Ask Slashdot: What Types of Jobs Are Opening Up In the New Field of AI?

Qbertino writes: I'm about to move on in my career after having a "short rethink and regroup break" and was for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something. But I have the seriously growing suspicion that artificial intelligence is coming for us programmers and IT experts faster than we might want to admit. Just last weekend I heard myself saying to a friend who was a pioneer on the web, "AI is today what the web was in 1993" -- I think that to be very true. So just 20 minutes ago I started thinking and wondering about what types of jobs there are in AI. Is anything popping up in the industry from the AI hype and what are these positions called, what do they precisely do and what are the skills needed to do them? I suspect something like an "AI Architect" for planning AI setups and clearly defining the boundaries of what the AI is supposed to do and explore. Then I presume the requirements for something like an "AI Maintainer" and/or "AI Trainer," which would probably resemble something like an admin of a big data storage, looking at statistics and making educated decisions on which "AI Training Paths" the AI should continue to explore to gain the skill required and deciding when the "AI" is ready to be let go on to the task. You're seeing we -- AFAIK -- don't even have names for these positions yet, but I suspect, just as in the internet/web boom 20 years ago, that is about to change *very* fast.

And what about Tensor Flow? Should I toy around with it or are we past that stage already and will others do AI setup and installation better than me before I know how this thing really works? Because I also suspect most of the AI work for humans will closely be tied to services and providers such as Google. You know, renting "AI" as you rent webspace or subscribe to bandwidth today. Any services and industry vendors I should look into -- besides the obvious Google that is? In a nutshell, what work is there in the field of AI that can be done and how do I move into that? Like now. And what should I maybe get a degree in if I want to be on top of this AI thing? And how would you go about gaining skill and knowledge on AI today, and I mean literally, today. I know, tons of questions but insightful advice is requested from an educated slashdot crowd. And I bet I'm not the only one interested in this topic. Thanks.

Comments

AI / ML is not a hobby

[jmcbain]

for quite some time now thinking about getting into perhaps a new programming language and technology, like NodeJS or Java/Kotlin or something

You are in for disappointment if you think that getting into a career in AI / ML is anything like a programming hobby, such as picking up NodeJS or Java over a few weekends. First, let me make clear the terminology I'm using. Artificial Intelligence is a broad field. Although the public perception of AI is of software/robots like HAL with which humans can talk or interact, the field also encompasses knowledge representation, reasoning, and learning. Machine learning is then an important subfield of AI, and it involves supervised learning, unsupervised learning, reinforcement learning, etc. Over the last several years, many people have started to conflate AI and ML, but for someone knowledgable in these fields, the distinction is clear. ML is the practical application of algorithms towards taking inputs and producing an output prediction, and it's this area that contains the vast majority of jobs in "AI". Some basic applications of ML include spam filtering, face detection and recognition, product recommendations, fraud detection, revenue forecasting, gait and step detection, voice recognition, etc. If you look at that list and think about them, you'll come to realize that you've probably been consuming ML results for the last five years or much longer. If you want to work as a "ML engineer" in this area, you'll have to be knowledgable with ML algorithms, setting up data pipelines, running experimentation, and using ML software, such as scikit-learn, R, Caffe, Tensorflow, etc.

I manage a ML team at a large company. Let me make clear: Unless you have a strong academic background in this field, no one will take you seriously. I recently applied to be a principal engineer working on an AI/ML personal assistant, and the recruiter told me straightforwardly that the hiring managers are not interviewing anyone unless they have a recent PhD related to deep learning. I'm a bit elitist about this as well: I tend to turn away candidates that don't have at least an MS or PhD in a field related to ML. Why is this so? Because you need a rigorous background to understand why and how ML works, and this involves understanding loss functions, gradients, training vs. validation error, decision boundaries, optimization, and other things. You need to understand these things because a lot of current ML involves choosing the right knob settings (hyperparameters) that make your ML work best. If your ML algorithm isn't working well, how do you fix it? That's where this rigorous background comes in handy.

Now, there are many things related to ML that you can still work on if you don't have a strong background. As opposed to a ML specialist, there are plenty of positions related to data engineering (e.g. setting up and maintaining huge data pipelines), infrastructure administration (e.g. installing and mastering all aspects of Hadoop and Spark), visualization (e.g. creating dashboards that take fresh data and display it), among many others.

Re:There is no 'AI'

[Anonymous Coward]

Self-driving cars are powered by artificial neural networks, which rely on multiplication/convolution of 16 bit floating point matrices. The decision tree / expert system stuff you're talking about with "if statements" is ancient. Nobody is doing image segmentation, object detection, or object recognition with that.