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AI Expert Roadmap

i.am.ai

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Re: AI Expert Roadmap

#31

Post by [deleted] »

pradn wrote: post #23
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 th…

[deleted]

Re: AI Expert Roadmap

#32

Post by amaigmbh »

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…

As for our opinion (which is just that, an opinion) why we think that the statistical foundations and knowledge about more traditional algorithms is important, it's based on the business needs and our experience. While it might seem less necessary if the goal is to "learn deep learning", it is highly relevant if your task is to "solve this business problem".

Our perspective is the industrial one. And while there are certainly many complex business problems where deep learning is required, there are more cases where a traditional approach is sufficient and actually the better solution (e.g. due to the memory footprint, latency or other reasons). We routinely work on both kinds of problems on a day to day basis, but we would never go straight to deep learning approaches if simpler and faster traditional methods comprise a better solution in a given use case. So, our employees are expected to know both and to be able to judge when to apply which approach.

Re: AI Expert Roadmap

#33

Post by YeGoblynQueenne »

At this point I feel like some crazy person talking to themselves when I say this, but "AI" is a research field that includes many more sub-fields than machine learning, let alone deep learning, and it's impossible for someone to be an "AI Expert" while ignoring this, even if they think they are because they ignore it. I appreciate that synecdoche is a thing, but to recommend a "roadmap" to becoming an "AI Expert in 2020" while ignoring most of AI is...

... well I don't know what it is, anymore. Sign of the times? Perhaps the field (AI that is) should abandon its name just to be sure that if a backlash ever comes against deep learning it won't take everyone else's reseach with it?

On the other hand, I feel a bit like the horses and the cows in Animal Farm, when the pigs took over the Revolution. Not that there was any revolution, not really, but the way that the research trends have shifted lately, from what would make good science to what will make you hired by Google, is a little bit of a shock to me. And I stared my PhD just three years ago. It's come to the point where I don't want to associate my research with "machine learning" and I don't want to use the term in my thesis, for fear of the negative connotations (of sleazy practices and shoddy research) that I am concerned might be attached to it in the time to come.

And it's such a shame because the people who really advanced the field, people like Joshua Bengio, Jurgen Schmidhuber, Geoff Hinton, Yan Le Cunn etc are formidable scientists, dedicated to their preferred approaches and with the patience to nurture their ideas against all opposition. The field they helped progress so much deserved better.

Re: AI Expert Roadmap

#34

Post by orange3xchicken »

realtalk_sp wrote: post #26
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…

Yikes! This doesn't really come across as a nice comment (bent minds???). There are good concrete reasons to pursue a PhD (ignoring soft reasons like pure interest): wanting a research career is one - it's pretty difficult to get hired as a scientist without a PhD. Also, historical evidence doesn't really support your claim that R&D is orthogonal to business value. Sure, pure science is often independent from $$$ (despite plenty of examples of producing real value), but applied R&D is oftentimes parallel to value generation. if R&D in general is useless, why do top tech companies spend big money on research groups?

Getting a PhD is a fine deal if you have good reasons.

Re: AI Expert Roadmap

#35

Post by arminiusreturns »

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 would say, however, there is ample opportunity for infrastructure designers/engineers in the space, especially in a world where everyone is going "cloud" but cloud doesn't cut it in the most demanding applications, so those of us who love data center design are sort of loving the resurgence of onprem/Colo usage and the opportunities for pushing the envelop on data transfer/processing rates, etc. The skills and knowledge gained are very useful in other industries, with a side bonus of, if you pay attention, knowing a bit more about the "big picture" of AI than many of the phd's!

Re: AI Expert Roadmap

#36

Post by marcinzm »

There's a split happening right now in the market between Data Scientist and Machine Learning Engineer roles. The former is generally a more soft-skills heavy role with results that are meant to drive human decisions. The later is about building systems that automatically make decisions (ie: recommendation systems, etc.). Data Science is over saturated while ML Engineering is less so.

Re: AI Expert Roadmap

#38

Post by nshm »

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…

Every time i fire a data scientist the earnings grow

Re: AI Expert Roadmap

#39

Post by Der_Einzige »

I chuckle a bit when they put PCA as being the very last thing you learn, way after dimensionality reduction (PCA is the classic and most "simple" dimensionality reduction technique)

Also, the reality is that very few full on AI experts (and yes, this means Ph.Ds with a lot of publications at top conferences who are also FAANG applied ML engineers) will have more than 70% of these skills. Luckily, this is a field which highly rewards specialization. They don't need the breadth if they have depth in their particular areas.

My experience has been that in the vast majority of applied roles involving AI, there is not enough emphasis on the software engineering skills necessary for productizing the models, leading to situations like baron_harkonnens critique.

Re: AI Expert Roadmap

#40

Post by cambalache »

6gvONxR4sf7o wrote: post #30
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.…

I totally agree with you, but let's be honest, in this publish-or-perish world in academy, and the cut-throat environment of VC-funded start-ups, people will, if not outright lie, exaggerate the nature and importance of what they are doing. Too much money and "prestige" is at stake.

The "right" way is slow, lonely and unglamorous. And most importantly without that fast money, acquisitions and promotions.

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