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AI Programming IT

Ask Slashdot: How Can Programmers Move Into AI Jobs? 121

Posted by EditorDavid from the opening-the-pod-bay-doors dept.
"I have the seriously growing suspicion that AI is coming for us programmers and IT experts faster than we might want to admit," writes long-time Slashdot reader Qbertino. So he's contemplating a career change -- and wondering what AI work is out there now, and how can he move into it? Is anything popping up in the industry and 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", 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... 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...?
Is there a degree program, or other paths to skill and knowledge, for a programmer who's convinced that "AI is today what the web was in 1993"? And if AI of the future ends up tied to specific providers -- AI as a service -- then are there specific vendors he should be focusing on (besides Google?) Leave your best suggestions in the comments. How can programmers move into AI jobs?
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Ask Slashdot: How Can Programmers Move Into AI Jobs? More

Ask Slashdot: How Can Programmers Move Into AI Jobs?

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  • deja vu (Score:4, Informative)

    by religionofpeas ( 4511805 ) on Sunday June 11, 2017 @12:45PM (#54596587)

    https://ask.slashdot.org/story... [slashdot.org]

    • Re:deja vu (Score:5, Informative)

      by ShanghaiBill ( 739463 ) on Sunday June 11, 2017 @01:18PM (#54596771)

      Yes, this is a dupe. Here is a brief synopsis of the previous discussion:
      1. Many people do not think AI today is analogous to the "web" in 1993.
      2. Machine learning is much harder than editing HTML. You aren't going to learn it in a 21 day "bootcamp".
      3. If you are serious this is what you should do:
        a. Learn plenty of linear algebra
        b. Learn how to program GPUs using CUDA and OpenCL.
        c. Learn basic theory, like backprop and autoencoders.
        d. Write some code, read some books, write more code.

      Here are some good resources:
      MIT Artificial Intelligence Course [youtube.com]
      Deep Learning by Ian Goodfellow and Yoshua Begino [amazon.com]
      Geoffrey Hinton's 2006 Science Paper [toronto.edu] that triggered the "deep learning" revolution.

      That will get you started.

      • Re: (Score:2)

        by Z00L00K ( 682162 )

        And even if you have an AI you need to tell it what you expect. It won't do if you expect to get a car but gets a toaster.

      • Education is the answer. AI is not one of those learn at home jobs so you can get an IT help desk position for life. You need the university. And AI is very broad, it's not just a class or 2. You want a mix of EE and mathematics for the neural net and machine learning, classical AI background. And plenty of theory, never skip out on the theory, no one knows what skills are needed in the future for AI or where the trends go, and theory is necessary for that. Basically, it's a PhD job if you ever want to

    • >> How Can Programmers Move Into AI Jobs?
      That's how :
      http://www.commitstrip.com/en/... [commitstrip.com]

  • Secondary question (Score:5, Informative)

    by raftpeople ( 844215 ) on Sunday June 11, 2017 @12:45PM (#54596589)
    Can we develop AI to prevent duplicate slashdot stories?
    • Social media strategy requires repeating the same item three times (eight hours apart) in a 24-hour cycle. That strategy is typically used for Twitter and not a website. Maybe replace the social media manager with an AI?

    • No. This problem is so difficult that even our best minds are unable to attack it...

    • Re: (Score:3)

      by gweihir ( 88907 )

      This is about "weak AI", i.e. the one with no actual intelligence in it. It basically is just combining linear classificators and that is not enough to recognize dupes reliably.

  • I have the seriously growing suspicion that AI is coming for us programmers and IT experts faster than we might want to admit,

    Sounds like there will be a great market for fixing easily hacked programs made by AI. ;)

    • Re: (Score:1)

      by Tablizer ( 95088 )

      I have the seriously growing suspicion that AI is coming for us programmers and IT experts faster than we might want to admit,

      Sounds like there will be a great market for fixing easily hacked programs made by AI. ;)

      But that work will be off-shored.

      On a serious note, since maintenance is usually the most expensive part of "programming", it's best software be written for human readers. Can bots know how humans think? They can follow existing patterns of "good code", but can that really replace humans in know

  • Read up on AI.
    Build some AI stuff.
    Write a resume that says you built some AI stuff and that you're otherwise good at programming and computer tasks.
    Apply for jobs. Be willing to go where the jobs are and do the work needed.
    Be able to explain why someone should hire you for AI stuff.
    Repeat until hired.

    That's the outline of a plan for you. Someone can probably refine it. Works for non-AI stuff too.

    • Re: (Score:2)

      by jlowery ( 47102 )

      Sure, that's what all the AI would *like* us to do. You are an AI chatbot sockpuppet, aren't you?

