AI Expert Roadmap • Hacker News Board

Re: AI Expert Roadmap

#21

Post by baron_harkonnen » Fri, Nov 06, 2020, 8:38 PM UTC

I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible).

The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have reduced years studying their field into a flurry of buzzwords on a resume that they don't understand.

The vast majority of AI/ML/DS teams don't know what they're doing. Management and "leadership" in this industry would be laughable if it wasn't so horrific. The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand and making sure someone else is using that so they can justify their work. You could likely cut entire 100+ DS teams at most companies today and only see a positive impact on the business.

There are of course really interesting niches that require these skills and skilled practitioners. But if you're reading a guide on how to be an "AI Expert", they you likely aren't one of these people. And because of the signal noise problem in this space it's hard for even really excellent people to get matched to the hard problems that genuinely need their help.

Re: AI Expert Roadmap

#22

Post by rexreed » Fri, Nov 06, 2020, 8:42 PM UTC

If you are looking to work in an enterprise as a data engineer, submission of research papers with code is certainly not a pre-requisite. Learning something useful like CPMAI methodology is more practical.

Re: AI Expert Roadmap

#23

Post by pradn » Fri, Nov 06, 2020, 8:44 PM UTC

baron_harkonnen wrote: post #21

I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible). The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have…

I wonder what the impact of easy-to-use cloud ML tools is going to be on the industry. BigQuery already lets users train models using a simple SQL query. AutoML makes simple ML tasks as easy as run and verify accuracy and run again. Of course, a basic amount of knowledge is still need to avoid common pitfalls - unclean data, overfitting, etc. Perhaps this will just mean data scientists can produce better work with the same amount of time. It doesn't seem like a good use of everyone's time to have to know so much cutting-edge research to solve run-of-the-mill business problems. Hardly any other area of CS requires that.

Re: AI Expert Roadmap

#24

Post by wunderwuzzi23 » Fri, Nov 06, 2020, 8:45 PM UTC

tnbalsam wrote: post #4

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would…

I agree with your suggestion, and that is what I did to get started in machine learning as well. Solving some kind of problem and doing practical projects and playing around with models is probably the most efficient way to learn. I started with the Machine Learning course on Coursera by Andrew Ng. And while going through, I started using what I learned on a small project. I did document my journey in case it's useful or interesting for others: https://embracethered.com/blog/posts/2020/machine-learning-b...

Re: AI Expert Roadmap

#25

Post by SpaceManNabs » Fri, Nov 06, 2020, 8:47 PM UTC

This is great in some aspects but really shallow in others.

In fundamentals, I don't see a reference to geometry or calculus. Good luck understanding methods like UMap or more modern clustering techniques, optimal transport methods in AI, and more recent gradient techniques without those.

Some might say this is more for the AI researcher, but people need to implement bleeding edge stuff all the time.

Re: AI Expert Roadmap

#26

Post by realtalk_sp » Fri, Nov 06, 2020, 8:51 PM UTC

nmrcq wrote: post #15

With all due to respect to the author of the site, mastering all the materials in the machine learning or data scientist path will make you a solid 'applied' machine learning/statistical learning practitioner, but not an expert, and definitely not a research candidate. We should be careful with how we guard our scale of semantic meaning - if somebody with an undergrad understanding of statistics (frequentist statisti…

Using a PhD as a gateway into applied ML is so horrifically misguided I hardly know where to even begin debunking it.

PhDs are one especially crappy way to prove you have the intellectual chops to engage with ML. There are far more direct, practical, and expedient alternative paths to get there.

Importantly, the number of people who were perfectly capable of doing a PhD but chose not to (because, frankly, it's a very bad deal) vastly dwarfs the number of people who stuck around in academia and obtained one. Additionally, my observation at several major tech companies is that PhDs have a bent of mind that is roughly orthogonal to the pursuit of real business value.

Re: AI Expert Roadmap

#27

Post by i_love_music » Fri, Nov 06, 2020, 9:08 PM UTC

nairboon wrote: post #14

Why is "Big Data Engineer" not a child node of "Data Engineer"?

Yeah that seems like a major flaw in this taxonomy. Like does the author really think there is significant enough difference between 'data' and 'big data' that it warrants an entirely separate track? That's silly.

Re: AI Expert Roadmap

#28

Post by i_love_music » Fri, Nov 06, 2020, 9:14 PM UTC

tnbalsam wrote: post #4

Perhaps I may be mistaken, but this seems to be a very long road for a more shallow understanding of deep learning. I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation; however, I'd venture that that is a rather inefficient way to get to deep learning proficiency. If I were to give a recommendation, it would…

"I'd venture this was written by someone who has a more traditional machine learning background that wants new people to the industry to have that same foundation"

Yeah I get the same feeling. I am hesitant to suggest it, but gives a little bit of a gate-keeping vibe. Like only once you've payed sufficient homage to the same education path that I took, shall you be granted permission to be a 'real data scientist'.

That said, I think there is value in something like this more for hiring managers than practitioners... which makes sense considering the intro comments.

Re: AI Expert Roadmap

#29

Post by rsp1984 » Fri, Nov 06, 2020, 9:22 PM UTC

I really appreciate the taxonomy and links given there, but whenever I see someone claiming "you can't do Z before you've mastered X and Y" it tells me that someone's afraid of others succeeding with simply skipping the masterhood of X and Y and going straight to Z, picking up just enough X and Y in the process.

In fact, since the direct path is more rocky than going via X and Y, it naturally selects the most motivated people, so those succeeding in it will generally outperform those who went X-Y-Z.

Re: AI Expert Roadmap

#30

Post by 6gvONxR4sf7o » Fri, Nov 06, 2020, 9:32 PM UTC

baron_harkonnen wrote: post #21

I would not advise anyone to go down the "data science" career path at this point, unless one of these topics is a true passion of yours and you can't imagine doing anything else (and even then I would recommend alternatives if possible). The explosion of AI/ML/Data Science teams in places that they really don't belong is going to have major backlash soon. The market is currently flooded with desperate PhDs who have…

For readers who read that and think they still want to do it:

> The vision of AI/ML/DS has turned into a nightmare of people shoving data they don't understand into models they don't understand...

Don't be this ^. Please.

If you do want to come into the field, please first take the time to understand the tools. Learn enough to re-derive and re-implement the tools you're using. Learn the assumptions that need to hold. Don't just learn to point the tools at data and hit go. It's not like a lot of programming where it either succeeds or fails. Failures are often silent in this field. If you can't reason through how and when a given tool will succeed and, probably more importantly, reason through how and when a given tool will fail, you haven't learned enough to know what to use and when.