Software needs to function correctly; a marketing campaign should be aesthetically consistent and interesting while conveying the right message to the target audience.

We know the pace of improvement for models built on human training data, like GPT-4. We don’t yet know the trend for synthetic data, as used in AlphaProof and o1.

Here’s another example. Last year, I asked GPT-4 to solve a real-world problem we faced while building Google Docs, and it failed miserably. o1-mini nailed it, although I gave it an important hint (GPT-4 was hopeless even with multiple hints). Without that hint, o1-mini did not manage to figure it out.

Creativity Doesn’t Require Novelty

There was a certain amount of creativity in the IMO proof I presented in my last post. But it contained nothing truly new; I was just gluing together known techniques and ideas. The creativity is in the combination – specifically, in finding a combination that worked.

can I point to an example of “creativity” that used a completely existing thing, the only cleverness was in realizing that thing was applicable?

Most (all?) human creativity can equally well be characterized as mixing and matching things that have come before. Lucas acknowledges that Star Wars was influenced by Kurosawa movies, etc.

https://claude.ai/chat/4d6ea2d6-598c-4a59-8e0f-fe0593009af4

shows ideas that are at least as “creative” as most movies.

A Lot of “Creative” AI Work is Crap

The flip side of arguing that AI can’t be creative because it only interpolates, is getting overly excited about some piece of AI output merely because it is novel.

[Zvi] Colin Fraser offers skeptical review of the recent paper about LLMs generating novel research ideas.

(A lot of non-AI creative work is also, of course, crap)

Don’t tell me AI (LLMs?) can’t be creative, unless you can give an operational (?) definition of “creative” and a coherent argument for why AIs (LLMs?) can’t meet that definition. An argument that doesn’t assume its conclusion (“they can only reproduce what’s in their training data”) or prove too much (humans made out of neurons can’t be creative either).

Conversely, be wary of getting excited about an AI doing a “creative” thing (writing a scientific paper) until it does it genuinely well (and watch out for false positives – benchmarks leaking into the training data, cherry-picking, questionable scoring methods used for the scientific papers, etc.)