It's possible LLMs can handle this after all! But at least so far we only have existence proofs of humans doing this, not LLMs yet, and I don't think it's easy to be certain how far away LLMs are from doing this. I should distinguish between LLMS and AI more generally here; I'm skeptical LLMs can do this, I think some other kind of more complete AI almost certainly can.

I supposed you could just, I dunno, randomly combine words into every conceivable sentence possible and treat each new sentence as a theory to somehow test and brute force your way through the infinite possible theories you could come up with. But at that point you're closer to the whole infinite random monkeys producing Shakespeare thing than you are to any useful conclusion about intelligence.

Imagine every bit of human knowledge as a discrete point within some large high dimensional space of knowledge. You can draw a big convex hull around every single point of human knowledge in a space. A LLM, being trained within this convex hull, can interpolate between any set of existing discrete points in this hull to arrive at a point which is new, but still inside of the hull. Then there are points completely outside of the hull; whether or not LLMs can reach these is IMO up for debate.

Reaching new points inside of the hull is still really useful! Many new discoveries and proofs are these new points inside of the hull; arguable _most_ useful new discoveries and proofs are these. They're things that we may not have found before, but you can arrive at by using what we already have as starting points. Many math proofs and Nobel Prize winning discoveries are these types of points. Many haven't been found yet simply because nobody has put the time or effort towards finding them; LLMs can potentially speed this up a lot.

Then there are the points completely outside of hull, which cannot be reached by extrapolation/interpolation from existing points and require genuine novel leaps. I think some candidate examples for these types of points are like, making the leap from Newtonian physics to general relativity. Demis Hassabis had a whole point about training an AI with a physics knowledge cutoff date before 1915, then showing it the orbit of Mercury and seeing if it can independently arrive at general relativity as an evaluation of whether or not something is AGI. I have my doubts that existing LLMs can make this type of leap. It’s also true that most _humans_ can’t make these leaps either; we call Einstein a genius because he alone made the leap to general relativity. But at least while most humans can’t make this type of leap, we have existence proofs that every once in a while one can; this remains to be seen with AI.

At the time the consensus narrative was that SpaceX no longer needed Colossus 1 for Grok and that was why it could be leased to Anthropic while Colossus 2 would handle Grok training and inference. Does Anthropic also leasing Colossus 2 change this?

In big adventure RPG games there’s always some kind of shop in every new area that is the same inside everywhere that you can reliably go to for whatever gear you need, to heal, to save your game, whatever. Costco is that but in real life.

In general Anthropic seems to be pretty bad at clearly communicating what is going on. I have both Claude Pro for Claude Code and ChatGPT Plus for Codex, and lately I've been reaching for Codex first more and more often... at least for the hobby stuff I'm using Claude/Codex on, they seem pretty much equivalent in terms of practical capability/usefulness.

Comments URL: https://news.ycombinator.com/item?id=47451326

Points: 1

# Comments: 1

https://youtu.be/5SaFTm2bcac?si=J99_Sh9x3fIBCSms

That stopped being true many years ago though, and the divergence has only accelerated with the advent of AI datacenter usage. The form factor is now fundamentally different (SXM instead of PCIe); you can adapt an SXM card to PCIe with some effort [1], but that may not even be worthwhile because 1. the power and cooling requirements for the SXM cards are radically different than a desktop part and more importantly 2. the dies are no longer even close to being the same. IIRC, Blackwell AI chips straight up don't have rasterization hardware onboard at all; internally they look like a moderate number of general SMs attached to a huge number of tensor core. Modern AI GPUs are fundamentally optimized for, well, mat-mults, which is not at all what you want for gaming or really any non-AI application.

[1] https://l4rz.net/running-nvidia-sxm-gpus-in-consumer-pcs/

[1] https://macwright.com/

[2] https://macwright.com/2016/05/03/the-featherweight-website

Comments URL: https://news.ycombinator.com/item?id=47158911

Points: 2

# Comments: 0

When I first started at Disney Animation, at one point I asked Ed Catmull what the rationale was for staffing two separate rendering teams, and he had an interesting answer. His answer was that it turns out that even when Disney Animation was using RenderMan, the high end needs of the studio still required enough rendering developers/TDs that in terms of cost it was essentially no different than staffing a team to build an in-house renderer, and from that perspective he liked the idea of having two separate teams with different focuses/perspectives so that for hard problems the wider company got two attempts at coming up with good solutions instead of one.

To this day the Hyperion and RenderMan teams work pretty closely together and share a lot of learnings/tech/R&D. The focuses are pretty different between the two renderers, and that’s actually been pretty beneficial to both.

The story with Presto is both a bit different and kind of similar. The two studios are now unified in using Presto, but Disney Animation now has an in-house Presto development team that co-develops Presto with Pixar. The two dev teams focus on the needs of their respective studios but move Presto forward together.