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Comment by master_yoda_1 | Hacker News Reader

I disagree that for becoming an expert you don't need PhD., or some kind of 3-4 years focused work in a particular topic in AI.

But the question is are all Ph.D equal, in my view many students fraudulently awarded Ph.D without enough rigorous work, earlier those won't get hired, but because of AI hype they get hired. Also many old PhDs brand them-self as AI expert even though they don't know much.

Hiring is still runs on hype and there are many bias (including gender bias) exists in industry.(e.g. Facebook's Mark does not like to hire 30+ people)

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I don't think I really disagree with you with respect to becoming an expert, but what you think of as becoming an expert may be different from what others are thinking.

Personally, what I see is a lot of AI becoming commoditized. There was a time when you had to have a fairly strong understanding of compilers to program anything complicated. These days you can use a high-level programming language and never get down to the level of the compiler if you don't want to.

If someone wants to make lots of money or change the world with AI, my advice would not necessarily be to start with a PhD. It would be to focus on understanding data, getting very good with a library, and building apps that are useful to people.

If you do that, then getting additional knowledge about the mathematics furthers your career, but your career doesn't block on acquiring that knowledge.

If you do this, you probably won't have a great of chance of working on a team at Google or Facebook improving the implementation of the AI infrastructure. Just as you probably wouldn't get a job at one of these companies optimizing the compiler if you didn't have an academic background or years of experience in compilers. But you could still work on other teams in those companies and make as much or more than those people and have a more direct impact than just making it marginally faster.

I have a PhD in pure mathematics, and I don't do ML. Getting a PhD is a particular journey, and it's not for everyone. Also, the idea of getting a PhD as a credential for industry seems a little odd to me, but that may be my personal bias.

Branching off of what you were saying, being an ML expert requires a high level of math typically only seen in academia, aka PhD required. Being an expert in using ML requires you to be an expert in your own field, and understanding your specific problem. Whether that requires a PhD is dependent on the field, but at the PhD level you end up collecting and analyzing significant amounts of data, and through that, understanding how to apply ML to that type of data, if applicable.

PhD for industry is useful for research and development positions in a variety of fields. Just like companies see bachelor's degrees as proof you can stick with something for 4 years and complete it, PhD degrees are proof you can develop and implement research procedures on a long time scale.

This article isn't about becoming an AI expert. It's about learning enough AI to be able to build applications that help the world.

Having taken Fast.ai's Deep Learning course, I can confidently say that their course is enough to help a software engineer with no previous AI experience build extremely powerful real-world AI applications.

Jeremy Howard (the cofounder of Fast.ai, the former president of Kaggle, and the former #1 Kaggle competitor) only has a B.A. in Philosophy. One student who started the Fast.ai course as a violinist is now working as a researcher at Google Brain. It may have been true in the past that you need many years of work to become competent at building AI, but that's not true anymore.

I think there’s a difference between mechanical sympathy and really knowing what’s going on mathematically.

IMO, parent is right. It’s going the way computing did. Ie most degree programmes basically teach you how to programme. Many degrees don’t even require maths anymore.

Hype drives demand drives hiring ,... and always at the top a bunch of guys that don’t know anything about ML at all :)

Yes, but couldn't the same be said about many aspects of software engineering. If you want to optimize a database query of some application code, yes you need to understand what's going on under the hood, but to produce something useful you only need a fraction of that knowledge.

Yes that quite right IMO. Software has been a craft for ages. And I don’t mean it disparagingly, but the number of devs that can think from first principles, know the difference between computing and programming or get “close to the metal”, is relatively small.

I think the same is true of marketing. The number of marketers that are also good researchers is tiny.

And so on.

The common denominator is short supply, high demand pressure, and in all I’m thankful for it because it keeps the wolf from the door for many of us.

But.. it leaves the deeper science/art (whatever yours be) untapped , and it adds so much noise to the market that gold becomes difficult to sell for peanuts.

Software seems to encourage shallow knowledge these days, with a constant churn of new frameworks / databases or whatever the latest trend is.

Why not less focused work over more years? The argument makes zero sense to me.

Of course a lot of study and also applying what you learned are important. But having a PhD says very little about a person's ability to do data science or machine learning work. Especially since most phds have an extreme focus on their narrow field.

Deep learning resurgence is hardly 4-5 year old. Also to become an expert you need more focused work.

As a PL PhD, many of my peers have gone into ML. They actually have competence in it, it turns out many phds are just smart and curious (eg people like Jeff Dean who has a PL background also).

I haven’t gotten into it myself, but that is more of an interest issue.

"it turns out many phds are just smart and curious" So does non-PhD my friend :)

Yep. Having a PhD is just one indicator.

i Disagree and there is no data to support this

What are you disagreeing with? Having a PhD is an indicator that you at least got a PhD. Better if it’s a good school or they know your advisor. You can’t take it for much more than that, but it isn’t an empty achievement either.

Also, there is no data for a lot of things.

It takes a special kind of something to think that there's no correlation between intelligence and having a PhD.

Just to confirm, PL means “programming language”?

Yes. Includes those who work on compilers, formal verification, functional languages, OO languages, and so on. Not the hottest topic these days (compared to ML).

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