Large Language Model Performance Doubles Every 7 Months
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spectrum.ieee.org/large-language-model-performance
By 2030, AI will greatly outperform humans in some complex intellectual tasks. Discover how LLMs are doubling their capabilities every seven months.
This is like measuring the increasing speeds of cars in the early years and extrapolating that they would be supersonic by now by ignoring the exponential impact that air resistance has.
Very good analogy. They're also ignoring that getting faster and faster at reaching a 50% success rate doesn't imply ever achieving higher success rates, let alone consistently.
My son has doubled in size every month for the last few months. At this rate he'll be fifty foot tall by the time he's seven years old.
Yeah, it's a stupid claim to make on the face of it. It also ignores practical realities. The first is those is training data, and the second is context windows. The idea that AI will successfully write a novel or code a large scale piece of software like a video game would require them to be able to hold that entire thing in their context window at once. Context windows are strongly tied to hardware usage, so scaling them to the point where they're big enough for an entire novel may not ever be feasible (at least from a cost/benefit perspective).
I think there's also the issue of how you define "success" for the purpose of a study like this. The article claims that AI may one day write a novel, but how do you define "successfully" writing a novel? Is the goal here that one day we'll have a machine that can produce algorithmically mediocre works of art? What's the value in that?
Air resistance has cubic not exponential impact
Or like looking at the early days of semiconductors and extrapolating that CPU speed will double every 18 months ..smh these people
Since CPU speeds are still doubling every 18 months you have a solid point!
Or maybe not since you are probably referring to the doubling of transistors that was an observation which was accurate over a lengthy period of time in the context of when the observation was made. Nobody said that would continue indefinitely either.
Yup, that's what I was alluding to, while it may not still be the case for transistors, they did manage to take 50 odd years to get there, push that trend line from the figure 50 years heh (not saying you should, 5 seems much more conservative)
Take a look at Nvidias pace wrt Moore's law (of FLOPS) https://netrouting.com/nvidia-surpassing-moores-law-gpu-innovation/
Classic pseudo-science for the modern grifter. Vague definitions, sloppy measurements, extremely biased, wild unsupported predictions, etc.
That graph is hilarious. Enormous error bars, totally arbitrary quantization of complexity, and it's title? "Task time for a human that an AI model completes with a 50 percent success rate". 50 percent success is useless, lmao.
On a more sober note, I'm very disappointed that IEEE is publishing this kind of trash.
in yes/no type questions, 50% success rate is the absolute worst one can do. Any worse and you're just giving an inverted correct answer more than half the time
and assuming that improvement doesn't plateau, ever,
*with 50 percent reliability.
Heck of an asterisk on this claim.
That sounds like a coin flip, but 50% reliability can be really useful.
If a model has 50% chance of completing a task that would cost me an hour - and I can easily check it was completed correctly - on average, I'm saving half of the time it would take to complete this.
That said, exponentials don't exist in the real world, we're just seeing the middle of a sigmoid curve, which will soon yield diminishing returns.
Can you always though?
Yes, but the tricky thing is we have no idea when the seemingly exponential growth will flip over into the plateuing phase. We could be there already or it could be another 30 years.
For comparison Moores law is almost certainly a sigmoid too, but weve been seeing exponential growth for 50 years now.
Moore's law hasn't been exponential for ~15 years now.
If you are just talking transitor density I believe it still is, but even if not, my point was that it had exponential growth spanning over many decades.
All that power used for a fucking coin flip.
This is such bullshit. Models have already consumed all available data and have nothing left to consume, whole needing exponentially more data for progressive advancements
This. It's the old "to the moon" mentality.
If my 2yo continues to grow at the current rate, we'll have to buy new doors soon becouse at age 10 the kid will be the tallest person on Earth.
Apparently, throwing more data at it will not help much from now on... But anyway what they're saying, I can't trust the snake oil seller, he is suspicious...
time for them to set sail to the wild seas again!
How is completely fucking up literally 50% of the time outperforming exactly???
You see, in 7 months, they'll fuck up literally 100% of the time! Progress.
It would be even better but unfortunately you can't exceed 100% wrong.
Wait, maybe you can, let me check the AI!
It's outperforming "messier" problems with a much lower success rate.
Someone doesn't know the folly of extending straight lines graphs into the future.
https://xkcd.com/605
Reminded me of this
And this
https://xkcd.com/1007
Oof, the alt text on that second one was unexpectedly dark lmao
Is it just me, or is this graph (first graph in the article) completely unintelligible?
The X-axis being time is self-explanatory, but the Y-axis is somehow exponential time but then also mapping random milestones of performance, meaning those milestones are hard-linked to that time-based Y-axis? What?
That's what you get when the "research" for the article is AI generated.
when will they be able to tell me how many 'r's are in 'strawberry' in under 1s?
Deepseek-r1:1.5b
Thinking
I like how it counted correctly and then gave an incorrect final answer.. Bravo 👏
I very much like those huge generalizations in AI articles that makes you small and stupid. Those generalizations proves nothing but they sound like something big is coming. It's parody. How long we see them before people wake up ? Just wait 2 more years and AI will be better bro. You're not using AI properly, you need to learn how to use AI bro. You need to use different model for this task bro. Just pay for corporate products bro. Amount of junk of top of this pile of shit is amusing.
Because so much money has been thrown at it, for startups, for power generation, for investors, that this is little more than marketing for retail investors to buy into.
That's no doubt that they pour money to machines instead of people. We all see that in statistics that machines get more support these days than people.
2 X 0 = 0
I doubt it
Then why share it?
Do you not see any value in engaging with views you don't personally agree with? I don't think agreeing with it is a good barometer for whether it's post-worthy
Good point, thank you, I figured that sharing poor scientific articles essentially equals spreading misinformation (which I think is a fair point either), but I like your perspective either
I guess the value is that at some point you'll probably hear the core claim - "AI is improving exponentially" - regurgitated by someone making a bad argument, and knowing the original source and context can be very helpful to countering that disinformation.
So we can mock it!
Is the performance increase related to computing power? I suspect the undelying massive datacenters running the cloud based LLMs are expanding at a similar rate...
Yawn...
So only 10 years until it isn't a ressource hog anymore...
Only if people give up on the whole concept by then. Each new generation of AI model takes more energy than the last.
Then why do I feel like it's programming abilites are getting worse? I've stopped paying for it now because it causes more frustration than anything else. Works for simple "how can I simplyfi this code" queries when my head hurts, but that's about it.
"performance"
new moore law dropped
they are improving at an exponential rate. It's just that the exponent is less than one.
They need to invent an inquiring-gpt or Q-GPT. Otherwise they'll need humans to do the digging.
I saw something once that explained how you can have an ai trained on a set of soccer games and have it generate soccer games as a use for it.
The idea is that the model has compressed all the soccer games into a smaller data size form than the total of having let's say 100+ games on video or whatever.
That's the real utility I see in generative ai that I know can keep going basically as long as we want to.