From the HN comment rules:

> Be kind. Don't be snarky. Converse curiously; don't cross-examine. Edit out swipes.

> Please don't fulminate. Please don't sneer, including at the rest of the community.

My read of the comment is: "You are correct, but bear in mind that the world seems infested with people who are far less realistic and honest than you."

The rules also say "Please don't complain that a submission is inappropriate. If a story is spam or off-topic, flag it. Don't feed egregious comments by replying; flag them instead. If you flag, please don't also comment that you did."

I'm not really sure it's the best idea to accuse someone of breaking the rules if in doing so you're also breaking one yourself.

Let's be snarky a bit:

Can you do a neural network that, given a starting position of the game of life, decides if it cycles or not? ;)

Ok, not cycles... dies, stabilizes, goes into a loop etc.

So Oracle's working on an LLM to, eh?

<cough> halting problem. But now I'm spoiling it.

We know neural networks cannot solve the halting problem. But isn’t the question whether they can learn the transition table for game of life? Since each cell depends only on neighbors, this is as easy as memorizing how each 3x3 tile transitions.

The original question, maybe. Mine is basically the halting problem, I think.

The other difference is I don't take it seriously.

Wolfram looked at this recently on his blog.

https://writings.stephenwolfram.com/2024/05/why-does-biologi...

He says it's possible for smaller games (fewer rules) but unlikely for larger ones.. IMHO anything Turing complete would have this problem.

Everybody and their mom are into LLMs.

Same thing happened with the Internet.

And Second Life and Myspace.

For a grid of a fixed size, yes.

The halting problem doesn't mean you can never decide if something cycles etc, just that you can't always decide.

As it stands, my guess is that the LLM would always confidently make a decision, even if it were wrong, and then politely backtrack if you pushed backed, even if it were originally right.

Every other day we see demos of AIs doing things that were thought of an impossible 6 months earlier, but sure, sounds like it's the "hype merchants" who are out of touch with reality.

Not according to the actual article that you're commenting on, either.

"As the researchers added more layers and parameters to the neural network, the results improved and the training process eventually yielded a solution that reached near-perfect accuracy."

So, no, we aren't asking too much from it. We just need more compute.

??

They are displaying emergence. They might as well be the walking definition of it.