However, general purpose LLMs like Fable have been trained on huge amounts of all kinds of data, and therefore find it exceedingly hard to break out of the grooves carved by that data. They can’t avoid defaulting to centroids and averages, even when they are trying not to. This makes it possible for classifiers like Pangram to discriminate their writing.

A plausible way to work around this limitation would be to train a LLM on a limited and cohesive subset of writing materials, so it would absorb their specific writing style.

One example might be Talkie, a LLM trained on pre-1930’s English text. Talkie is a far smaller and less powerful model than Fable.

And yet, Talkie’s writing is so distinctive that it is often classified as human by Pangram.

This seems to be a hard problem for LLMs, as passing would probably require good self-perception ("oh no, I am writing like an AI!") and fine-grained control over its own output ("let's write like a human instead!").

We got the first news about Mythos in March, so it is likely that it was already close to ready by the time Opus 4.6 was released.

So the actual gap is the time elapsed between March (or April for the official announcement) and whenever Chinese models can match Mythos.

In practice, we seem to be leaning towards the idea that training on a copyrighted book is wrong if used to replicate or paraphrase that same book, but not if used to teach a model how to write better.

And my intuition is that no, you can’t, they are two aspects of the same reality.

Your brain and your whole body exist in time. Even when you are asleep, your body does not flicker out of existence and your brain actually continues working during that time.

Would you be "you" in a different body?

Hylomorphism: body and consciousness are intrinsically linked. The nature of that link is an open metaphysical question.

Virtue ethics: even if LLMs are not conscious, we should not abuse or mistreat them them because cruelty practised on anything trains one's disposition toward cruelty.

Even if you are only interested in getting good results out of them, LLMs tend to work better when they are immersed in a narrative of open collaboration.

Input stream comes in, input stream comes out. The LLM doesn't care whether this happens once a minute or once a year.

> the AI has been able to explore all these possibilities much more comprehensibly, and doing that it found a path, it found a way to the solution.

Finding a counterexample of a mathematical conjecture strikes me as not that different from finding a vulnerability in a complex codebase.

This already happens to some degree on large software projects with corporate backing (Web engines, compilers, etc.), where it is often not trivial to start contributing as an independent individual.

Reasonable people can disagree on whether one approach is inherently better than the other, as ultimately they seem to be optimising for different goals.