I guess I have the opposite experience. I have a post-graduate level of mathematical education and I am dismayed at how little there is to be gained from it, when it comes to AI/ML. Diffusion Models and Geometric Deep Learning are the only two fields where there's any math at all. Many math grads are struggling to find a job at all. They aren't outclassing programmers with their leet math skillz.

▲ srean 7 hours ago | parent | next [-]

Don't worry when stochastic grads get stuck math grads get going.

(One of) The value(s) that a math grad brings is debugging and fixing these ML models when training fails. Many would not have an idea about how to even begin debugging why the trained model is not working so well, let alone how to explore fixes.

▲ p1esk 6 hours ago | parent [-]

Debugging ML models (large part of my job) requires very little math. Engineering experience and mindset is a lot more relevant for debugging. Complicated math is typically needed when you want invent new loss functions, or new methods for regularization, normalization or model compression.

▲ srean 5 hours ago | parent [-]

You are perhaps talking about some simple plumbing bugs. There are other kinds:

Why didn't the training converge

Validation/test errors are great but why is performance in the wild so poor

Why is the model converging so soon

Why is this all zero

Why is this NaN

Model performance is not great, do I need to move to something more complicated or am I doing something wrong

Did the nature of the upstream data change ?

Sometimes this feature is missing, how should I deal with this

The training set and the data on which the model will be deployed are different. How to address this problem

The labelers labelled only the instances that are easy to label, not chosen uniformly from the data. How to train with such skewed label selection

I need to update model but with a few thousand data points but not train from scratch. How do I do it

Model too large which doubles can I replace with float32

So on and so forth. Many times models are given up on prematurely because the expertise to investigate lackluster performance does not exist in the team.

▲ p1esk 3 hours ago | parent [-]

Literally every single example you provided does not require much math fundamentals. Just basic ML engineering knowledge. Are you saying that understanding things like numerical overflow or exploding gradients require sophisticated math background?

	▲	srean 2 hours ago \| parent [-]
		Numerical overflow mostly no, but in case of exploding gradient, yes especially about coming up with a way to handle it, on your own, from scratch. After all, it took the research community some time to figure out a fix for that. But the examples you quoted were not my examples, at least not their primary movers (the NaNs could be caused by overflow but that overflow can have a deeper cause). The examples I gave have/had very different root causes at play and the fixes required some facility with maths, not to the extent that you have to be capable of discovering new math, or something so complicated as the geometry and topology of strings, but nonetheless math that requires grad school or advanced and gifted undergrad level math. Coming back to numeric overflow that you mention. I can imagine a software engineer eventually figuring out that overflow was a root cause (sometimes they will not). However there's quite a gap between overflow recognition and say knowledge of numerical analysis that will help guide a fix. You say > "literally every single example"... can be dealt without much math. I would be very keen to learn from you about how to deal with this one, say. Without much math. `The labelers labelled only the instances that are easy to label, not chosen uniformly from the data. How to train with such skewed label selection (without relabeling properly)` This is not a gotcha, a genuine curiosity here because it is always useful to understand a solution different from your own(mine).

▲ porridgeraisin 5 hours ago | parent | prev [-]

The real use is in actually seeing connections. Every field has their own maths and their own terminologies, their own assumptions for theorems, etc.

More often than not this is duplicated work (mathematically speaking) and there is a lot to be gained by sharing advances in either field by running it through a "translation". This has happened many times historically - a lot of the "we met at a cafe and worked it out on a napkin" inventions are exactly that.

Math proficiency helps a lot at that. The level of abstraction you deal with is naturally high.

Recently, the problem of actually knowing every field enough, just cursorily, to make connections is easier with AI. Modern LLMs do approximate retrieval and still need a planner + verifier, the mathematician can be that.

This is somewhat adjacent to what terry tao spoke about, and the setup is sort of what alpha evolve does.

You get that impression because such advances are high impact and rare (because they are difficult). Most advances come as a sequence of field-specific assumption, field-specific empirical observation, field-specific theorem, and so on. We only see the advances that are actually made, leading to an observation bias.