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Failing to Understand the Exponential, Again

julian.ac

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Re: Failing to Understand the Exponential, Again

#201

Post by Someone »

jsnell wrote: post #43
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…

Indeed you can't be sure. But on the other hand a bunch of the commentariat has been claiming (with no evidence) that we're at the midpoint of the sigmoid for the last three years. They were wrong. And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. They were right. Now, the frontier labs rarely (never?) provide evidence either, but they do have about…

> Indeed you can't be sure. But on the other hand a bunch of the commentariat has been claiming (with no evidence) that we're at the midpoint of the sigmoid for the last three years.

I haven’t followed things closely, but I’ve seen more statements that we may be near the midpoint of a sigmoid than that we are at it.

> Thy were wrong. And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. They were right.

I know it’s an unfair question because we don’t have an objective way to measure speed of progress in this regard, but do you have evidence for models not only getting better, but getting better faster? (Remember: even at the midpoint of a sigmoid, there still is significant growth)

Re: Failing to Understand the Exponential, Again

#202

Post by littlestymaar »

It's funny because the author doesn't realize that this sentence at the beginning undermines his entire argument:

> Or they see two consecutive model releases and don’t notice much difference in their conversations, and they conclude that AI is plateauing and scaling is over.

The reason why we now fail to notice the difference between consecutive models now is because the progress isn't in fact exponential. Humans tend to have a logarithmic perception, which means we only appreciate progress when it is exponential (for instance you'd be very happy to get a $500 raise if you are living the minimum wage, but you wouldn't even call that “a raise” when on a SV engineers salary).

AI models have been improving a ton for the past three years, in many direction, but the rate of progress is definitely not exponential. It's not emergent either, as the focus is now being specifically directed at solving specific problems (both riddles and real world problems) thanks to trillions of token of high quality synthetic data.

On topics that aren't explicitly being worked on, progress have been minimal or even negative (for instance many people still use the 1 year old Mistral Nemo for creative writing because the more recent ones all have been STEMmaxxed)

Re: Failing to Understand the Exponential, Again

#203

Post by justlikereddit »

The exponential progress argument is frequently also misconstrued as a

>"we will get there by monotonously doing more of what we did previously"

Take the independent time being an SWE metric of the article. This is a rather new( metric for measuring AI capabilitie, it's also a good metric, it is directly measurable in a quantified way, unlike nebulous goal points such as "AGI/ASI".

It also doesn't necessarily predict any upheaval, which I also think is a good trait of a metric, we know it will be better when it hits 8, or 16 hours, but we can skip the hype and prophecies of civilizational transformation that are attached to terminology like "AGI/ASI".

Now the caveat is that a SWE-time metric is useful at the moment because it's an intra day timescale, but if we push this number to the point of comparing 48 hour vs 54 hour SWE-time models we can easily end up chasing abstractions that have little to no explanatory power as to how good this AI really is and what consists as a proper and good incremental improvement and what comes out as a numerical benchmark number that may or may not be artificial.

The same can be said of math-olympiad scores and many of the existing AI benchmarks.

In the past there existed a concept of narrow AI. We could take task A, make a narrow AI become good at it. But we would expect a different application to be needed for task B.

Now we have generalist AI, and we take the generalist AI and make it become good at task A because that is the flavor of the month metric, but maybe that doesn't translate for improving task B, which someone will come around to improving when that becomes flavor of the month.

The conclusion? There's probably no good singular metric to get stuck on and say

"this is it, this graph is the one, watch it go exponential and bring forth God"

We will instead skip, hop and jump between task-or-category specific metrics that are deemed significant at the moment and arms-race style pump them up until their relevance fades.

Re: Failing to Understand the Exponential, Again

#204

Post by meheleventyone »

sdenton4 wrote: post #125
rmunn wrote: post #112

Earlier quoted context omitted.

NOTE IN ADVANCE: I'm generalizing, naturally, because talking about specifics would require an essay and I'm trying to write a comment. Why predict that the growth rate is going to slow now? Simple. Because current models have already been trained on pretty much the entire meaningful part of the Internet. Where are they going to get more data? The exponential growth part of the curve was largely based on being able t…

Curiously, humans don't seem to require reading the entire internet in order to perform at human level on a wide variety of tasks... Nature suggests that there's a lot of headroom in algorithms for learning on existing sources. Indeed, we had models trained on the whole internet a couple years ago, now, yet model quality has continued to improve. Meanwhile, on the hardware side, transistor counts in GPUs are in the t…

This is a time horizon thing though. Over the course of future human history AI development might look exponential but that doesn’t mean there won’t be significant plateaus. We don’t even fully understand how the human brain works so whilst the fact it does exist strongly suggests it’s replicable (and humans do it naturally) that doesn’t make it practical in any time horizon that matters to us now. Nor does there seem to be fast movement in that direction since everyone is largely working on the same underlying architecture that isn’t similar to the brain.

