AI 2027

It’s good science fiction, I’ll give it that. I think getting lost in the weeds over technicalities ignores the crux of the narrative: even if this doesn’t lead to AGI, at the very least it’s likely the final “warning shot” we’ll get before it’s suddenly and irreversibly here.

The problems it raises - alignment, geopolitics, lack of societal safeguards - are all real, and happening now (just replace “AGI” with “corporations”, and voila, you have a story about the climate crisis and regulatory capture). We should be solving these problems before AGI or job-replacing AI becomes commonplace, lest we run the very real risk of societal collapse or species extinction.

The point of these stories is to incite alarm, because they’re trying to provoke proactive responses while time is on our side, instead of trusting self-interested individuals in times of great crisis.

> even if this doesn’t lead to AGI, at the very least it’s likely the final “warning shot” we’ll get before it’s suddenly and irreversibly here.

I agree that it's good science fiction, but this is still taking it too seriously. All of these "projections" are generalizing from fictional evidence - to borrow a term that's popular in communities that push these ideas.

Long before we had deep learning there were people like Nick Bostrom who were pushing this intelligence explosion narrative. The arguments back then went something like this: "Machines will be able to simulate brains at higher and higher fidelity. Someday we will have a machine simulate a cat, then the village idiot, but then the difference between the village idiot and Einstein is much less than the difference between a cat and the village idiot. Therefore accelerating growth[...]" The fictional part here is the whole brain simulation part, or, for that matter, any sort of biological analogue. This isn't how LLMs work.

We never got a machine as smart as a cat. We got multi-paragraph autocomplete as "smart" as the average person on the internet. Now, after some more years of work, we have multi-paragraph autocomplete that's as "smart" as a smart person on the internet. This is an imperfect analogy, but the point is that there is no indication that this process is self-improving. In fact, it's the opposite. All the scaling laws we have show that progress slows down as you add more resources. There is no evidence or argument for exponential growth. Whenever a new technology is first put into production (and receives massive investments) there is an initial period of rapid gains. That's not surprising. There are always low-hanging fruit.

We got some new, genuinely useful tools over the last few years, but this narrative that AGI is just around the corner needs to die. It is science fiction and leads people to make bad decisions based on fictional evidence. I'm personally frustrated whenever this comes up, because there are exciting applications which will end up underfunded after the current AI bubble bursts...

> Someday we will have a machine simulate a cat, then the village idiot... This isn't how LLMs work.

I think you misunderstood that argument. The simulate the brain thing isn't a "start from the beginning" argument, it's an "answer a common objection" argument.

Back around 2000, when Nick Bostrom was talking about this sort of thing, computers were simply nowhere near powerful enough to come even close to being smart enough to outsmart a human, except in very constrained cases like chess; we did't even have the first clue how to create a computer program to be even remotely dangerous to us.

Bostrom's point was that, "We don't need to know the computer program; even if we just simulate something we know works -- a biological brain -- we can reach superintelligence in a few decades." The idea was never that people would actually simulate a cat. The idea is, if we don't think of anything more efficient, we'll at least be able to simulate a cat, and then an idiot, and then Einstein, and then something smarter. And since we almost certainly will think of something more efficient than "simulate a human brain", we should expect superintelligence to come much sooner.

> There is no evidence or argument for exponential growth.

Moore's law is exponential, which is where the "simulate a brain" predictions have come from.

> It is science fiction and leads people to make bad decisions based on fictional evidence.

The only "fictional evidence" you've actually specified so far is the fact that there's no biological analog; and that (it seems to me) is from a misunderstanding of a point someone else was making 20 years ago, not something these particular authors are making.

I think the case for AI caution looks like this:

A. It is possible to create a superintelligent AI

B. Progress towards a superintelligent AI will be exponential

C. It is possible that a superintelligent AI will want to do something we wouldn't want it to do; e.g., destroy the whole human race

D. Such an AI would be likely to succeed.

Your skepticism seems to rest on the fundamental belief that either A or B is false: that superintelligence is not physically possible, or at least that progress towards it will be logarithmic rather than exponential.

Well, maybe that's true and maybe it's not; but how do you know? What justifies your belief that A and/or B are false so strongly, that you're willing to risk it? And not only willing to risk it, but try to stop people who are trying to think about what we'd do if they are true?

What evidence would cause you to re-evaluate that belief, and consider exponential progress towards superintelligence possible?

And, even if you think A or B are unlikely, doesn't it make sense to just consider the possibility that they're true, and think about how we'd know and what we could do in response, to prevent C or D?

> The idea is, if we don't think of anything more efficient, we'll at least be able to simulate a cat, and then an idiot, and then Einstein, and then something smarter. And since we almost certainly will think of something more efficient than "simulate a human brain", we should expect superintelligence to come much sooner.

The problem with this argument is that it's assuming that we're on a linear track to more and more intelligent machines. What we have with LLMs isn't this kind of general intelligence.

We have multi-paragraph autocomplete that's matching existing texts more and more closely. The resulting models are great priors for any kind of language processing and have simple reasoning capabilities in so far as those are present in the source texts. Using RLHF to make the resulting models useful for specific tasks is a real achievement, but doesn't change how the training works or what the original training objective was.

So let's say we continue along this trajectory and we finally have a model that can faithfully reproduce and identify every word sequence in its training data and its training data includes every word ever written up to that point. Where do we go from here?

Do you want to argue that it's possible that there is a clever way to create AGI that has nothing to do with the way current models work and that we should be wary of this possibility? That's a much weaker argument than the one in the article. The article extrapolates from current capabilities - while ignoring where those capabilities come from.

> And, even if you think A or B are unlikely, doesn't it make sense to just consider the possibility that they're true, and think about how we'd know and what we could do in response, to prevent C or D?

This is essentially https://plato.stanford.edu/entries/pascal-wager/

It might make sense to consider, but it doesn't make sense to invest non-trivial resources.

This isn't the part that bothers me at all. I know people who got grants from, e.g., Miri to work on research in logic. If anything, this is a great way to fund some academic research that isn't getting much attention otherwise.

The real issue is that people are raising ridiculous amounts of money by claiming that the current advances in AI will lead to some science fiction future. When this future does not materialize it will negatively affect funding for all work in the field.

And that's a problem, because there is great work going on right now and not all of it is going to be immediately useful.