128
Generative AI Goes 'MAD' When Trained on AI-Created Data Over Five Times
(www.tomshardware.com)
you are viewing a single comment's thread
view the rest of the comments
view the rest of the comments
this post was submitted on 14 Jul 2023
128 points (100.0% liked)
Technology
34838 readers
10 users here now
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
founded 5 years ago
MODERATORS
Right, but the primary difference is that the AI is both creating errors and magnifying them in a horrifying Cronenberg feedback loop, where incest doesn't actually introduce errors.
That said, there's a known trait called inbreeding depression where fitness is reduced as a result of repeated inbreeding, however it can result is purifying selection that removes deleterious genes and recessive alleles that are unmasked by the inbreeding and actually increase fitness. If they could adapt some sort of testing algorithm to prevent rampancy maybe they could "breed" diffusion algorithms or just curtail the outputs of the current ones.
Though there'd probably be some strange feedback loops if it was set up as two adversarial models where one is trained to slap down weird outputs and the other is trained to adapt to rejected outputs.
Well, the ideal would probably be to train a discriminator based on human ratings of generated outputs.
Take generation 0 (G0), produce output which is accepted or rejected based on humans, train a discriminator to predict those ratings off output, and then use the combined accepted outputs from humans and trained discriminator to train G1.
Repeat again for G1, G2, G3, etc.
My guess would be that the end result would continue to get better and better rather than worse.
The problem is if the diffusion model can't properly reject weird hands or pupils, those magnify in subsequent rounds.
But there's likely adaptive and maladaptive tendencies in the diffusion model, and adding a halfway decent filter between human selection and synthetic selection of outputs separate from the diffusion model itself would effectively curb the magnification here.
It seems like a simple enough fix, though also setting a weird precedent. Instead of directly fixing things, just keep adding layers of machine learning to produce improved outputs.
The future of AI isn't spaghetti code, but spaghetti AI chains lol. Probably why people much smarter than me are the ones working on machine learning.