor that isn't going to exceed 10). And this is in a configuration that takes 8e4 transistors to support a single FP16 operation!
Positing that brains are ~6 orders of magnitude more energy efficient than today's transistor circuits doesn't seem at all crazy to me. ~6 orders of improvement on 2e6 is ~2 eV per operation, still two orders of magnitude above the 0.02 eV per bit erasure Landauer limit.
I'll note too that cells synthesize informative sequences from nucleic acids using less than 1 eV of free energy per bit. That clearly doesn't violate Landauer or any laws of physics, because we know it happens.
Why does switching barriers imply that electrical potential energy is probably being converted to heat? I don't see how that follows at all.
Where else is the energy going to go?
What is "the energy" that has to go somewhere? As you recognize, there's nothing that says it costs energy to change the shape of a potential well. I'm genuinely not sure what energy you're talking about here. Is it electrical potential energy spent polarizing a medium?
I think what I'm saying is standard in how people analyze power costs of switching in transistors, see e.g. this physics.se post.
Yeah, that's pretty standard. The ultimate efficiency limit for a semiconductor field-effect transistor is bounded by the 60 mV/dec subthreshold swing, and modern tiny transistors have to deal with all sorts of problems like leakage current which make it difficult to even reach that limit.
Unclear to me that semiconductor field-effect transistors have anything to do with neurons, but I don't know how neurons work, so my confusion is more likely a state of my mind than a state of the world.
+1. The derailment probabilities are somewhat independent of the technical barrier probabilities in that they are conditioned on the technical barriers otherwise being overcome (e.g., setting them all to 100%). That said, if you assign high probabilities to the technical barriers being overcome quickly, then the odds of derailment are probably lower, as there are fewer years for derailments to occur and derailments that cause delay by a few years may still be recovered from.
Thanks, that's clarifying. (And yes, I'm well aware that x -> B*x is almost never injective, which is why I said it wouldn't cause 8 bits of erasure rather than the stronger, incorrect claim of 0 bits of erasure.)
To store 1 bit of information you need a potential energy barrier that's at least as high as k_B T log(2), so you need to switch ~ 8 such barriers, which means in any kind of realistic device you'll lose ~ 8 k_B T log(2) of electrical potential energy to heat, either through resistance or through radiation. It doesn't have to be like this, and some idealized device could do better, but GPUs are not idealized devices and neither are brains.
Two more points of confusion:
- Why does switching barriers imply that electrical potential energy is probably being converted to heat? I don't see how that follows at all.
- To what extent do information storage requirements weigh on FLOPS requirements? It's not obvious to me that requirements on energy barriers for long-term storage in thermodynamic equilibrium necessarily bear on transient representations of information in the midst of computations, either because the system is out of thermodynamic equilibrium or because storage times are very short
Right. The idea is: "What are the odds that China invading Taiwan derails chip production conditional on a world where we were otherwise going to successfully scale chip production."
If we tried to simulate a GPU doing a simple matrix multiplication at high physical fidelity, we would have to take so many factors into account that the cost of our simulation would far exceed the cost of running the GPU itself. Similarly, if we tried to program a physically realistic simulation of the human brain, I have no doubt that the computational cost of doing so would be enormous.
The Beniaguev paper does not attempt to simulate neurons at high physical fidelity. It merely attempts to simulate their outputs, which is a far simpler task. I am in total agreement with you that the computation needed to simulate a system is entirely distinct from the computation being performed by that system. Simulating a human brain would require vastly more than 1e21 FLOPS.
Thanks for the constructive comments. I'm open-minded to being wrong here. I've already updated a bit and I'm happy to update more.
Regarding the Landauer limit, I'm confused by a few things:
- First, I'm confused by your linkage between floating point operations and information erasure. For example, if we have two 8-bit registers (A, B) and multiply to get (A, B*A), we've done an 8-bit floating point operation without 8 bits of erasure. It seems quite plausible to be that the brain does 1e20 FLOPS but with a much smaller rate of bit erasures.
- Second, I have no idea how to map the fidelity of brain operations to floating point precision, so I really don't know if we should be comparing 1 bit, 8 bit, 64 bit, or not at all. Any ideas?
Regarding training requiring 8e34 floating point operations:
- Ajeya Cotra estimates training could take anything from 1e24 to 1e54 floating point operations, or even more. Her narrower lifetime anchor ranges from 1e24 to 1e38ish. https://docs.google.com/document/d/1IJ6Sr-gPeXdSJugFulwIpvavc0atjHGM82QjIfUSBGQ/edit
- Do you think Cotra's estimates are not just poor, but crazy as well? If they were crazy, I would have expected to see her two-year update mention the mistake, or the top comments to point it out, but I see neither: https://www.lesswrong.com/posts/AfH2oPHCApdKicM4m/two-year-update-on-my-personal-ai-timelines
Interested in betting thousands of dollars on this prediction? I'm game.
