[martingoodson]

Most comments here are in one of two camps: 1) you don't need to know any of this stuff, you can make AI systems without this knowledge, or 2) you need this foundational knowledge to really understand what's going on.

Both perspectives are correct. The field is bifurcating into two different skill sets: ML engineer and ML scientist (or researcher).

It's great to have both types on a team. The scientists will be too slow; the engineers will bound ahead trying out various APIs and open-source models. But when they hit a roadblock or need to adapt an algorithm many engineers will stumble. They need an R&D mindset that is quite alien to many of them.

This is when an AI scientists become essential.

[braza]

> But when they hit a roadblock or need to adapt an algorithm many engineers will stumble.

My experience is the other way around.

People underestimate how powerful building systems is and how most of the problems worth solving are boring and require out-of-the-box techniques.

During the last decade, I was in some teams and I noticed the same pattern: The company has some extra budget and "believes" that their problem is exceptional.

Then goes and hires some PhDs Data scientists with some publications but only know R and are fresh from some Python bootcamps.

After 3 months, or this new team no much was done, tons of Jupyter notebooks around but no code in production, and some of them did not even have an environment to do experimentation.

The business problem is still not solved. The company realizes that having a lot of Data Scientists not not so many Data/ML Enginers means that they are (a) blocked to do pushing something to production or (b) are creating a death star of data pipelines + algorithms + infra (spending 70% more of resources due to lack of knowledge on Python).

The project gets delayed. Some people become impatient.

Now you have a solid USD 2.5 million/year team that is not capable of delivering a proof of concept due to the fact that people cannot do the serving via Batch or via REST API.

The company lost momentum, competitors moved fast. They released an imperfect solution, but a solution ahead, and they have users on it and they are enhancing.

Frustration kicks in, and PMs and Eng Managers fight about accountability. VP of Product and Engineering wants heads in a silver plate.

Some PhDs get fired and go to be teachers in some local university.

Fin.

[gardenhedge]

Would you see these as analogous?

The people who create the models and the people that use them.

The people who create the programming languages and the people that use them.

[martingoodson]

I think because it's a relatively 'younger' field, there is a bit more need to know about the foundations in AI than in programming. You hit the perimeters a bit more often and need to do a bit of research to modify or create a model.

Whereas it's unlikely in most programming jobs you would need to do any research into programming language design.

[Breza]

I agree with you. Being a corporate department head, I've led exactly one project that's had me digging through my DS&A textbook. But it's much more common to need to go beyond the limits of an off-the-shelf deep learning algorithm. Plus many of the cutting edge deep learning advancements have been fairly simple to implement but required serious effort to create, and being able to understand an Arxiv paper can have a direct impact on the job you're currently working on, whereas being able to read all of TAOCP will make you a better coder, but in a more abstract way.

[3abiton]

This sounds like a sell-pitch for an AI scientist.

[rising-sky]

This sounds like a dont-buy-pitch for an AI engineer...

The point the commenter is making is that both schools of thought in the comments are valuable and unless you perform both roles, i.e. an engineer who is familiar with the scientific foundations, both are symbiotic and not in contention.

[mi_lk]

I guess this message is delivered by an AI scientist, sure.

It's almost self-exploratory that when you hit a roadblock in practice you go back to foundations, and good people should aim to do both. In that case I don't see where ML engineer/scientist bifurcation comes from except for some to feel good about themselves

[martingoodson]

Not at all. It's something I've seen in practice over many years. Neither skill set is 'better' than the other, just different.

There is a need for people who are able to build using available tools, but who don't have an interest in the theory or foundations of the field. It's a valuable mindset and nothing in my original comment suggested otherwise.

It's also pretty clear that many comments on this post divide into the two mindsets I've described.

[johnthescott]

as a friend from at&t dallas told me, tis cheaper to turn a mathematician into a programmer than a programmer into a mathematician.