By 2030, AI will greatly outperform humans in some complex intellectual tasks. Discover how LLMs are doubling their capabilities every seven months.
They need to invent an inquiring-gpt or Q-GPT. Otherwise they’ll need humans to do the digging.
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.
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.
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.
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.
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!
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…
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.
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?
Very good analogy. They’re also ignoring that getting faster and faster at reaching a 50% success rate (a totally unacceptable success rate for meaningful tasks) doesn’t imply ever achieving consistently acceptable success.
Air resistance has cubic not exponential impact
*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.
and I can easily check it was completed correctly
Can you always though?
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.
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.
All that power used for a fucking coin flip.
Yawn…
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!
2 X 0 = 0
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…
“performance”
