Re:Vote on which of Hacker News' challenges for AI have been...
by hatthew on Friday October 02, 2026 @06:40PM
(#49939492)
Genuine question, not trying to be difficult: how much have you worked with training/finetuning generative models? In my experience, models frequently converge to a semi-arbitrary style. I wouldn't be surprised if some of the quirks we see in recent gen AI models are artifacts of a particular training run or amplification of unintentional dataset bias, rather than artifacts of an architecture or human guidance.
Re:Vote on which of Hacker News' challenges for AI have been...
by CamperBob2 on Friday October 02, 2026 @08:42PM
(#49940323)
Not much beyond the Zero-to-Hero videos, TBH. I do know that once bias is identified and labeled, it can be post-trained out... or in.
At least to me, the first Dall-E 3 images were more realistic in some respects than anything that has shipped since. Likewise, I don't think it's a coincidence that the conversational capabilities of today's frontier models aren't much better than they were a year or two ago, even though other aspects and capabilities have improved massively. If they wanted a Turing test-capable model with no superficial tells, they'd have it, so I have to assume they don't want it.
(Elsewhere in the thread someone else suggests that the conversational degradation/lack of improvement might be the result of increased training on synthetic data, and that's another theory that sounds reasonable to me. If so, it's another thing they could fix if they wanted to.)