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

Worked in the AI field for 45 years...

[Anonymous Coward]

and AI never works since anything that works is redefined as not AI. Sucks that I've worked my ass off for four different companies, and the last one is Microsoft, where what I do is considered amazing before it works then not appreciated after it works.

Currently, I think AI will only "succeed" when it can make it's own arguments as to why it works.

Re:There is no 'AI'

[Anonymous Coward]

Like 'self-driving' cars AI is a scam and my eyes glaze over when I hear anyone mentioning it. It's usually a clueless investor or someone with something to peddle. All we have are systems with an ever increasing number of if statements. There is no true AI and won't be for a very long time.

If you think self-driving cars are a scam, then yes, AI is definitely a scam. Maybe you should wait a year or so and re-evaluate.
You can't ignore that computers can now beat us at chess, Go, or pretty much any game based on strategy.
Applications such as face-recognition, speech-recognition are already used in every-day life, and are increasingly based on deep-nets.
I'm curious to know how many if statements are in the code?

look ma, no code!

[epine]

AI gets to the point were it solves a set of previously unsolvable problems, the algorithms are then researched and better non-AI solutions are then used to solve the same problems. Then AI falls out of fashion for a while and computer power increases thanks to Moore's law. Then it all repeats.

This old story is such a crock.

I highly recommend the following to anyone who wants a different perspective on modern ML:

* Talking Machines: Remembering David MacKay with Philipp Hennig — 21 April 2016

* Probabilistic-Numerics.org

This is plain old numerical methods, optimization, and search viewed through a Bayesian inference filter. I would never have termed any of this "artificial intelligence".

It took the recent large advances in unsupervised learning, the kitty classifier (and progeny), and the LSTM machine translation models to finally justify rethinking academic labels. Programs like SHRDLU from 1968 were perhaps explorations in AI, if our baby-step microscope is especially well focused. But this was closer to natural philosophy than what later became physics. Even our shiny new LSTM language models remain weirdly proximal to Searle's Chinese room. What have we really learned from watching our machines learn? Not a whole damn lot.

I'd nominate a term such as I-cubed: inexplicable inductive inference, or perhaps MIII: massively inexplicable inductive inference.

Even so impeded with an appropriate name, MIII is pretty mind-blowing. But it still ain't AI. It might be a viable building block to proceed in that direction, sooner rather than later, as we begin to erect dynamical systems upon this foundation. To drive the point home, it remains way overblown to call it MIIR: massive inexplicable inductive reasoning.

An Alberta AlphaGo Pioneer Is in China to Watch the AI Wallop Human Opponents

"Before AlphaGo, much of the fundamental games and machine learning research was done here," Muller wrote in an email. "If you look through the references list of the AlphaGo paper in the journal Nature, over 40% of these references have a University of Alberta (co-)author. Then, DeepMind greatly surpassed all of these previous efforts with their new ideas."

I haven't waded through this yet, but I suspect even the vaunted AlphaGo has a backbone of techniques that I personally wouldn't have classed as "AI" (or even AI-ish) by my own standards.

For decades, the big idea in AI was supposed to be recursion. Perhaps human language is recursive in theory, but it's only barely recursive in practice (nest more than three levels, your accurately attentive audience grows thin). Winograd is not completely wrong about this, but my long suspicion is that recursion is not going to enter the AI building through the ground floor.

Lately, we really have hit home runs with distributed representation, and to a lesser degree with convolutional image recognition. These are actual AI-ish ideas. However, two solid take-home techniques do not a field make.

Here's another possible intermediate term: generalized gradient exploitation (GGE). Plus there's tons of great mathematics about overfitting and regularization. But should we really call all this math "AI"?

In practice what AI ends up meaning is "look ma, no code!" Hey, we just built an impressive system without hiring rooms full of code monkeys, so we must be doing something right.

AI is not the moving target of lore. It's mainly our long AI pretension that fits the bill.

How many legs d