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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

by lain98 6mo ago

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.

rsp1984 6mo ago
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.
- griffzhowl 6mo ago
  > 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
  - rsp1984 6mo ago
    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.
    - griffzhowl 6mo ago
      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 6mo ago
        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 6mo ago
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 6mo ago
  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 6mo ago
    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 6mo ago
      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.
RA_Fisher 6mo ago
AI makes it easier to catch up. :)
ecshafer 6mo ago
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.
swimmingbrain 6mo ago
[dead]
AndrewKemendo 6mo ago
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
dsign 6mo ago
> 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.
- deleted 6mo ago
  [deleted]

by lain98 6mo ago

rsp1984 6mo ago

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.

griffzhowl 6mo ago
> 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
- rsp1984 6mo ago
  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.
  - griffzhowl 6mo ago
    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 6mo ago
      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 6mo ago

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 6mo ago
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 6mo ago
  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 6mo ago
    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.

RA_Fisher 6mo ago

AI makes it easier to catch up. :)

ecshafer 6mo ago

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.

swimmingbrain 6mo ago

[dead]

AndrewKemendo 6mo ago

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

dsign 6mo ago

> 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.

deleted 6mo ago
[deleted]