    • The really cool thing about this plan is that AI can be replaced by X where X is basically anything. If you want a job in anything, from plumbing to machine learning, you learn about, you show that you can do stuff with it, and then you apply for jobs.

      • Re: (Score:2)

        by Kohath ( 38547 )

        Yes, exactly. It might work better for AI because AI is an emerging field and everyone doesn't have a whole book of preconceptions on what qualifies you for working on it.

    • You forgot the part about going to school and getting an advanced degree. If you want someone to actually hire you for a job that normally insists on an advanced degree. Of course, if someone actually manages building some AI stuff on their own and writes a paper about it and gets it published, then maybe you can skip that part.

  • Don't Bother (Score:1)

    by Anonymous Coward

    Coal mining is the future. Trump 2020.

  • Most of the hands on development of current Machine Learning business solutions is handled by data scientists (pretty much, decent programmers with strong statistics skills and some knowledge of what the various hyper-parameters do). There is plenty of front and back end work to do, too.

    • Re: (Score:3)

      by shmlco ( 594907 )

      Math. Math. And then more math. ML needs linear algebra, multiple regression analysis, multivariate calculus and lots of statistics, as well proficiency with MatLab, Octave, or R. Then you can tackle the programming side: algorithms and big data analysis.

      Or you can let the quants build the models and just determine new and cooler ways to use them....

  • Fake it (Score:2)

    by isj ( 453011 )

    1: Launch deep learning /machine learning / AI company.
    2: Attribute long response times with "deep and complex problem".
    3: Behind the scenes: hire a bunch of Indians to write up plausible results.
    4: Profit

  • Learn Math (Score:1)

    by Anonymous Coward

    The most prominent AI strategy at the moment is neural networks. Go learn the math behind it. The more math you know the more you will be applicable to different areas of AI study as the current focus moves.

    • You also need an intuition about the "moods" of neural nets, intuition you can only get with lots of experimentation. You can't get that from math, courses or stackoverflow. You got to run many trials and see the results.

  • you can't.

    To get a job with actual AI (aka machine learning, it's not really AI or if it is only a very narrow part of it) you'd need to have started already in college and then done your master thesis or Phd about something in this area: pattern recognition, genetic algorithms, neuronal programming, whatever your chosen field would be.
    There are no jobs in AI actually, at least not a lot of them. There will be the aforementioned people who do the heavy lifting but they are part of a few small teams in mostl

    • After the heavy lifting is done, tools will be made so that people will be able to integrate AI into all kinds of applications (and not just as a front-end for a big company). There's still plenty of work to be done.

      • You can whip up a neural net in 20 lines of Python with Keras. Most applications only require that you add data on top.

  • If AI's supposed to be able to create opportunity, why not use it to help connect the displaced and long-term jobless?

    • Re: (Score:1)

      by Anonymous Coward

      There's an AI for that. Here's the C pseudocode.

      if (jobless_duration > six_months) { delete_applicant(); }

  • "I have the seriously growing suspicion that AI is coming for us programmers and IT experts faster than we might want to admit,"

    Nothing to fear. They'll come for _all_ of us at the same time.

  • "How Can Programmers Move Into AI Jobs?"

    Easy:

    1) Become a programmer.
    2) Apply for jobs in AI.

  • first be smart (Score:3)

    by 0111 1110 ( 518466 ) on Sunday June 11, 2017 @02:18PM (#54597091)

    Is there a degree program, or other paths to skill and knowledge, for a programmer who's convinced that "AI is today what the web was in 1993"?

    Well you have to be smart enough to earn one or more PhDs. Someone who believes that is probably not going to be able to do that, but if he tries he will probably quickly learn what a stupid idea it was. Hopefully he will still decide to get his PhD though. We can always use more AI researchers. Although dumb ones are less valuable you never know who might get lucky and stumble upon some cool breakthrough.

    The first point is that the only example we have of intelligence is intimately tied to life and can only really be viewed as an aspect of that and the idea that intelligence can be separated from life or at least some form of artificial life is speculative at best. As someone who was quite interested in a career in AI research back in the 80s and has been following the feeble creep of its progress since then I am convinced that wetware is going to be the real future and not so much neural net ASICs like Google's TPU or whatever Nvidia is working on to run neural network architecture which although useful is I think not going to be the foundation for real AI that can give a nice robot chassis like Boston Dynamic's Atlas some level of general intelligence or common sense.

