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Failing to Understand the Exponential, Again

julian.ac

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Re: Failing to Understand the Exponential, Again

#141

Post by timmytokyo »

FrustratedMonky wrote: post #63
HexDecOctBin wrote: post #6

Exponential curves don't last for long fortunately, or the universe would have turned into a quark soup. The example of COVID is especially ironic, considering it stopped being a real concern within 3 years of its advent despite the exponential growth in the early years. Those who understand exponentials should also try to understand stock and flow.

Exponentials exist in their environment. Didn't Covid stop because we ran out of people to infect. Of course it can't keep going exponential, because there aren't exponential people to infect. What is this limit on AI? It is technology, energy, something. All these things can be over-come, to keep the exponential going. And of course, systems also break at the exponential. Maybe AI is stopped by the world economy col…

>What is this limit on AI?

Data. Think of our LLMs like bacteria in a Petri dish. When first introduced, they achieve exponential growth by rapidly consuming the dish's growth medium. Once the medium is consumed, growth slows and then stops.

The corpus of information on the Internet, produced over several decades, is the LLM's growth medium. And we're not producing new growth medium at an exponential rate.

Re: Failing to Understand the Exponential, Again

#142

Post by danlitt »

I am constantly astonished that articles like this even pass the smell test. It is not rational to predict exponential growth just because you've seen exponential growth before! Incidentally, that is not what people did during COVID, they predicted exponential growth for reasons. Specific, articulable reasons, that consisted of more than just "look, like go up. line go up more?".

Incidentally, the benchmarks quoted are extremely dubious. They do not even really make sense. "The length of tasks AI can do is doubling every 7 months". Seriously, what does that mean? If the AI suddenly took double the time to answer the same question, that would not be progress. Indeed, that isn't what they did, they just... picked some times at random? You might counter that these are actually human completion times, but then why are we comparing such distinct and unrelated tasks as "count words in a passage" (trivial, any child can do) and "train adversarially robust image model" (expert-level task, could take anywhere between an hour and never-complete).

Honestly, the most hilarious line in the article is probably this one:

> You might object that this plot looks like it might be levelling off, but this is probably mostly an artefact of GPT-5 being very consumer-focused.

This is a plot with three points in it! You might as well be looking at tea leaves!

Re: Failing to Understand the Exponential, Again

#143

Post by Inufu »

pickledish wrote: post #107
crazygringo wrote: post #7

> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped. I'm sure people were saying that about commercial airline speeds in the 1970's too. But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. With LLM's at the moment, the limiting fac…

Agreed! And, maybe I'm missing something, but to me it seems obvious that flat top part of the S curve is going to be somewhere below human ability... because, as you say, of the training data. How on earth could we train an LLM to be smarter than us, when 100% of the material we use to teach it how to think, is human-style thinking? Maybe if we do a good job, only a little bit below human ability -- and what an acco…

Author here.

The standard way to do this is Reinforcement Learning: we do not teach the model how to do the task, we let it discover the _how_ for itself and only grade it based on how well it did, then reinforce the attempts where it did well. This way the model can learn wildly superhuman performance, e.g. it's what we used to train AlphaGo and AlphaZero.

Re: Failing to Understand the Exponential, Again

#144

Post by joshdavham »

> - Models will be able to autonomously work for full days (8 working hours) by mid-2026. > - At least one model will match the performance of human experts across many industries before the end of 2026. > - By the end of 2027, models will frequently outperform experts on many tasks.

I’ve seen a lot of people make predictions like this and it will be interesting to see how this turns out. But my question is, what should happen to a person’s credibility if their prediction turns out to be wrong? Should the person lose credibility for future predictions and we no longer take them seriously? Or is that way too harsh? Should there be reputational consequences for making bad predictions? I guess this more of a general question, not strictly AI-related.

Re: Failing to Understand the Exponential, Again

#145

Post by hnlmorg »

Inufu wrote: post #140
bawolff wrote: post #134

Earlier quoted context omitted.

That feels like you're moving the goal posts a bit. Exponential growth over the short term is very uninteresting. Exponential growth is exciting when it can compound. E.g. if i offered you an investing opportunity 500% / per year compounded daily - that's amazing. If the fine print is that that rate will only last for the very near term (say a week), then it would be worse than a savings account.

Well, growth has been on this exponential already for 5+ years (for the METR eval), and we are at the point where models are very close to matching human expert capabilities in many domains - only one or two more years of growth would put us well beyond that point. Personally I think we'll see way more growth than that, but to see profound impacts on our economy you only need to believe the much more conservative ass…

Which domains?

What I’ve seen is that LLMs are very good at simulating an extremely well read junior.

Models know all the tricks but not when to use them.

And because of that, you’re continually have to hand hold them.

Working with an LLM is really closer to pair programming than it is handing a piece of work to an expert.