Interesting! How do you think this dimension of intelligence should be calculated? Are there any good articles on the subject?
What conditional probabilities would you assign, if you think ours are too low?
Conditioning does not necessarily follow time ordering. E.g., you can condition the odds of X on being in a world on track to develop robots by 2043 without having robots well in advance of X. Similarly, we can condition on a world where transformative AGI is trainable with 1e30 floating point operations then ask the likelihood that 1e30 floating point operations can be constructed and harnessed for TAGI. Remember too that in a world with rapidly advancing AI and robots, much of the demand will be for things other than TAGI.
I'm sympathetic to your point that it's hard for brains to forecast these conditional probabilities. Certainly we may be wrong. But on the other hand, it's also hard for brains to forecast things that involve smushing lots of probabilities together under the hood. I generally think that factoring things out into components helps, but I can understand if you disagree.
I agree with your cruxes:
Ted Sanders, you stated that autonomous cars not being as good as humans was because they "take time to learn". This is completely false, this is because the current algorithms in use, especially the cohesive software and hardware systems and servers around the core driving algorithms, have bugs.
I guess it depends what you mean by bugs? Kind of a bummer for Waymo if 14 years and billions invested was only needed because they couldn't find bugs in their software stack.
If bugs are the reason self-driving is taking so long, then our essay is wildly off.
So present day, the cost is $100.19 an hour.
Yes, if with present day hardware we can effectively emulate a human brain for $100/hr, then our essay is wildly off.
Right, I'm not interested in minimum sufficiency. I'm just interested in the straightforward question of what data pipes would we even plug into the algorithm that would result in AGI. Sounds like you think a bunch of cameras and computers would work? To me, it feels like an empirical problem that will take years of research.
I'm not convinced about the difficulty of operationalizing Eliezer's doomer bet. Effectively, loaning money to a doomer who plans to spend it all by 2030 is, in essence, a claim on the doomer's post-2030 human capital. The doomer thinks it's worthless, whereas the skeptic thinks it has value. Hence, they transact.
The TAGI case seems trickier than the doomer case. Who knows what a one dollar bill will be worth in a post-TAGI world.
Sounds good. Can also leave money out of it and put you down for 100 pride points. :)
If so, message me your email and I'll send you a calendar invite for a group reflection in 2043, along with a midpoint check in in 2033.
Right, but what inputs and outputs would be sufficient to reward modeling of the real world? I think that might take some exploration and experimentation, and my 60% forecast is the odds of such inquiries succeeding by 2043.
Even with infinite compute, I think it's quite difficult to build something that generalizes well without overfitting.
Gotcha. I guess there's a blurry line between program search and training. Somehow training feels reasonable to me, but something like searching over all possible programs feels unreasonable to me. I suppose the output of such a program search is what I might mean by an algorithm for AGI.
Hyperparameter search and RL on a huge neural net feels wildly underspecified to me. Like, what would be its inputs and outputs, even?
Excellent comment - thanks for sticking your neck out to provide your own probabilities.
Given the gulf between our 0.4% and your 58.6%, would you be interested in making a bet (large or small) on TAI by 2043? If yes, happy to discuss how we might operationalize it.
I'm curious and I wonder if I'm missing something that's obvious to others: What are the algorithms we already have for AGI? What makes you confident they will work before seeing any demonstration of AGI?
If humans can teleoperate robots, why don't we have low-wage workers operating robots in high-wage countries? Feels like a win-win if the technology works, but I've seen zero evidence of it being close. Maybe Ugo is a point in favor?
Interesting. When I participated in the AI Adversarial Collaboration Project, a study funded by Open Philanthropy and executed by the Forecasting Research Institute, I got the sense that most folks concerned about AI x-risk mostly believed that AGIs would kill us on their own accord (rather than by accident or as a result of human direction), that AGIs would have self-preservation goals, and therefore AGIs would likely only kill us after solving robotic supply chains (or enslaving/manipulating humans, as I argued as an alternative).
Sounds like your perception is that LessWrong folks don't think robotic supply chain automation will be a likely prerequisite to AI x-risk?
Yeah, that's a totally fair criticism. Maybe a better header would be "evidence of accuracy." Though even that is a stretch given we're only listing events in the numerators. Maybe "evidence we're not crackpots"?
Edit: Probably best would be "Forecasting track record." This is what I would have gone with if rewriting the piece today.
Edit 2: Updated the post.
