bananaflag

Probably they have a more impressive model in preparation, and they're gonna dub that Fable 5.5.

Those theoretical physics papers look like the real thing (i.e. not mathematical physics like the OpenAI Yang-Mills result). It looks like AI is finally branching out of math.

> When I had started my PhD, I thought the writing part of a paper was just the last thing I needed to do to summarize my results, like a boring epilogue to the more exciting science.

I personally (caveat: in math, not science) always found the writing part more pleasant and exciting than the research part. In research, I was always anxious that I won't have any idea, or that the proof idea won't work. In writing, I was relaxed that I had obtained a result so I got excited about how to write it up beautifully.

And then in 10^33 years you'll fight viciously for the last pockets of negentropy.

> Adding reasoning agents, agent swarms, and so on to enhance the results from what is essentially just predicting the next token still means this technology at its core only predicts the next token. It is not AGI and unless something fundamental changes, it never will be.

A lot of people have dismissed LLMs four years ago for this reason. "It cannot possibly do coding since it doesn't think, it just predicts the next token." On the other hand, Scott Alexander did say in 2019 that next token prediction is a brilliant idea and might cover a lot, including math proofs [1]. So I wouldn't say it is impossible for either 1) LLMs to be all the way to AGI or 2) LLMs to become creative enough to tell us what the AGI architecture is.

[1] https://slatestarcodex.com/2019/02/19/gpt-2-as-step-toward-g...

This is what I've been saying for years, then I was surprised by Pangram actually working.

Sometimes it pays to attempt the impossible.

Stupid question: why is there a separate image model? Shouldn't GPT-6.x be multimodal?

Indeed, and as with AI, people felt very "empowered" by it. "Wow, I can do this" no, you just pushed some random buttons.

To be clear, I am also criticizing myself, I was an avid Microsoft Word user back then.

But why? Shouldn't people who like CS be more open to the idea? I mean this is why computers were created, to think instead of people.

I used to transfer files (photos mainly) by IrDA with my classmates between our phones in the mid-00s.

Shouldn't this be third quarter?

Anyway the growth this year is incredulous!

Who is "they" here?

> But even if it had solved every problem in the Langlands program, to imagine that would have “solved number theory” would be as naive as saying that the construction of the periodic table solved chemistry.

Of course if you solve any existing problem, new ones appear. That's not the point. The point is that AI will be able to advance on any problem better than any human.

> I've noticed people reacting to going hiking beyond cell networks with a "but that's dangerous!" response. Being outside of cell networks was the natural state of being for the vast majority of human existence!

And it was dangerous.

Also: I grew up just before cellphones started to be a thing and I felt a lot of times as a kid the frustration of not being able to contact someone remotely.

I personally would like more reviews of new capabilities unlocked. Like there was a lot of talk when o3 got released that it can do geolocation. Feels more interesting than being better at an existing capability.

> late 2024 did seem to be a plateau

Why? o1 was released in late 2024

This is exactly how I feel. I tell everyone around me who is churning AI papers that the whole thing is pointless.

In 1-2 years the AI will give an instant answer to most conjectures anyway.

It seems a strangely underdiscussed topic on HN.

I have heard that the 2nd edition of Inside Windows NT is much more informative than the 1st.

Yes, but thing is, for a lot of basic stuff there are myriad ways in which it is formulated, so humans have to decide which of them goes in the library. Of course you can include two or five or ten equivalent formulations (like textbooks sometimes do) but still, not everything under the sun.

Probably this will be done in some form in the coming months.

> The opening premise baffles me. What kind of illusion must one be under to believe that employers would ever pay you the same amount of money to do less work?

I guess the idea that AI will do all the work and either take care of us or kill us all. Which will probably happen, but the transition takes years and in the meantime we work much harder. People were expecting a fast takeoff.

> Why to multiply instead of going to the same place for knowledge?

Why write more than a book instead of just putting everything into one book?

This is a way of thinking from which it is hard to get out, I have no idea how to point it to you.

The text looks very weird on my PC, see: https://imgur.com/a/vAMVHc7

No, there's a lot of stuff where formal proofs don't make sense or are highly prohibitive.

The flagship case is AI itself, where we rely on benchmarks, not proofs (how could one prove "is good at seducing people").

Something that could have qualified as a "CS discovery" would have been, for example, a SAT or SMT solver that dwarfs in performance any existing one.

Something that could have qualified as a "physics discovery" would have been a new particle. Anyway, something that would come out of experimental data.

I find it a bit ridiculous that they claim three discoveries in "math, CS, physics" when they are basically all pure math (in particular, they all may be formalized in Lean) and only belong to these different disciplines for historical reasons.

Anthropic also claims a disproof of the ETH, posted here:

https://news.ycombinator.com/item?id=50255159

This is a book about this:

https://www.goodreads.com/book/show/37986693-factor-man

I guess it's the same as Opus 5.5 being in a way better than any Fable.

Ah, no!

About the fact that people may tend to judge people as characters in a book and not as real people with influence.