The stuff I’ve seen in computer vision is far more impressive in terms of putting people out of a job. But even there, it’s still highly specific models left to churn away at tasks that are ostensibly just long and laborious tasks. Which so much of VFX is.

Re: Failing to Understand the Exponential, Again

#146

Post by danlitt »

theptip wrote: post #98
crazygringo wrote: post #7

> Given consistent trends of exponential performance improvements over many years and across many industries, it would be extremely surprising if these improvements suddenly stopped. I'm sure people were saying that about commercial airline speeds in the 1970's too. But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. With LLM's at the moment, the limiting fac…

> a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. Yes of course it’s not going to increase exponentially forever. The point is, why predict that the growth rate is going to slow exactly now? What evidence are you going to look at? It’s possible to make informed predictions (eg “Moore’s law can’t get you further than 1nm with silicon due to fundamental physica…

> why predict that the growth rate is going to slow exactly now?

why predict that it will continue? Nobody ever actually makes an argument that growth is likely to continue, they just extrapolate from existing trends and make a guess, with no consideration of the underlying mechanics.

Oh, go on then, I'll give a reason: this bubble is inflated primarily by venture capital, and is not profitable. The venture capital is starting to run out, and there is no convincing evidence that the businesses will become profitable.

Re: Failing to Understand the Exponential, Again

#147

Post by alyxya »

The sentiment of the comments here seems rather pessimistic. A perspective that balances both sides might be that the rate of mass adoption of some technology often lags behind the frontier capabilities, so I wouldn’t expect AI to take over a majority of those jobs in GPDval in a couple of years, but it’ll probably happen eventually.

There are still fundamental limitations in both the model and products using the model that restrict what AI is capable of, so it’s simultaneously true that AI can do cutting edge work in certain domains for hours while vastly underperforming in other domains for very small tasks. The trajectory of improvement of AI capabilities is also an unknown, where it’s easy to overestimate exponential trends due to unexpected issues arising but also easy to underestimate future innovations.

I don’t see the trajectory slowing down just yet with more compute and larger models being used, and I can imagine AI agents will increasingly give their data to further improve larger models.

Re: Failing to Understand the Exponential, Again

#148

Post by jsnell »

danlitt wrote: post #142

I am constantly astonished that articles like this even pass the smell test. It is not rational to predict exponential growth just because you've seen exponential growth before! Incidentally, that is not what people did during COVID, they predicted exponential growth for reasons . Specific, articulable reasons, that consisted of more than just "look, like go up. line go up more?". Incidentally, the benchmarks quoted…

> but then why are we comparing such distinct and unrelated tasks as ...

Because a few years ago the LLMs could only do trivial tasks that a child could do, and now they're able to do complex research and software development tasks.

If you just have the trivial tasks, the benchmark is saturated within a year. If you just have the very complex tasks, the benchmark is has no sensitivity at all for years (just everything scoring a 0) and then abruptly becomes useful for a brief moment.

This seems pretty obvious, and I can't figure out what your actual concern is. You're just implying it is a flawed design without pointing out anything concrete.

Re: Failing to Understand the Exponential, Again

#149

Post by tim333 »

marginalia_nu wrote: post #64
breuleux wrote: post #53

Earlier quoted context omitted.

> But a lot of technologies turn out to be S-shaped, not purely exponential, because there are limiting factors. I'd argue all of them. Any true exponential eventually gets to a point where no computer can even store its numerical value. It's a physically absurd curve.

The narrative quietly assumes that this exponential curve can in fact continue since it will be the harbinger of the technological singularity. Seems more than a bit eschatological, but who knows. If we suppose this tech rapture does happen, all bets are off; in that sense it's probably better to assume the curve is sigmoidal, since the alternative is literally beyond human comprehension.

I think the technological singularity has generally been a bit of a metaphor rather than a mathematical singularity.

Re: Failing to Understand the Exponential, Again

#150

Post by coldtea »

senordevnyc wrote: post #117
coldtea wrote: post #39

> People notice that while AI can now write programs, design websites, etc, it still often makes mistakes or goes in a wrong direction, and then they somehow jump to the conclusion that AI will never be able to do these tasks at human levels, or will only have a minor impact. When just a few years ago, having AI do these things was complete science fiction! Both things can be true, since they're orthogonal. Having AI…

Except it’s not been five years, it’s been at most three, since approximately no one was using LLMs prior to ChatGPT’s release, which was just under three years ago. We did have Copilot a year before that, but it was quite rudimentary. And really, we’ve had even less than that. The first large scale reasoning model was o1, which was released 12 months ago. More useful coding agents are even newer than that. This narr…

>Except it’s not been five years, it’s been at most three,

Why would it be "at most" 3? We had Chat GPT commercially available as private beta API on 2020. It's only the mass public that got 3.5 3 years ago.

But those who'd do the noticing as per my argument is not just Joe Public (which could be oblivious), but people already starting in 2020, and includes people working in the space, who worked with LLM and LLM-like architectures 2-3 years before 2020.

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