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- Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really sol

- Have crystal clear Mathematical foundations, as in why this formula/method the way it is, rather than being able to solve college/HS test problems. Really solid footing in Differential Calculus and Linear Algebra is necessary. - Know the Statistical language that you learn from a basic college-level Stat 101 course. Be able to translate normal sentences into those using Statistical notation, and be able to read easily. Also, know basic Statistics. - You already know programming, I assume. Learn Python if you don't know already. It's really easy. - There are a number of paths you can go from there. Here's what I did. -- IBM Data Science Professional Certificate (not deep at all, but lays out the landscape well; did it in a week) -- Machine Learning for Absolute Beginners by Oliver Theobald which you can finish in an evening. -- Machine Learning Specialization by Andrew Ng on Coursera. -- Deep Learning Specialization by Andrew Ng on Coursera. -- fast.ai course. - Learn PyTorch really well. I suggest Sebastian Raschka's book. Now from here, you can chart your own path. You can choose NLProc, Vision, RL, or something else. I went towards Vision. And I do Edge AI as hobby. I was in the last year of college as a Physics undergrad, when I was hired to do Vision modelling/research for a non-flashy company in 2021. Finishing my CS Master's next month and starting to look for PhD. I worked in the same company for the ~2.5 years. EDIT: If you want a job in big tech, grind Leetcode, and learn about system design, study Machine Learning systems, and be able to design them. Chip Huyen has a good book as I hear. 6-7 rounds of interview is common in Meta/Google. DL hackathon awards, open source contributions are significantly helpful.

jahsome 3y ago
Highjacking for self-edification as you led with math... Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren't working directly on the AI systems themselves? I ask because I'm quite math averse (dyscalulia). I am self-taught engineer, largely because it became glaringly obvious I wasn't going to be able to complete a CS program, so I pivoted my educational focus. I still manage a relatively successful and effective career in software. I love the engineering aspects, e.g. programming, system design, problem solving, pattern recognition, etc. When it comes down to it, I couldn't calculate my way out of a wet paper sack. Are my days (pun intended) numbered?
- aplummer 3y ago
  Have you tried recently? I was one of these people in high school but now in my 30s, stuff that seemed impossible no longer does. Maybe me or the context is different.
  - BossingAround 3y ago
    What did you try? I was the same, also in my 30s, did some online stat and calc classes, passed those classes, and I don't think I learned much. If I did, I've already forgotten it I'm afraid.
  - jahsome 3y ago
    I have. I am disappointingly crippled by dyslexia. It takes a tremendous amount of energy for me to work with numbers and formulae which is one of the reasons I was drawn to programming. Once I realized I could abstract away the numbers and only really needed to get the calculation right once before putting it into a function, it was like jet fuel.
- n4r9 3y ago
  > Are my days numbered? Only in the sense that ultimately everyone's days are numbered. LLMs can suggest bits of code but there's no reason to believe that it can undertake large-scale, novel software engineering projects. In fact the lack of an underlying mental model makes me doubt that the LLM paradigm will ever get to that point. For the forseeable future I think there will always be a role for the person who can turn around and say "I see what you're getting at, but it just doesn't fit conceptually with the system. What are you actually trying to achieve? What if we did it this way instead?". I could well be wrong!
  - __rito__ 3y ago
    Copilot can absolutely do the jobs of Data Scientists who got hired based on some minimal skills that were considered valuable even three years back. I have tried applying Copilot to my own work where it spews- garbage. But I use Copilot a lot to generate some example data, or write repetitive code- I needed a basic Flask frontend and I used Copilot to do 70% of the job. But it cannot yet solve Vision tasks. > " I think there will always be a role for the person who can turn around and say" These roles, if and when they start to exist, will exist in extremely tiny numbers. And they will all likely be hired from Stanford/Oxbridge/MIT/Caltech, and they will have PhDs. If this scenario plays out, then, I am afraid for my future, too.
maypop 3y ago
Great post - thank you. What directions can you head in AI Eng these days if you _dont_ want big tech?

jahsome 3y ago

Highjacking for self-edification as you led with math... Is math going to become more important for software engineers in general, as AI is adopted in to more and more aspects of the SDLC, even if said engineers aren't working directly on the AI systems themselves? I ask because I'm quite math averse (dyscalulia). I am self-taught engineer, largely because it became glaringly obvious I wasn't going to be able to complete a CS program, so I pivoted my educational focus. I still manage a relatively successful and effective career in software. I love the engineering aspects, e.g. programming, system design, problem solving, pattern recognition, etc. When it comes down to it, I couldn't calculate my way out of a wet paper sack. Are my days (pun intended) numbered?

aplummer 3y ago
Have you tried recently? I was one of these people in high school but now in my 30s, stuff that seemed impossible no longer does. Maybe me or the context is different.
- BossingAround 3y ago
  What did you try? I was the same, also in my 30s, did some online stat and calc classes, passed those classes, and I don't think I learned much. If I did, I've already forgotten it I'm afraid.
- jahsome 3y ago
  I have. I am disappointingly crippled by dyslexia. It takes a tremendous amount of energy for me to work with numbers and formulae which is one of the reasons I was drawn to programming. Once I realized I could abstract away the numbers and only really needed to get the calculation right once before putting it into a function, it was like jet fuel.
n4r9 3y ago
> Are my days numbered? Only in the sense that ultimately everyone's days are numbered. LLMs can suggest bits of code but there's no reason to believe that it can undertake large-scale, novel software engineering projects. In fact the lack of an underlying mental model makes me doubt that the LLM paradigm will ever get to that point. For the forseeable future I think there will always be a role for the person who can turn around and say "I see what you're getting at, but it just doesn't fit conceptually with the system. What are you actually trying to achieve? What if we did it this way instead?". I could well be wrong!
- __rito__ 3y ago
  Copilot can absolutely do the jobs of Data Scientists who got hired based on some minimal skills that were considered valuable even three years back. I have tried applying Copilot to my own work where it spews- garbage. But I use Copilot a lot to generate some example data, or write repetitive code- I needed a basic Flask frontend and I used Copilot to do 70% of the job. But it cannot yet solve Vision tasks. > " I think there will always be a role for the person who can turn around and say" These roles, if and when they start to exist, will exist in extremely tiny numbers. And they will all likely be hired from Stanford/Oxbridge/MIT/Caltech, and they will have PhDs. If this scenario plays out, then, I am afraid for my future, too.

maypop 3y ago

Great post - thank you. What directions can you head in AI Eng these days if you _dont_ want big tech?