• Category Archives Computers
  • Imagine a life with no computers……ahhhh……bliss…..

  • IoT hand dryer

    Oh, gross, this is just messed up and wrong… When are they going to stop doing this sort of thing just because they can??

    What am I ranting about this time?

    https://internetofbusiness.com/intelligent-addryer-bathroom/

    Drying your hands after a bathroom break may never be the same again. A new smart product, the AdDryer from Savortex, promises to blast users with advertisements, as well as hot air, while simultaneously collecting data on the use of toilet facilities.

    So it’s a hand dryer. It has a camera in the bottom. It can identify people, individual people, by the way you dry your hands, apparently no two people do it exactly the same.
    So it learns who you are and when you use it. It then tailors ads to play on its video screen while you stand there for 10 seconds and dry your hands.

    How revenue is shared will vary from client to client. Savortex works out an ‘audience value’, based on visitor demographics and other data, and a cost-per-view ad rate is formulated.

    When you put it all together, Ahmed told Internet of Business that the AdDryer allows enterprises to “transform wasteful and costly commercial washrooms into connected, hygienic, and sustainable revenue-producing assets.”

    Yuck. No thanks.


  • AI and heart attacks

    We blogged a few posts ago about AI and criminal activity, how AI can (and did) do a better job than a human judge in predicting if a person would go on to reoffend if released.

    Mixed feelings on that one?

    How about this then….. They used AI to look at around 350,000 medical records with the sole focus on predicting the person’s likelihood of having a heart attack.

    http://www.sciencemag.org/news/2017/04/self-taught-artificial-intelligence-beats-doctors-predicting-heart-attacks

    First, the artificial intelligence (AI) algorithms had to train themselves. They used about 78% of the data—some 295,267 records—to search for patterns and build their own internal “guidelines.” They then tested themselves on the remaining records. Using record data available in 2005, they predicted which patients would have their first cardiovascular event over the next 10 years, and checked the guesses against the 2015 records.

    So the exact same process is used in both scenarios, a whole lot of data, a chunk of training time and then checking the training.
    To me this is one of the interesting things about AI, it’s the same process to teach a computer about criminal activity as predicting heart attacks.
    In this study, they used 4 different AI methods, we might get into them some other time, but enough to say, my personal favorite was among them, neural networks.

    All four AI methods performed significantly better than the ACC/AHA guidelines. The four new methods ranged from 0.745 to 0.764 (with 1.0 being perfect), Weng’s team reports this month in PLOS ONE. The best one—neural networks—correctly predicted 7.6% more events than the ACC/AHA method, and it raised 1.6% fewer false alarms. In the test sample of about 83,000 records, that amounts to 355 additional patients whose lives could have been saved. That’s because prediction often leads to prevention.

    So yeah. My point is, while we all may have been creeped out by a computer deciding if you should stay locked behind bars, or set free, there are other, many other, uses for AI interaction with human lives.


  • No degree tech job

    I have talked about this here and there in past posts, so it’s good to see it start to hit mainstream media and have it come up in actual conversations with people that do something changing the ‘norm’.

    Why More Tech Companies Are Hiring People Without Degrees.

    For years, the tech pipeline has been fed mostly from the same elite universities. This has created a feedback loop of talent and a largely homogenous workplace. As a result, tech continues to stumble when it comes to diversity.

    The technology industry is now trying to figure out a way to attack its cultural and demographic homogeneity issues. One simple initiative is to begin to recruit talent from people outside of its preferred networks. One way is to extend their recruiting efforts to people who don’t have four-year degrees.

    So how do you find these people, what sort of ‘test’ do you apply to them since they don’t have a degree check box that you can lazily tick?

    IBM’s head of talent organization, Sam Ladah, calls this sort of initiative a focus on “new-collar jobs.” The idea, he says, is to look toward different applicant pools to find new talent. “We consider them based on their skills,” he says, and don’t take into account their educational background. This includes applicants who didn’t get a four-year degree but have proven their technical knowledge in other ways.

    What other way?

    “We’re looking for people who have a real passion for technology,” says Ladah. He goes on to say that currently about 10% to 15% of IBM’s new hires don’t have traditional four-year degrees.

    To do this is going to take work. You are going to need to go out and lift a few rocks to find these people, they are not going to seek out the limelight, they are not going to be making a lot of look-at-me noise. Their minds are on other aspects of life…. Like what?

    Passion. This is sort of in the same corner as what I sometimes call curiosity. In fact, I think they both need, and often do, go together.
    Some of the people we have interviewed here at Opto over the years often don’t have a smartwatch, don’t know what model phone they have, are running XP on their computer at home…. They have a 4.0 or close to it GPA, clearly very ‘smart’, but there is no passion for tech, they are just good at what they do and you can tell they just took the line of least resistance through college. They ticked the box, they got their 4 year degree and are now job hunting.
    Frequently, when asked, they don’t do any programming at home, they have not built anything, have not written any programs outside of their homework assignments…. There is no passion, no excitement, no curiosity for how stuff works.

    Anyway, I just am glad to see that someone somewhere is thinking about hiring people that may not ‘fit’ the college mould and yet are driven in other ways that are desperately needed in all companies.


  • Leave a bad review? Get locked out of your garage.

    Gary is going to love this one…….

    Well of course you install a cloud (computer) connected garage door opener.
    I mean it was the hot ticket on your beloved Kickstarter or Indigogo right… What could go wrong?

    Locked out – Garadget owner blocks IoT app user following negative review

    The guy that puts it together is learning as he goes, which is sort of Ok, but he does not have the most robust servers in place. One guy takes it as a personal slight that things do not work as slick as the Google/Facebook experience and leaves a negative review. The guy that runs the servers takes a personal affront to said negative review and disables the guys account…. thus locking him out of his garage.

    The Internet grabs all available pitchforks and goes after everyone.
    Yup. Death Threats….
    The device guy tells everyone to calm down and the complainer lives on.

    I’m being a little silly, but what happens if you don’t like your pacemaker?
    What happens when a guy throws a bike into a critical substation and takes half the power grid down for 48 hours?
    To me, they are the same scenario. Either way, you don’t get what you expect. We expect 100% uptime. We just do.


  • AI judge

    File this one in the super scary bucket – Thanks.

    I am going to try really hard on this one and not comment, not share my thoughts, just, well, report the news…..

    How to Upgrade Judges with Machine Learning

    In a new study from the National Bureau of Economic Research, economists and computer scientists trained an algorithm to predict whether defendants were a flight risk from their rap sheet and court records using data from hundreds of thousands of cases in New York City. When tested on over a hundred thousand more cases that it hadn’t seen before, the algorithm proved better at predicting what defendants will do after release than judges.

    So, in short, they taught a computer, using old records on convicted criminals how to look at their rap sheet and feeding in what those convicted criminals ended up doing in their life when they were released or paroled.
    Using this as their baseline, they then started to look at current cases. Since the computer can not feel or have an emotion in the case, it just looks at the rap sheet and makes a call based on its rule engine…. In this case, turns out that it knows better than judges.

    Read the article if you want to know more.

    Ok, ok, I said I would not comment, but just a quick thought.. I wonder if we are not going to see something like this come in as we saw in cricket… the never blinking third umpire…. The usual two are out there, but when it comes down to the close call, they call in the third, the TV camera… It makes the call frame by frame…. I wonder if we are not going to see this sneak into the courtroom like this?
    Just a thought.