back to article You wanted robo-butlers. Instead, you're getting robo-BOFHs

Park Place Technologies for the past two years has been working with IT services biz BMC to develop a way to augment its data center service business with machine learning. Chris Adams, chief operating officer of the data center hardware-tending shop, based in Cleveland, Ohio, told The Register in a phone interview the way …

  1. Anonymous Coward
    Anonymous Coward

    OTOH

    When I automated, yes there was an AI component, the maintenance for FM secure radios at my command, 4 people ended up being able to maintain the same number of vehicle and handheld radios as if we deployed 18 people. Major plus with the Bureau of Personnel when they came out to evaluate our effectiveness. The big plus for the people was engaging with clients and tweaking the radios to maximum performance. Which just happened to match their psych profiles, but I didn't discuss that.

    Later on, they added CBP & ICE to their portfolio for support which I'd rather didn't happen. You can never tell where your code will end up.

  2. TRT Silver badge
    Terminator

    I've just picked up a fault in the AE35 unit.

    It's going to go 100% failure in 72 hours.

  3. Anonymous Coward
    Anonymous Coward

    Some training may be in order..

    .. to ensure it has the proper attitude.

    I propose we feed it all BOFH episodes.

  4. Anonymous Coward
    Anonymous Coward

    Can I not just write a script that checks the service and machine in bash and get it to send me an email should it stop working from another machine? ping or wget or some other such test? You could go even further and get it to try and fix the fault for you.

    Why do you need AI?

    1. TRT Silver badge

      Bash?

      Ah yes. Percussive maintenance. The technician's tap. One of my favourites.

    2. MonkeyCee

      Why AI?

      I'm reading a little into the article here, but my assumption is they are already doing this. Something breaks, they get an alert, and it gets fixed. More steps in the real world, but that's the general principle.

      If you then feed an AI (in this case machine learning) with your test data, which in this case would be your server logs and service record. Then the AI should be able to be given some new data from a different source (such as current logs), and make some sort of prediction about expected service required.

      Depending how you're defining your model of success/failure, it should enable people to at least process log files quicker, by flagging up stuff as either recognised behaviours that are OK, things that appear to be going wrong right now, stuff that appears to be going wrong in the near future, and stuff that is just unexpected.

      This is exactly the sort of thing AI is great for. Taking an existing data processing task done by humans and automating as much as possible, freeing up the meatsacks to do something more productive. The AI might be no good at diagnosing a solution, but it will be excellent at picking up the warning signs.

      So the AI hopefully will pick up potential faults that humans missed, which makes perfect sense. You need a level of reporting other than pass/fail, but the more detail, the more noise in the data. Even skimming out the "working as expected" and "temp fault but now working" messages there will be many more false positives than true positives. Or you've got other more pressing problems.

  5. Florida1920
    Pirate

    "A better experience for the customer"

    A handful of silicon is smarter than me. Yes, I feel better already.

  6. John Smith 19 Gold badge
    Coat

    BMC still in business.

    Wow, that's a blast from the past.

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