According to our rough and imperfect model, dropping inference needs by 2 OOMs increases our likelihood of hitting the $25/hr target by 20%abs, from 16% to 36%.
It doesn't necessarily make a huge difference to chip and power scaling, as in our model those are dominated by our training estimates, not our inference need estimates. (Though of course those figures will be connected in reality.)
With no adjustment to chip and power scaling, this yields a 0.9% likelihood of TAGI.
With a +15%abs bump to chip and power scaling, this yields a 1.2% likelihood of TAGI.
Great points.
I think you've identified a good crux between us: I think GPT-4 is far from automating remote workers and you think it's close. If GPT-5/6 automate most remote work, that will be point in favor of your view, and if takes until GPT-8/9/10+, that will be a point in favor of mine. And if GPT gradually provides increasingly powerful tools that wildly transform jobs before they are eventually automated away by GPT-7, then we can call it a tie. :)
I also agree that the magic of GPT should update one into believing in shorter AGI timelines with lower compute requirements. And you're right, this framework anchored on the human brain can't cleanly adjust from such updates. We didn't want to overcomplicate our model, but perhaps we oversimplified here. (One defense is that the hugeness of our error bars mean that relatively large updates are needed to make a substantial difference in the CDF.)
Lastly, I think when we see GPT unexpectedly pass the Bar, LSAT, SAT, etc. but continue to fail at basic reasoning, it should update us into thinking AGI is sooner (vs a no pass scenario), but also update us into realizing these metrics might be further from AGI than we originally assumed based on human analogues.
How has this forecast changed in the last 5 years? Has widespread and rapid advance of non-transformative somewhat-general-purpose LLMs change any of your component predictions?
We didn't have this framework 5 years ago, but the tremendous success of LLMs can only be a big positive update, I think. That said, some negative updates for me from the past 15 years have been how slowly Siri improved, how slowly Wolfram Alpha improved, and how slowly Alexa improved. I genuinely expected faster progress from their data flywheels after their launches, but somehow it didn't seem to happen. Self-driving seems to be middle of the road compared to how I thought it would go 5 years ago.
I don't actually disagree, but MUCH of the cause of this is an excessively high bar (as you point out, but it still makes the title misleading).
Agreed. I think the "<1%" headline feels like an aggressive claim, but the definition from the contest we use is a very high bar. For lower bars, we'd forecast much higher probabilities. We expect great things from AI and AGI, and we are not reflexively bearish on progress.
Excellent points. Agree that the compute needed to simulate a thing is not equal to the compute performed by that thing. It's very possible this means we're overestimating the compute performed by the human brain a bit. Possible this is counterbalanced by early AGIs being inefficient, or having architectural constraints that the human brain lacks, but who knows. Very possible our 16% is too low, and should be higher. Tripling it to ~50% would yield a likelihood of transformative AGI of ~1.2%.
We invent a way for AGIs to learn faster than humans : Why is this even in the table? This would be 1.0 because it's a known fact, AGI learns faster than humans. Again, from the llama training run, the model went from knowing nothing to domain human level in 1 month. That's faster. (requiring far more data than humans isn't an issue)
100% feels overconfident. Some algorithms learning some things faster than humans is not proof that AGI will learn all things faster than humans. Just look at self-driving. It's taking AI far longer than human teenagers to learn.
AGI inference costs drop below $25/hr (per human equivalent): Well, A100s are 0.87 per hour. A transformative AGI might use 32 A100s. $27.84 an hour. Looks like we're at 1.0 on this one also.
100% feels overconfident. We don't know if transformative will need 32 A100s, or more. Our essay explains why we think it's more. Even if you disagree with us, I struggle to see how you can be 100% sure.
Oh, to clarify, we're not predicting AGI will be achieved by brain simulation. We're using the human brain as a starting point for guessing how much compute AGI will need, and then applying a giant confidence interval (to account for cases where AGI is way more efficient, as well as way less efficient). It's the most uncertain part of our analysis and we're open to updating.
For posterity, by 2030, I predict we will not have:
- AI drivers that work in any country
- AI swim instructors
- AI that can do all of my current job at OpenAI in 2023
- AI that can get into a 2017 Toyota Prius and drive it
- AI that cleans my home (e.g., laundry, dishwashing, vacuuming, and/or wiping)
- AI retail workers
- AI managers
- AI CEOs running their own companies
- Self-replicating AIs running around the internet acquiring resources
Here are some of my predictions from the past:
- Predictions about the year 2050, written 7ish years ago: https://www.tedsanders.com/predictions-about-the-year-2050/
- Predictions on self-driving from 5 years ago: https://www.tedsanders.com/on-self-driving-cars/