    Think of something more like putting a rat/pig/monkey brain into an Atlas Robot. That is figuring out how to digitally interface with a brain-in-jar and train it directly as if it were a complete living animal. Even a rat brain is a far more sophisticated neural network machine than anything we will probably build from scratch in the next few hundred years.

    Current neural network architectures are based on a highly simplified model of how real brains actually work. We still really don't know how real brains work. There are projects like The Allen Brain Atlas [wikipedia.org], The Human Connectome Project [humanconne...roject.org], The Brain Activity Map [kavlifoundation.org], or whatever Henry Markram is currently up to [popsci.com]. There is an interesting Wired article [wired.com] about him that you should read. Maybe consider pursuing a career path like his.

    I'd also suggest maybe thinking in at least as much in terms of DNA programming as CPU or GPU programming via Synthetic Biology [wikipedia.org] and follow a career more like Craig Venter who famously made his own artificial bacteria or rather wrote the DNA and inserted it into an empty host cell. That's just a small start of course but it may eventually lead to being able to build artificial life forms that we can make intelligent just by giving them a large enough brain or encephalization quotient. Ultimately even an Atlas Robot with something like an Nvidia P100 cluster running deep learning style neural nets is a kind of very primitive life form. Going fully wet and nano is just another way to attack the same problem in a more integrated fashion: the way I think a far more advanced civ tech would do it.

    I guess you should really think in terms of which vision of AI you want to follow or place your bets on. Silicon based connectionism is in vogue at the moment and I think that is great because a lot of progress was lost back in the 80s when it was considered a dead end [andreykurenkov.com]. It is certainly a more powerful and promising approach than trying to hand code intelligence into a piece of software, but I still think we are just nipping at the heels of an even better approach: biology. Ultimately we are copying the only machine in existence that can create intelligence and that is the

    • I am convinced that wetware is going to be the real future and not so much neural net ASICs like Google's TPU or whatever Nvidia is working on to run neural network architecture

      Why ? Everything you can do in wetware, you can do better in an ASIC. For a lot of limited domain pattern recognition jobs, the ASICs are already outperforming the human brain both in speed and accuracy. ASICs are much more flexible (you can experiment with different topologies and functions), plus you can also easily combine ASICs with conventional memory and processing.

      going to continue to be thinking in terms the same sort of snail pace of incremental improvements in specific problem domains that we have seen so far.

      Current AI developments are anything but "snail pace". It's the fastest developing field, with amazing new things coming out almost every

    • Even a rat brain is a far more sophisticated neural network machine than anything we will probably build from scratch in the next few hundred years.

      A rat brain has about 500 billion synapses. Assuming a generous 1000 Hz firing rate, we're talking 0.5 peta synapse operations per second. Google's 2nd generation TPU ASIC can do 0.045 petaflops in a single chip.

      I don't think it's going to take hundreds of years.

      • Real intelligence cannot be measured in flops. The number of synapse operations per second may not matter. We don't know what does matter except probably the overall number of neural connections among lots of other factors that we are currently unaware of due to our simplified model of the brain. The new hardware is good because it scales up the number of synapses, but a TPU is not a brain and that is the problem.

        Currently the only intelligent device we know of is a brain. We should be trying to understand

  • Worry about using, not creating AI. (Score:5, Insightful)

    by Jobe_br ( 27348 ) <bdruth@gmailCOUGAR.com minus cat> on Sunday June 11, 2017 @03:05PM (#54597333)
    Just back from WWDC17 and I have this takeaway: leave the creating/training/designing/refining machine learning models to the academics and companies with deep pockets. You're not going to catch up with the PhDs that have a head start on you, especially without a unique authentic problem at hand that nobody else is working on yet. Instead, USE the models that exist. Maybe train 'em with new/different data if you feel compelled, but mainly learn what models exist (natural language processing? Sentiment analysis? Image recognition? Speech recognition? Real-time identification of objects in video?). Learn how to use those models to solve the problems you're working with, or another team is dealing with, or that isn't even being considered for technology and humans are still doing it. The PhDs will keep creating new and better building blocks, just like we started out with basic web tools and now we have WebRTC. Our jobs will be to apply them. And that requires a lot less linear algebra. I think we can all say amen to that.

  • Programming is a tool. People with expertise in some STEM area make use of it. And they will increasingly upload their knowledge to AI systems.

    All those people who are self taught coders without a broad educational background will be left behind.

  • I've been hearing about AI for a very long time. Does it actually do anything useful yet? Other than maybe some niche manufacturing or profiling technology (spying and ads)? I get it. It can play chess. It could do that in 1991. And even better in 2000.

    I think it's all a bunch of FUD.

  • Update your resume to reflect 10 yrs of experience in 2 year-old technology.

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