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AI models collapse when trained on recursively generated data

nature.com

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Re: AI models collapse when trained on recursively generated data

#11

Post by tyingq »

roughly wrote: post #6
tyingq wrote: post #2

Which is good background to this story about Reddit locking down robots.txt and trying to get money from the AI teams scraping their content. https://news.ycombinator.com/item?id=41057033

If they're considering Reddit content to be free of generated material, I've got bad news for them. It's not quite the Chernobyl-grade hole that Pinterest has become, but it's hardly "low background".

Sure. I think Reddit is aware though, that time is running out to get paid for whatever human generated content is there that isn't already scraped.

Re: AI models collapse when trained on recursively generated data

#12

Post by mensetmanusman »

jlos wrote: post #8

As far as I understand Douglas Hofstadter's Godel, Escher, Bach - self-referential recursive structures (strange loops) are the foundation of consciousness (among other interesting things). I've been watching to see if LLM's becoming self-referential actually improves them as opposed to degrades them.

The interesting thing about loops is that they can generate fields (think motion if current generating a magnetic field).

Consciousness is more like a field than like a particle (which are also fields), but we haven’t determined how conscious fields fit in physics models.

Re: AI models collapse when trained on recursively generated data

#13

Post by [deleted] »

tyingq wrote: post #11
roughly wrote: post #6

Earlier quoted context omitted.

If they're considering Reddit content to be free of generated material, I've got bad news for them. It's not quite the Chernobyl-grade hole that Pinterest has become, but it's hardly "low background".

Sure. I think Reddit is aware though, that time is running out to get paid for whatever human generated content is there that isn't already scraped.

[deleted]

Re: AI models collapse when trained on recursively generated data

#18

Post by asah »

roughly wrote: post #4

Back when I was getting my econ degree, we were taught about the Ultimatum game, which goes like this: You get two participants who don't know each other and will (ostensibly) never see each other again. You give one of them $100, and they make an offer of some portion of it to the other. If the other accepts, both parties keep their portion - so, if A offers B $20, and B accepts, A keeps $80 and B keeps $20, if B re…

...in the real world, A tells B that he "sourced" the deal and therefore deserves a bigger cut and in the real world, B agrees up to a point (the $30 mark). Over time and rounds of playing the game, the A's of the world learn where the line is and optimize to stay on the correct side of it, only testing the other side 1-2% of the time to see if rules/behavior has changed.

Re: AI models collapse when trained on recursively generated data

#19

Post by slashdave »

anon291 wrote: post #14

Is this an artifact of floating point precision or a fundamental mathematical truth.

Floating point precision is not involved (most LLM models still function after floating-point quantization).

I am puzzled that some find this result at all surprising. You simply cannot generate information from nothing.

Re: AI models collapse when trained on recursively generated data

#20

Post by skybrian »

anon291 wrote: post #14

Is this an artifact of floating point precision or a fundamental mathematical truth.

It’s a lossy transformation, so you’re losing information each time. It’s never going to add information.

However, some information is junk that obscures the good stuff. It’s likely that how they train today is very inefficient compared to what’s possible, and there will be smarter ways to transform preexisting data so that it’s a better dataset to train on, without losing very much.

Papers like this one show what not to do.

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