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Ask HN: How to Break into AI Engineering

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Re: Ask HN: How to Break into AI Engineering

#31

Post by markus_zhang »

I'm surprised that many recommend heavy Math, but I always thought AI engineer is sort of specialized data engineer for AI projects.

It's definitely a plus to know a bit about the Mathematics, but I doubt anything short of a Master of Science in Math with AI as specialization is going to close the gap. How many data engineers can do that?

Wouldn't it make a lot of sense to hire someone with zero AI exposure but tons of experience in sysadmin, data engineering and ops? It's going to be tough to find someone who are both an engineer and a Math wizard, I think.

Re: Ask HN: How to Break into AI Engineering

#33

Post by jahsome »

__rito__ wrote: post #21

- 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 eas…

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?

Re: Ask HN: How to Break into AI Engineering

#34

Post by StevenHarlow »

tikkun wrote: post #16

I wrote something a bit ago to answer this question: https://llm-utils.org/AI+Learning+Curation It was previously popular on HN though only inside a comment thread, and I haven't submitted it as a link post yet.

This looks like a very good resource, thank you. It also clarifies for me a bit more the domains within the field, which I wouldn't have been able to define in terms of prerequisites.

Re: Ask HN: How to Break into AI Engineering

#35

Post by aplummer »

jahsome wrote: post #33
__rito__ wrote: post #21

- 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 eas…

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 comp…

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.

Re: Ask HN: How to Break into AI Engineering

#36

Post by n4r9 »

jahsome wrote: post #33
__rito__ wrote: post #21

- 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 eas…

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 comp…

> 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!

Re: Ask HN: How to Break into AI Engineering

#37

Post by mehh »

hackernoteng wrote: post #30

Why? Do you like to make software that barely works? Do you like to make and sell software which is dishonest in its capabilities? In traditional software, 10% error rate is unacceptable - it can't be released. But hey 10% error rate in AI is good! No, it isn't.

You’re either missing the point, or you’ve seen too many demos of people using neural networks for the wrong problem.

Re: Ask HN: How to Break into AI Engineering

#39

Post by thatsadude »

AI Engineering is broad. There are a lot of things to learn and a lot of mistakes you have to made yourself.

For applied ML, my tips are: make sure you learn the dark side of BatchNorm and Dropout, start with simple and elegant baselines instead of complex SOTA algorithms, spend more time on understanding your data than trying algorithms, be aware that SOTA in a related task will often suck at your task, be data driven. Also, most of your ideas will not work but you have to try and conduct experiments carefully.

Re: Ask HN: How to Break into AI Engineering

#40

Post by itake »

tjpnz wrote: post #25
itake wrote: post #10

Earlier quoted context omitted.

I worry that even if self-taught, I wouldn't have the credentials (job experience or degree), to do a full-time ML job. Our my concerns unfounded?

ML is a relatively new field. How do you think current engineers got their start?

5 years ago, you needed a masters or phd to qualify for a Data Science role. At my current company, that is still the case.

Has that change?

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