The fingerprinting they're doing now is meant to help with this. If they see a fingerprint, it was probably not human generated so don't ingest it.
Won't come at all. It's mathematically impossible.
Proof?
The output of a statistical model cannot contain more information than what it already had.
In one sense, true… in another sense, somehow, evolution did just that over the course of hundreds of millions of years. We are the proof, are we not?
"That" is not what evolution did.
It's entirely possible to improve current AI using only information available to us right now. Once that well runs dry, current AI is in theory capable of running experiments and training on their results if we give it a harness to do that. This gives it access to new information. Could it succeed doing this? Unclear, but it is capable of trying. How do you think we discover AI improvements? Divine inspiration? No, we follow a relatively simple research loop.
found that AI agents could solve the engineering problems necessary to do AI research but lacked the judgment and creativity to produce original research at the caliber of papers accepted by a top machine-learning conference.
I mean that describes a majority of engineers. No small feat
If the standard of ML talks at conferences I've attended is anything to go by then a top machine learning conference is functionally a daycare for the most annoying people you've ever met
I'm pretty sure this sort of thing was tried with the prior era of neural nets too. When the field hits a ceiling they grasp at the make-AI-teach-itself straw. It's the Hail Mary pass. What if we keep stacking AIs on top of each other. Maybe they'll somehow break out of their own limitations.
There's a cadence. Once in a while a breakthrough happens. The tech is incorporated into the world. There are variations of the tech, but all have the same fundamental ceiling.
The AI Effect takes place. People forget about AI for a while. Time passes. A breakthrough paper is published. AI is upon the world once again.
Only this time with LLMs, it's seemingly passed the Turing Test so people think it's close to the fictional AGI. Not just recognizing handwriting, speech, or images. Or putting an annoying animated character on your desktop. This time it's being freakishly good at predicting what the next words should be based on known sum total of human knowledge. Making it be creative isn't it this time. That's the ceiling.
it seems that for most people generating clip art+ counts as creativity
Turing Test is fundamentally based on fooling humans. Not sure it's smart to pin humankind's future on what a birthday magician can do.
How is the Turing test related to the article ?
Someone should DLSS this meme
What I would have never have guessed. I'm shocked I say. /s
not a reflection on the quality of OP's submission, but man... like every day now I wish we had an active "noshitsherlock" sub for headlines like these
It's not being said for the benefit of those who already know.
Lol what? Of course it won't, If the AI slop ends with recursive edits it's just going to cause degradation and collapse. I swear techbros have reality confused with their favorite fantasy fiction books.
EDIT: To demonstrate, 90% accuracy of 90% is 81%. Even the best most specific models on earth are not capable of self improvement because they will never reach much less exceed their training data's capability even if the largest most perfect dataset existed. They might think that by simply adding more layers of machines running in parallel and killing off models which underperform creating a system similar to evolutionary adaptation that it might eventually reach that 91%, but our current approach and level of technology have never demonstrated that capability not even theoretically.
I think people made some crazy magical assumptions about AI based on scifi that doesn't apply to real life. Real things to consider and prepare for, but not likely.
Recursive exponential self improvement? Just because something knows how to code doesnt mean it can make the best ultimate next version of itself. Even physical evolution takes millions of years, and it creates mistakes and has setbacks.
We might be seeing logarithmic AI improvement today, like evolution hitting a hill that it can't cross. We might need trillions more gigabytes of clean training data that isn't LLM generated to hit the next level, and it might not even be worth it.
I think what the AI developers are doing by constantly promoting ai fear is linking the idea of exponential ai self improvement to it, because their biggest fear right now might be investors realizing that isn't real, and every dollar they invest is getting less and less back.
Exponential improvement is indeed optimistic - a sigmoid curve (plateauing after a period of increase) is much more plausible, though in the computer programming case I haven't noticed the plateau yet.
Indeed, throughout nature it's almost all sigmoids. The trick is that sigmoids look exponential before the inflection point and it's hard to predict when that inflection point is going to come.
Agreed... I've been dabbling in "smart" algorithms for 50 years, the recent (last 8-10 years) progress has been dramatically faster than the previous 40, but each new amazing field: voice transcription, language translation, computer vision object recognition, games mastery, have all rather obviously hit sigmoid-like plateaus. LLM agent software writing has been a slow-burn improvement over the past 18 months - from my perspective it seems like it's still improving, though that also seems to be a combination of the models getting better, their built in instructions getting better, my local "memory" getting better, and me learning what to challenge it with and what's unrealistic. A big sign for me is: something I challenged it with 12-14 months ago and got basically nowhere, I tried again last month and it's made solid progress, delivering a lot of features it couldn't last year - and those are a lot of features I "gave up on" 5-6 years ago, not because they were impossible, but because they were just too much annoying, time consuming work for the value they deliver to me (personally) - and now the barrier to entry for making those things happen in software is dramatically lower.
Mmmh... They used Opus 4.8 (which is already outdated) as the model, and OpenClaw (which is utter garbage) as the harness...
Everything is false until it's true? There's governments and corporations around the world right now racing to make that happen. What's the point of the article? If it was so easy it would have been delivered already
A year ago they were similarly bad at writing code, often created unit tests that tested nothing, etc.
If the models are trained in what they're doing wrong, that can accelerate their progress toward doing it right.
They still don't get it right all the time, they just stacked a few together to filter out the obviously wrong stuff.
They would need to be trained for open ended creative tasks, which is just hard in the current reinforcement learning paradigm.
I think they'll find infinite ways to fuck up. The guardrails will never be high enough, or strong enough.
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