Are AI models narrowly creative?

If there had been no AlphaGo and no Move 37, and a human had made that move, as they have similar moves, it would no doubt be called creative genius (as similar moves have). Isn't the above a double standard?

Move 37 was not new knowledge. It was the winning choice in that situation before the AI ever existed, because it was deducible from the game's rules and the current board state. It was implicit knowledge, already contained in the system at that time. AlphaGo made it explicit, by finding it, like a search engine, but did not create it. If you calculate the trillionth digit of pi, you haven't created new knowledge, at least not in any sense we should mean. You have simply revealed a value that was already fixed by a definition.

The fact that Move 37 wasn't explicitly in the training data or the programmers is irrelevant to its status as knowledge. This is true for pi, and for all content created by AI at the time of this writing.

Be sure to mention the title of your book so others can look it up :)

By the latter standard, neither nature nor random number generators are people, which is sensible; nor can nature create any given possible knowledge tractably -- this is true because the fact that all possible knowledge exists is only by way of the multiverse, which is a process that cannot be simulated in its entirety, even by a quantum computer, never mind tractability.

By this standard, a random number generator has universal creativity as well, and is therefore a person. So there must be standard for personhood other than: able to generate any possible explanation. Such as: can do that tractably.

Nature does have universal creativity; it can generate any possible knowledge. And all possible knowledge exists somewhere in reality.

But nature created genetic knowledge from nothing. So this is an example of something which does not have universal creativity which created knowledge ex nihilo.

This also admits of the distinction between AI and AGI (and "universal creativity") as being whether the system is capable of creating knowledge ex nihilo, as argued by Deutsch. Only universal creativity could create knowledge from nothing. Bounded creativity must start with something.

I keep returning to the notion of the space or domain in which simulated evolution so far operates in. It seems like we can say that current sim'd evolution can discover new knowledge via conjecture and criticism, but it is always bound by a domain predefined by fitness functions, automatic evaluators and so on, even if that domain itself contains many subdomains.

Then we can say that in nature, and in the minds of people, there is no externally defined space in which exploration is happening; the space is also evolving, also subject to criticism. I suspect this is part of how open-endedness comes about.

But the immediate question here was how to explain why AI is or is not "creative". Saying AIs are "narrowly creative" seems it could work, or saying they are creative within a fixed domain. The common intuition I think is that current AIs are "truly" creative, and I would say this is because the predefined domain (of LLMs, for instance) is gigantic, being sculpted by an internet-sized training corpus. But I suppose we should argue that "true creativity" means universal creativity.

I was curious if there are criticisms of the argument that current AI does legitimately create new knowledge.

This seems to me to be the same distinction that Deutsch and others have made between the genetic evolution we can simulate through evolutionary algorithms and the kind we actually observe in nature. I think it would be helpful to investigate evolutionary algorithms a bit further if you want to develop a clear distinction. This is how I describe it in my book:

There are several mechanisms that genes use to create variants, including sex, mutation, gene flow, and genetic drift, all of which appear to introduce change randomly. But we now know it cannot be entirely random. Something more is shaping what gets trialed, because when we model and simulate evolution using random changes, we never see the sort of novelties that arose in nature. We see optimization. We see exploitation. We see organisms become better at using resources they already use. But we never see a genuinely new use of a resource emerge. A fin may become better at swimming, but it does not become a limb. A metabolism may become more efficient, but it does not open up an entirely new biological pathway. And yet the natural world is full of exactly such extraordinary adaptations.

Move 37 was not explicitly present in the training data, nor designed by the programmers, and is extremely hard to vary (Deutsch's criterion for good explanations). Was the move present implicitly in the design of the system and/or the training data? Or inexplicitly? Does either of these mean the discovery of the move was non-creative?

Since evolution created genetic knowledge from nothing, it can be said to have the same "narrow creativity" as AI. The confusion over whether AI "is creative" can be resolved by saying that it is, but only narrowly (like evolution), and that the creativity defining people is universal, not limited to any domain. AI creates knowledge in domains it was designed for; AGI can create knowledge in all possible domains, each of which it designs itself.

Recent conversations have revealed that I cannot argue against the notion that current AI systems create new knowledge (and so are creative in some domain). Yet, David Deutsch has argued that creativity is a binary property of programs, and those that have it are people (including AGIs), who can create all possible explanatory knowledge. I am not of the mind that current AI is AGI. So I will try to iron all this out, here.

AIs have created output that is not only novel, but seems to constitute new knowledge (resilient information), such as the famous Move 37 from AlphaGo. That is new knowledge because the move was not present in the training data explicitly, nor did the designers construct it.