I find myself completely outclassed by mathematicians in my own field. I tried to learn a little math on the side after my regular software engineer gig but I'm completely outclassed by phd's.

I am unsure of the next course of action or if software will survive another 5 years and how my career will look like in the future. Seems like I am engaged in the ice trade and they are about to invent the refrigerator.

▲ ecshafer 6 hours ago | parent | next [-]

IMO Computer Science doesn't have enough mathematics in the core curriculum. I think more CS students should be double majoring or minoring in Physics and/or Math. The skills you gain in analyzing problems and constructing models in Physics, finding truth/false values and analyzing problems in math, and the algorithmic skills in CS really compliment each other.

Instead of people "hacking" university education to make them purely fotm job training centers. The real hack would be something that really drills down at the fundamentals. CS, Math, Physics, and Philosophy to get an all around education in approaching problems from fundamentals I think would be the optimal school experience.

▲ dsign 4 hours ago | parent | prev | next [-]

> Seems like I am engaged in the ice trade and they are about to invent the refrigerator.

The way I like to look at it is that I'm engaged in the ice trade and they are about to invent everything else that will end mine and every other current trade. Which leaves me with two practical options: a) deep despair. b) to become a Jacks of all trades, master of none, but oftentimes better than a master of one. The Jacks can, for now, capitalize in the thing that the Machines currently lack, which is agency.

	▲	2 hours ago \| parent [-]
		[deleted]

▲ rsp1984 12 hours ago | parent | prev | next [-]

Don't despair. The key to becoming proficient in advanced subjects like this one is to first try to understand the fundamentals in plain language and pictures in your mind. Ignore the equations. Ask AI to explain the topic at hand at the most fundamental level.

Once the fundamental concepts are understood, what problem is being solved and where the key difficulties are, only then the equations will start to make sense. If you start out with the math, you're making your life unnecessarily hard.

Also, not universally true but directionally true as a rule of thumb, the more equations a text contains the less likely it is that the author itself has truly grasped the subject. People who really grasp a subject can usually explain it well in plain language.

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griffzhowl 10 hours ago | parent [-]

> People who really grasp a subject can usually explain it well in plain language.

That's very much a matter of style. An equation is often the plainest way of expressing something

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rsp1984 3 hours ago | parent [-]

The problem is that equations give the illusion of conciseness and brevity but in reality always heavily depend on context.

You give a physicist an equation of a completely unrelated field in mathematics and it will make zero sense to them because they lack the context. And vice versa. The only people who can readily read and understand your equations are those that already understand the subject and have learned all the context around the math.

Therefore it's pointless to try to start with the math when you're foreign to a field. It simply won't make any sense without the context.

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griffzhowl 2 hours ago | parent [-]

Of course, but everything depends on context. Stating a mathematical theorem in English will also make no sense to someone who's not acquainted with the field

	▲	rsp1984 an hour ago \| parent [-]
		You can start with plain language and work your way up towards the math. But it doesn't work the other way round.

▲ numbers_guy 11 hours ago | parent | prev | next [-]

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 8 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 8 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 7 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 5 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 3 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).

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p1esk an hour ago | parent [-]

Maybe I don’t understand this data labeling issue - are you talking about imbalanced classification dataset? Are hard classes under-represented or missing labels completely?

	▲	srean an hour ago \| parent [-]
		None of those (but they could be added to the mix to complicate matters). Consider the case that the labelers creates the labelled training set by cherry picking those examples that are easy to label. He labels many, but selects the items to label according to his preference. First question, is this even a problem. Yes, most likely. But why ? How to fix it ? When are such fixes even possible.

▲ porridgeraisin 6 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.

▲ AndrewKemendo 6 hours ago | parent | prev | next [-]

The big thing that made it all click for mathematics was that I stopped thinking about mathematics the way that it was taught to me and I started thinking about it the way that it naturally felt correct to me

So in my specific case I stopped thinking about mathematics as: how to interpret a sequence of symbols

But instead I decided to start thinking about it as “the symbols tell me about the multidimensional topological coordinate space that I need to inhabit

So now when I look at a equation (or whatever) my first step is “OK how do I turn this into a topology so that I can explore the toplogical space the way that a number would”

Kind of like if you were to extend Nagle’s “what it’s like to be a bat” but instead of being a bat you’re a number

▲ RA_Fisher 9 hours ago | parent | prev | next [-]

AI makes it easier to catch up. :)

▲ swimmingbrain 6 hours ago | parent | prev [-]

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