Re: Failing to Understand the Exponential, Again

#205

Post by [deleted] »

hnlmorg wrote: post #137
Inufu wrote: post #111

Earlier quoted context omitted.

Author here. The argument is not that it will keep growing exponentially forever (obviously that is physically impossible), rather that: - given a sustained history of growth along a very predictable trajectory, the highest likelihood short term scenario is continued growth along the same trajectory. Sample a random point on an s-curve and look slightly to the right, what’s the most common direction the curve continu…

My point is that the limits of LLMs will be hit long before we they start to take on human capabilities. The problem isn’t that exponential growth is hard to visualise. The problem is that LLMs, as advanced and useful a technique as it is, isn’t suited for AGI and thus will never get us even remotely to the stage of AGI. The human like capabilities are really just smoke and mirrors. It’s like when people anthropomorp…

[deleted]

Re: Failing to Understand the Exponential, Again

#206

Post by spwa4 »

Yoric wrote: post #188
jsnell wrote: post #43

Earlier quoted context omitted.

Indeed you can't be sure. But on the other hand a bunch of the commentariat has been claiming (with no evidence) that we're at the midpoint of the sigmoid for the last three years. They were wrong. And then you had the AI frontier lab insiders who predicted an accelerating pace of progress for the last three years. They were right. Now, the frontier labs rarely (never?) provide evidence either, but they do have about…

There are a few other limitations, in particular how much energy, hardware and funding we (as a society) can afford to throw at the problem, as well as the societal impact. AI development is currently given a free pass on these points, but it's very unclear how long that will last. Regardless of scientific and technological potential, I believe that we'll hit some form of limit soon.

Luckily both middle eastern religious dictatorships and countries like China are throwing way too many resources at it ...

So we can rest assured the well-being of a country's people will not allowed to be a drag on AI progress.

Re: Failing to Understand the Exponential, Again

#207

Post by meheleventyone »

theptip wrote: post #135
rmunn wrote: post #130

Earlier quoted context omitted.

I am extremely confident that AGI, if it is achievable at all (which is a different argument and one I'm not getting into right now), requires a world model / fact model / whatever terminology you prefer, and is therefore not achievable by models that simply chain words together without having any kind of understanding baked into the model. In other words, LLMs cannot lead to AGI.

Agreed, it surely does require a world-model. I disagree that generic LLMs plus CoT/reasoning/tool calling (ie the current stack) cannot in principle implement a world model. I believe LLMs are doing some sort of world modeling and likely are mostly lacking a medium-/long-term memory system in which to store it. (I wouldn’t be surprised if one or two more architectural overhauls end up occurring before AGI, I also wo…

Isn’t the memory the pre-trained weights that let it do anything at all? Or do you mean they should be capable of refining them in real-time (learning).

Re: Failing to Understand the Exponential, Again

#208

Post by dist-epoch »

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…

> We would have 1 trillion transistor cpus following Moore's "exponential curve"

Cerebras wafer scale chip has 4 trillion transistors.

https://www.cerebras.ai/chip

Re: Failing to Understand the Exponential, Again

#209

Post by adornKey »

Earw0rm wrote: post #70
analog31 wrote: post #46

Earlier quoted context omitted.

It's possible to understand both exponential and limiting behavior at the same time. I work in an office full of scientists. Our team scrammed the workplace on March 10, 2020. To the scientists, it was intuitively obvious that the curve could not surpass 100% of the population. An exponential curve with no turning point is almost always seen as a sure sign that something is wrong with your model. But we didn't have a…

I'm curious as to whether the consensus is that the observed behaviour of COVID waves was ever fully and satisfactorily explained - the tend to grow exponentially but then seemingly saturate at a much lower point than a naïve look at the curve might suggest?

To those interested in numbers it was explained early - even on TV. Anyone interested saw that it was going like a seasonal flue wave. Numbers were following strict mathematics. My area was early - the numbers peaked right before people started to go crazy - the rest was censorship - There was a lot of fakery going on by using very soft numbers. Very often they used reporting date instead of infection date.. and some numbers were delayed 9 months... So most curves out there were seriously flawed. But if you were really interested you could see real epidemiological curves - but you had to do real work to find the numbers. Strict mathematics of a seasonal virus was something people didn't want to see - and this is still the consensus...
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