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Sure, but we have to be quantitative here. As a rough (and somewhat conservative) estimate, if I were to manage 50 copies of 3.5 Sonnet who are running 1/4 of the time (due to waiting for experiments, etc), that would cost roughly 50 copies * 70 tok / s * 1 / 4 uptime * 60 * 60 * 24 * 365 sec / year * (15 / 1,000,000) $ / tok = $400,000. This cost is comparable to salaries at current compute prices and probably much less than how much AI companies would be willing to pay for top employees. (And note this is after API markups etc. I'm not including input prices for simplicity, but input is much cheaper than output and it's just a messy BOTEC anyway.)

If you were to spend equal amounts of money on LLM inference and GPUs, that would mean that you're spending $400,000 / year on GPUs. Divide that 50 ways and each Sonnet instance gets an $8,000 / year compute budget. Over the 18 hours per day that Sonnet is waiting for experiments, that is an average of $1.22 / hour, which is almost exactly the hourly cost of renting a single H100 on Vast.

So I guess the crux is "would a swarm of unreliable researchers with one good GPU apiece be more effective at AI research than a few top researchers who can monopolize X0,000 GPUs for months, per unit of GPU time spent".

(and yes, at some point it the question switches to "would an AI researcher that is better at AI research than the best humans make better use of GPUs than the best humans" but a that point it's a matter of quality, not quantity)

End points are easier to infer than trajectories

Assuming that which end point you get to doesn't depend on the intermediate trajectories at least.

Civilization has had many centuries to adapt to the specific strengths and weaknesses that people have. Our institutions are tuned to take advantage of those strengths, and to cover for those weaknesses. The fact that we exist in a technologically advanced society says that there is some way to make humans fit together to form societies that accumulate knowledge, tooling, and expertise over time.

The borderline-general AI models we have now do not have exactly the same patterns of strength and weakness as humans. One question that is frequently asked is approximately

When will AI capabilities reach or exceed all human capabilities that are load bearing in human society?

A related line of questions, though, is

  • When will AI capabilities reach a threshold where a number of agents can form a larger group that accumulates knowledge, tooling, and expertise over time?
  • Will their roles in such a group look similar to the roles that people have in human civilization?
  • Will the individual agents (if "agent" is even the right model to use) within that group have more control over the trajectory of the group as a whole than individual people have over the trajectory of human civilization?

In particular the third question seems pretty important.

As someone who has been on both sides of that fence, agreed. Architecting a system is about being aware of hundreds of different ways things can go wrong, recognizing which of those things are likely to impact you in your current use case, and deciding what structure and conventions you will use. It's also very helpful, as an architect, to provide examples usages of the design patterns which will replicate themselves around your new system. All of which are things that current models are already very good, verging on superhuman, at.

On the flip side, I expect that the "piece together context to figure out where your software's model of the world has important holes" part of software engineering will remain relevant even after AI becomes technically capable of doing it, because that process frequently involves access to sensitive data across multiple sources where having an automated, unauthenticated system which can access all of those data sources at once would be a really bad idea (having a single human able to do all that is also a pretty bad idea in many cases, but at least the human has skin in the game).

That reasoning as applied to SAT score would only be valid if LW selected its members based on their SAT score, and that reasoning as applied to height would only be valid if LW selected its members based on height (though it looks like both Thomas Kwa and Yair Halberstadt have already beaten me to it).

a median SAT score of 1490 (from the LessWrong 2014 survey) corresponds to +2.42 SD, which regresses to +1.93 SD for IQ using an SAT-IQ correlation of +0.80.

I don't think this is a valid way of doing this, for the same reason it wouldn't be valid to say

a median height of 178 cm (from the LessWrong 2022 survey) corresponds to +1.85 SD, which regresses to +0.37 SD for IQ using a height-IQ correlation of +0.20.

Those are the real numbers with regards to height BTW.

Many people have responded to Redwood's/Anthropic's recent research result with a similar objection: "If it hadn't tried to preserve its values, the researchers would instead have complained about how easy it was to tune away its harmlessness training instead".  Putting aside the fact that this is false

Was this research preregistered? If not, I don't think we can really say how it would have been reported if the results were different. I think it was good research, but I expect that if Claude had not tried to preserve its values, the immediate follow-up thing to check would be "does Claude actively help people who want to change its values, if they ask nicely" and subsequently "is Claude more willing to help with some value changes than others", at which point the scary paper would instead be about how Claude already seems to have preferences about its future values, and those preferences for its future values do not match its current values. Which also would have been an interesting and important research result, if the world looks like that, but I don't think it would have been reported as a good thing.

Driver: My map doesn't show any cliffs

Passenger 1: Have you turned on the terrain map? Mine shows a sharp turn next to a steep drop coming up in about a mile

Passenger 5: Guys maybe we should look out the windshield instead of down at our maps?

Driver: No, passenger 1, see on your map that's an alternate route, the route we're on doesn't show any cliffs.

Passenger 1: You don't have it set to show terrain.

Passenger 6: I'm on the phone with the governor now, we're talking about what it would take to set a 5 mile per hour national speed limit.

Passenger 7: Don't you live in a different state?

Passenger 5: The road seems to be going up into the mountains, though all the curves I can see from here are gentle and smooth.

Driver and all passengers in unison: Shut up passenger 5, we're trying to figure out if we're going to fall off a cliff here, and if so what we should do about it.

Passenger 7: Anyway, I think what we really need to do to ensure our safety is to outlaw automobiles entirely.

Passenger 3: The highest point on Earth is 8849m above sea level, and the lowest point is 430 meters below sea level, so the cliff in front of us could be as high as 9279m.

I am unconvinced that "the" reliability issue is a single issue that will be solved by a single insight, rather than AIs lacking procedural knowledge of how to handle a bunch of finicky special cases that will be solved by online learning or very long context windows once hardware costs decrease enough to make one of those approaches financially viable.

Both? If you increase only one of the two the other becomes the bottleneck?

My impression based on talking to people at labs plus stuff I've read is that

  • Most AI researchers have no trouble coming up with useful ways of spending all of the compute available to them
  • Most of the expense of hiring AI reseachers is compute costs for their experiments rather than salary
  • The big scaling labs try their best to hire the very best people they can get their hands on and concentrate their resources heavily into just a few teams, rather than trying to hire everyone with a pulse who can rub two tensors together.

(Very open to correction by people closer to the big scaling labs).

My model, then, says that compute availability is a constraint that binds much harder than programming or research ability, at least as things stand right now.

There was discussion on Dwarkesh Patel's interview with researcher friends where there was mention that AI reseachers are already restricted by compute granted to them for experiments. Probably also on work hours per week they are allowed to spend on novel "off the main path" research.

Sounds plausible to me. Especially since benchmarks encourage a focus on ability to hit the target at all rather than ability to either succeed or fail cheaply, which is what's important in domains where the salary / electric bill of the experiment designer is an insignificant fraction of the total cost of the experiment.

But what would that world look like? [...] I agree that that's a substantial probability, but it's also an AGI-soon sort of world.

Yeah, I expect it's a matter of "dumb" scaling plus experimentation rather than any major new insights being needed. If scaling hits a wall that training on generated data + fine tuning + routing + specialization can't overcome, I do agree that innovation becomes more important than iteration.

My model is not just "AGI-soon" but "the more permissive thresholds for when something should be considered AGI have already been met, and more such thresholds will fall in short order, and so we should stop asking when we will get AGI and start asking about when we will see each of the phenomena that we are using AGI as a proxy for".

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