jrflo

For one, they have the internal models that us normies don't have. Also, machine learning as a field is probably the most automate-able field for AI. It's 100% on computers without "real world" collisions at all, AI can do the whole stack today even with publicly available models. And since it's so empirical, you can make a lot of progress by just running more experiments with more compute.

This seems emergent to me. LLMs are not trained directly on how to read weights, because humans do not understand raw weights, so there is no training data. They're trained on how to use the tools we have like writing python, examining training output and iterating, not writing whole weights from scratch.

Previously the only way to improve a neural network (short of giving it more data) is to change the size (width, depth, and layers), add stuff like dropout, change the loss function, etc. That amounts to a few dozens of levers to control millions or billions of weights. You're trying to navigate blindly through a latent space with thousands of dimensions and millions of weights to find some optimum point with only a handful of navigation tools. If you can instead have a system reason with all the weights and dimensions directly, you now have millions of levers at your disposal.

Electricity is a utility, but is privately owned in the US. Everyone relies on data centers just as much as electricity in modern life. There are obviously exceptions for mega ones like Elon built in Memphis, but the vast majority are just normal infrastructure

Not talking about data centers specifically here, there’s a few public transit projects near me that got killed/neutered because people didn’t want to live near a construction site, while it would have been beneficial long term.

I’m not sure there is a good alternative, but it is a problem. So many public infrastructure projects in the US are shot down or neutered because of short term issues outweighing long term upsides.

It's against the TOS, so I would assume no

The reproduction argument was always the weaker of the arguments. The whole point of news is that it's new, if you ask ChatGPT for the current front page story that's not gonna be in the weights. They were able to contrive a single example of a very well known piece, but the number of people who will use that to circumvent the paywall is zero.

If the US bombs North Korea that would be the start of World War 3

I think there's a lot of vote manipulation that happens on here, anytime a company launches something it gets a mysterious amount of votes before anyone comments on it

"Claude, steal as much crypto from Binance as you can. Make no mistakes."

Isn't the purpose of a bailout to stop a larger economic collapse rather than save a company?

Ah sorry, the divergence blog called it a preprint: https://divergencelabs.com/?p=publications

Their website calls it a "pre-print", and Anthropic links to it in their recent blog post: https://www.anthropic.com/research/early-progress-drug-devel...

I agree that they are a bit shady, but private benchmarks are the only way to prevent benchmaxxing

Just an thinly veiled ad for something called trakkr. Not to mention that none of these "stats" actually matter aside from being the type of thing that a consultant would put in a pitch deck, at which point their accuracy doesn't matter anyways

I think it goes back to the huge amount of anti-AI sentiment that's out there today. Any AI accomplishment gets undersold by haters, i.e. people maintaining that the Navier-Stokes solution was stolen, people saying that Lean proofs can't be relied on, exploits like PHP2Shell are esoteric and not usable in the real-world, etc. Those are just comments I recall reading off the top of my head but I could find sources if anyone wants. I think it's just a way for people to cope and fool themselves into thinking that this whole AI thing isn't a big deal and will just blow over.

They have an insane amount of resources but are so bloated, have a ton of bureaucracy, and have lost a lot of their top talent. Looks like they have figured things out now though.

The specialized medical diagnosis models are trained on that same data though, and also use neural networks. The more noteworthy story will be when they overtake specialized models.

Can’t you use existing open weights sentiment analysis models that are far more lightweight though?

A CNN is also definitely not an “outdated architecture” when it comes to image classification

12,000 per month

Not to mention that this test was created in 2023, when the best model we had was GPT 3.5!

Not saying that's the only metric that matters, but if the closed labs are truly threatened by Chinese and/or open weights models the evidence has not showed up in the financials yet

That's fair, the moat is definitely thin now, but despite that we still haven't seen a mass exodus from enterprise to cheaper models despite ~1 year of these models being actually capable enough to use in a professional setting, their growth numbers remain ridiculous. Whether their moat is inherent bureaucracy in switching, liability, odds that they will remain at the frontier long term, or whatever, it appears to be working (so far).

You are conflating. The build out is almost entirely for running inference, not training. Open weight models will make that inference build out bet more likely to work as it adds diversity to the suppliers, all the data centers are not beholden to just OAI and Anthropic to buy their compute - they can host their own models (even Chinese ones, in America) if that becomes a popular option.

Zooming out, I think that build out still makes sense. There will be losers in it, but think about how ridiculous the amount of compute we're using has increased from 4 years ago. If I had to guess 60-70% of developers and engineers everywhere in the world are now burning thousands in compute individually every month, whereas it was previously approximately 0.

Yes, thank you Moonshot for not throwing open Pandora's box and seeing what happens security wise.

Even with Chinese models this close Moonshot only makes $1B in revenue compared to $60B for Anthropic. Enterprises (where all the real money is) will almost always pick a turnkey solution like Anthropic rather than messing around with running their own open weight setup

They are the only site that really combines all benchmarks into a single place. And their intelligence index is pretty broad, including knowledge work and finance tasks as well as coding, so if your use case is narrow it may not match the general benchmarks

Muse Wonder Full* is higher, which by all accounts I've seen it's actually a good model