bobmarleybiceps (u/bobmarleybiceps)

bobmarleybiceps commented on LoC is a dumb metric for functions theaxolot.wordpress.com/2... · Posted by u/Axol

A personal guideline for a lot of stuff is that a function may be too long when people add comments to mark what sections of it do. (ofc not really a hard rule). I just think it's easier to see "oh this is calling the load_some_stuff function, which I can easily see returns some data from a file." Rather than <100 lines of stuff, inlined in a big function, that you have to scan through to realize it loads some stuff and/or find the comment saying it loads some stuff>. That is to say, descriptive functions names are easier to read than large chunks of code!

smaller functions are also usually easier to test :shrug:

bobmarleybiceps commented on John Jumper: AI is revolutionizing scientific discovery [video] youtube.com/watch?v=2Yguz... · Posted by u/sandslash

hodgehog11 · 3 months ago

> We need more of these.

> Available data is 0 for most things.

I would argue that we need an effective alternative to benchmarks entirely given how hard they are to obtain in scientific disciplines. Classical statistics has gone very far by getting a lot out of limited datasets, and train-test splits are absolutely unnecessary there.

bobmarleybiceps · 3 months ago

I kind of dislike the benchmarkification of AI for science stuff tbh. I've encountered a LOT of issues with benchmark datasets that just aren't good... In a lot of cases they are fine and necessary, but IMO, the standard for legit "success" in a lot of ML for science applications should basically be "can this model be used to make real scientific or engineering insights, that would have been very difficult and/or impossible without the proposed idea."

Even if this is a super high bar, I think more papers in ML for science should strive to be truly interdisciplinary and include an actual science advancement... Not just "we modify X and get some improvement on a benchmark dataset that may or may not be representative of the problems scientists could actually encounter." The ultimate goal of "ml for science" is science, not really to improve ML methods imo

bobmarleybiceps commented on SimpleFold: Folding proteins is simpler than you think github.com/apple/ml-simpl... · Posted by u/kevlened

barbarr · 3 months ago

Why is apple doing protein folding?

bobmarleybiceps · 3 months ago

This may not be the actual reason in this case, but I think it's good to be aware of: A non-zero chunk of "ai for science" research done at tech companies is basically done for marketing. Even in cases where it's not directly beneficial for the companies products or is unlikely to really lead to anything substantial, it is still good for "prestige"

bobmarleybiceps commented on Towards a Physics Foundation Model arxiv.org/abs/2509.13805... · Posted by u/NeoInHacker

bobmarleybiceps · 3 months ago

I'm not an expert on this, so take this with a grain of salt. Chaotic PDEs are extremely sensitive to initial conditions. This essentially makes it so that any numerical solution will (quickly) diverge from the true solution over time. (Just due to floating point error, discretization error, etc.) This is why for a lot of turbulent navier-stokes stuff, people don't necessarily care about the specific phenomena that occur, but look at statistical properties.

I think one of the reasons it is important to preserve conservation laws is that, at the very least, you can be confident that your solution satisfies whatever physical laws your PDE relies on, even if it's almost certainly not the "actual" solution to the PDE. You actually can ensure that a numerical solver will approximately satisfy conservation laws. Then at the very least, even if your solution diverges from the "actual" PDEs solution, you can have some confidence that it's still a useful exploration of possible states. If conservation laws are not preserved AND your solution diverges from the "actual" PDE solution, then you probably cannot be confident about the model's utility.

bobmarleybiceps · 3 months ago

Actually I just happened to see this: https://www.stochasticlifestyle.com/how-chaotic-is-chaos-how.... It's basically explaining the same thing, but much better than me :-)

bobmarleybiceps commented on Towards a Physics Foundation Model arxiv.org/abs/2509.13805... · Posted by u/NeoInHacker

ogogmad · 3 months ago

Why? Is this important as a sanity check in the absence of any independent verifications?

bobmarleybiceps · 3 months ago

I'm not an expert on this, so take this with a grain of salt. Chaotic PDEs are extremely sensitive to initial conditions. This essentially makes it so that any numerical solution will (quickly) diverge from the true solution over time. (Just due to floating point error, discretization error, etc.) This is why for a lot of turbulent navier-stokes stuff, people don't necessarily care about the specific phenomena that occur, but look at statistical properties.

I think one of the reasons it is important to preserve conservation laws is that, at the very least, you can be confident that your solution satisfies whatever physical laws your PDE relies on, even if it's almost certainly not the "actual" solution to the PDE. You actually can ensure that a numerical solver will approximately satisfy conservation laws. Then at the very least, even if your solution diverges from the "actual" PDEs solution, you can have some confidence that it's still a useful exploration of possible states. If conservation laws are not preserved AND your solution diverges from the "actual" PDE solution, then you probably cannot be confident about the model's utility.

bobmarleybiceps commented on Gluon: a GPU programming language based on the same compiler stack as Triton github.com/triton-lang/tr... · Posted by u/matt_d

lukax · 3 months ago

Is this Triton's reply to NVIDIA's tilus[1]. Tilus is suposed to be lower level (e.g. you have control over registers). NVIDIA really does not want the CUDA ecosystem to move to Triton as Triton also supports AMD and other accelerators. So with Gluon you get access to lower level features and you can stay within Triton ecosystem.

[1] https://github.com/NVIDIA/tilus

bobmarleybiceps · 3 months ago

it feels like Nvidia has 30 "tile-based DSLs with python-like syntax for ML kernels" that are in the works lol. I think they are very worried about open source and portable alternatives to cuda.

bobmarleybiceps commented on Towards a Physics Foundation Model arxiv.org/abs/2509.13805... · Posted by u/NeoInHacker

measurablefunc · 3 months ago

How do they prove their model preserves conservation principles? I looked in the paper & didn't find any evidence of how they verify that whatever their "trained" model is doing is actually physically plausible & maintains the relevant invariants like mass, energy, momentum, etc.

bobmarleybiceps · 3 months ago

I think very few of these "replace numerical solver with ML model" papers do anything to verify invariants are satisfied (they often are not well preserved). They basically all just check that the model approximately reproduces some dynamics on a test data of PDEs, that's often sampled from the same distribution as the training dataset...

bobmarleybiceps commented on Teenagers no longer answer the phone: Is it a lack of manners or a new trend? phys.org/news/2025-08-tee... · Posted by u/PaulHoule

bobmarleybiceps · 4 months ago

100% of the time I get a call, it's spam, a doctor, or my mom.

bobmarleybiceps commented on Tversky Neural Networks gonzoml.substack.com/p/tv... · Posted by u/che_shr_cat

dkdcio · 4 months ago

> Another useful property of the model is interpretability.

Is this true? my understanding is the hard part about interpreting neural networks is that there are many many neurons, with many many interconnections, not that the activation function itself is not explainable. even with an explainable classifier, how do you explain trillions of them with deep layers of nested connections

bobmarleybiceps · 4 months ago

I've decided 100% of papers saying their modification of a neural network is interpretable are exaggerating.

bobmarleybiceps commented on Los Alamos is capturing images of explosions at 7 millionths of a second lanl.gov/media/publicatio... · Posted by u/LAsteNERD

AlotOfReading · 5 months ago

Nuclear research is done under the Department of Energy, not DoD. Los Alamos is a DoE lab, and the DoE received major cuts in the recent budget bill, though that shifts energy efficiency research into weapons research and net increases lab funding.

bobmarleybiceps · 5 months ago

I have heard that their internal review processes for papers have started telling people to not say stuff like "XYZ may be useful for climate research" or "this is an alternative energy source that's environmentally friendly." Like they are literally discouraged from talking about climate stuff at all lol.