9 ms·
> The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hat
by achompas 9y ago
> The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry.
It is absolutely true that you do not need a graduate degree to apply AI/ML to vanilla problems.
It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem.
That said, I think these MOOCs are good enough to get someone to a place where they can create nice /r/dataisbeautiful-style visualizations, or pair with a senior-level DS to deliver something.
(Edited to add folks who have worked on problems for years and add a final note.)
- bluetwo 9y agoI can't help but think in 3-5 years how quaint our tools of the day will seem.
- achompas 9y agoI think about this constantly. Not to sound like I walked uphill in both directions back in my day or something, but I remember building models in numpy without pandas. It was tedious -- and that's just a nice API wrapping ndarrays!
- bhrgunatha 9y ago> Not to sound like I walked uphill in both directions back in my day Local minima?
- YeGoblynQueenne 9y agoMost likely, gradient descent with momentum.
- farazbabar 9y agoOh boy, that and perturbation.
- jimbokun 9y agoI’m not so sure. You can make an argument current tools haven’t really surpassed a Lisp Machine for developer productivity, or a SmallTalk environment.
- noobermin 9y agoNot really. I see things like leftpad and npm fails and CEOs mailing private keys they've stored from customers. I see the same lessons we have have to re-learn year after year.
- lugg 9y agoWhat's an example of a problem that needs that troubleshooting? (Curious)
- RealCasually 9y agoFairness and fighting adversarial examples come to mind.
- achompas 9y agoHonestly? The exact problem I'm dealing with at work right now. We're trying to re-write our recommender for artist music stations at iHeartRadio (aka "I'll listen to Drake or Kendrick Lamar's station at the gym today"). Just today, I tried adding negative sampling to the matrix I'm factorizing, hoping it encourages spread in the embeddings learned for artists in certain types of genres. I have a MS, but not a lot of research experience. It would have taken me a while to find this solution on my own. However, the moment I described this problem to my manager - a PhD graduate with several years of research and industry experience - he immediately suggested negative sampling. What I learned during my MS helped me grok the math immediately. We're adding noise to the training set and penalizing vectors lengths to avoid overfitting. Easy! Identifying a solution worth exploring? Not easy, at least without a degree or significant experience. (There's also the chance I should know this, in which case I have some reading to do. ¯\_(ツ)_/¯)
- Retric 9y agoThat has little to do with a PhD, it's the kind of thing you get with experience leading to a deeper understanding. 3D programming started as a field where only PHD's had any deep understanding of what was going on simply because they had experience when nobody else did. You see this pattern repeated frequently, in any complex domain.
- achompas 9y agoYeah, I expected this reply. The PhD is sufficient but not necessary here, right? A PhD researcher's job description is basically "learn necessary math, become a domain expert, and publish papers advancing that domain." It's difficult (but possible) to gain the same experience in industry if you don't have a graduate degree. Which company would pay you to work through Bishop or Goodfellow for a few months? Even a principal DS doesn't get that deal, much less a junior/associate. Also remember: my comment addressed non-vanilla cases. In your example, this is the difference between a researcher advancing 3D programming and someone using Unity or Unreal. (Also, sorry for all the edits. Done now!)
- gchadwick 9y ago> It is also absolutely true, in my experience, that you need a graduate-level education or years of hands-on experience to troubleshoot cases where AI/ML fails on a deceptively-simple problem, or to tweak an AI/ML algorithm (or develop a new one) so it can solve a novel problem. How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though? Certainly the person who does have the masters/PhD and a few years of applying that to real-world ML problems will have the edge but an experienced developer who's got a knack for maths (though no direct ML experience) may be able to get up to speed quicker than you think. Part of that will be experience with knowing how and when to ask the right questions when you get stuck.
- infinite8s 9y agoThe knack for maths is the important bit.
- sannee 9y agoThe math necessary for ML/AI (statistics/vector calculus) is mostly taught at undergrad level though isn't it? So most engineers should already have it covered.
- achompas 9y ago> How much of that is critical domain specific knowledge and how much of that is just general engineering debugging/problem solving experience though? It's both, right? You pick up problem-solving techniques as a researcher or engineer; as the former, those techniques lean towards scientific problems. Your average engineer doesn't need to know about contrasting. Again: it's possible to learn the necessary math in your spare time! I agree!! However, it's far easier to do it in a graduate program as a full-time job for 2-5+ years.