[kristiandupont]

I had Claude help me get a program written for Linux to compile on macOS. The program is written in a programming language the author invented for the project, a pretty unusual one (for example, it allows spaces in variable names).

Claude figured out how the language worked and debugged segfaults until the compiler compiled, and then until the program did. That might not be magic, but it shows a level of sophistication where referring to “statistics” is about as meaningful as describing a person as the statistics of electrical impulses between neurons.

[compass_copium]

But the programming language has explicitly laid out rules. It was not trained on those sets of rules, but it was trained on many trillions of lines of code. It has a map of how programs work, and an explanation of this new language. It's using training data and data it's fed to generate that result.

[selridge]

What doesn't that explain tho?

What behavior would you need to see for that explanation to no longer hold? Because it seems like it explains too much.

[BobaFloutist]

I don't know how you'd prompt this, but if there was a clean example of an A.I. coming up with an idea that's completely novel in more than details, it would be compelling evidence that these next-token predictors have some weird emergent properties that don't necessarily follow from intricate, sophisticated webs of token-prediction.

E.g. "What might be a room-temperature superconductor" -> "some plausible iteration on existing high-temperature superconductors based on our current understanding of the underlying physics" would not be outside how we currently understand them.

"What might be a room-temperature superconductor?" -> "some completely outlandish material that nobody has studied before and, when examined, seems to have higher temperature superconducting than we would predict" would provoke some serious questions.

A fun experiment I've heard suggested is training a model on all scientific understanding just up to some counterintuitive quantum leap in scientific understanding, say, Einstein's theory of relativity, and then seeing if you can prompt it to "discover" or "invent" said leap, without explicitly telling it what to look for. This would of course be pretty hard to prove, but if you could get it to work on a local model, publish the training set and parameters so that anyone can replicate it on their own machine, that could be pretty darn compelling.

[selridge]

Why would it matter whether or not the robot looks something up if it makes a novel discovery?

Why would it matter that the discovery wasn't just novel but felt like an unconventional one to me, someone who is probably a total outsider to that field?

Both of those feel subjective or at least hard to sustain.

Look. What I'm trying to tell people is that the easy explanations for how these models worked circa GPT-2 is just not cutting it anymore. Neither is setting some subjective and needlessly high bar for...what exactly? What? Do we decide to pay attention to AI after it does all the above? That seems a bit late to the party for cheering on or resisting it.

Some new shit is afoot. Folk need to pay attention, not think they got it figured out already.

[compass_copium]

Programs are fundamentally lists of instructions. LLMs are very good at building these lists. That it performs well when you say "Build a list you've seen before, but do it in a slightly different way this time. Here's the exact way I want you to do it." is not surprising. I would honestly be surprised if it couldn't do it.

As the other commenter suggested, a genuinely novel scientific idea would be surprising. A new style of art (think Picasso or Pollack coming along), not just an iteration on Ghibli, would be surprising. That's actual creativity.

[selridge]

>I would honestly be surprised if it couldn't do it.

You'd be surprised if an LLM couldn't write *any* program?

[orf]

That’s still over-general to the point of being useless.

What you wrote would apply to a human approaching this task as well, sans the “many trillion lines of code”.