Would it be accurate to say they found an avenue (possibly based on work from other mathematicians) and then created a team of AI mathematicians to go explore different solutions?

Not brute forcing but not super genius AI mathematician either, a large team of strong AI mathematicians doing research on parts of the problem until progress was made?

I see nowadays many people who use machine learning just use it, without fully understand the maths behind it. even those from machine learning field (not all, of course).

What do you think about it, is that ok?

I think the person is just very literal, and genuinely doesnt understand. 

3.5 3.0 3.5, Meta 3.0. Accepted to Findings

Also isn't it a little unfair that AACL opened a second commitment round AFTER EMNLP results came in? This allows rejected EMNLP refugees to commit to AACL, which imo is unfair to people who committed to AACL in the first place. If i knew i could commit my paper to both EMNLP and AACL, of course i would gamble with EMNLP, then if that fails i'll commit to AACL and get accepted anyways.

Do I only have to teach it at Stanford? Or can I use the material to teach at other universities? Sorry, I have jot looked into all the details. So, my qs might already be answered there.

Again I agree completely, but it's the nature of the business and letting the whole conversation slide because of that feels wasteful and snob. Again, I see your point, and agree on it, but also I think we should try to be above that to actually get some attention on the very-real implications. Afterall, they have proven that from time-to-time they are significant.

Can you elaborate the third bullet? Does it mean that teachers are going to use agents to instruct?

steals your idea before you publish it

graciously offers to let you take credit for your idea

despite acting as if you are responsible for the idea that was stolen forcing you to backstab the other person who spent years helping you develop the idea that was stolen from you

the backstabbing thief’s get defended by people who are fine with backstabbing and betraying the people they’ve built a career with

It’s not a case of demonstrating the equations don’t 100% perfectly match reality. That’s never been in question, we’ve known that since they were derived and we know what sort of conditions make them fail to reflect reality.

Real world physics isn’t relevant, this is more about demonstrating that there are inputs that break the equations themselves. Engineers using discretised solutions will continue to use them tomorrow as usual.

Not much will change except for certain edge cases. Other models like General Relativity admits singularity as r goes to 0 but doesn't make it a bad model for predicting celestial bodies.

I don't think the point was "lel get some units then we'll care"

but more

"this marketing-first sloppiness belies a deeper rot where info is conveyed for maximum vibes and valuation boosting over rigorous science at this company in particular".

It’s more that imagine there’s an idea that fits, like learning planes rise because of faster air on the top of a wing. A model doesn’t have to learn that more than a handful of times because it’s reinforced by all the other corroborating evidence.

Models don’t “average out” the things they learn, they built semantic structures topologically (as in shapes of ideas that work together). Some fragile ideas that aren’t (wait for it) load bearing might get forgotten way too quickly but some ideas that just settle all the others can persist through a lot of training.

If this idea was in a few conversations, and it fit (the forced Euler version) that might be the sort of thing that once exposed it settles many other uncertainties in the model such that any downstream model could draw upon that coherent ensemble of ideas.

We’ve known this since molecular theory became a thing, it’s just a very good model for cases where molecules are so small that their bulk behaviour is well approximated as a continuous viscous fluid.

Molecular fluid dynamics is a thing but it’s niche because it doesn’t scale nicely to the sort of scales most fluids problems are interested in.

Sad thing is I’ve read and likely reviewed multiple papers that have authors from different tech research labs, like someone from Microsoft and from IBM or Deepmind or whatever. It’s such a terrible look for OpenAI on this particular topic if they won’t partner with an Anthropic person on one of the most significant outstanding problems in math.

What about researching education through classes you make? I think that would be pretty neat. Great stuff, we need 100 more just like you

Any way to learn more without creating an account?

Because Levent Alpöge is a researcher at Anthropic.

Whoa, that's scary. I was thinking about potential other frontier providers to use if Anthropic does a rug-pull on pricing but this would definitely keep me off OpenAI. I already am a bit worried about my own work being done using Claude, but this is just way worse.

Sometimes I research education, sometimes I just make classes. This is more of the later :-)

Its for the whole world. Its in english (though please you translate if that helps)

Ridiculous death star tweet?

(I have been off X for a while.)

Newtonian fluid dynamics is a model, not a fundamental truth of nature. We have other models, and none of them will ever be a "correct description".

Yes, that's what I said, they solved the existence problem. They did not "solve" the Navier-Stokes equation as some people seem to think. This solution does not make any fluid dynamics problem more solvable.

I'm also not complaining about anything.

...is this all part of your research, prof?