• webghost0101@sopuli.xyz
    link
    fedilink
    English
    arrow-up
    38
    ·
    edit-2
    2 months ago

    Autism is that you?

    “When we walk down the street, we know what we need to pay attention to—and what we don’t. Robots, on the other hand, treat all the information they receive about their surroundings with equal importance. Driverless cars have to continuously analyze data about things around them whether or not they are relevant. This keeps drivers and pedestrians safe, but it draws on a lot of energy and computing power.

  • ShittyBeatlesFCPres@lemmy.world
    link
    fedilink
    English
    arrow-up
    13
    ·
    2 months ago

    Laziness is the number one skill anyone innovative should have. Whoever invented the wheel definitely didn’t do it because they liked work. It was because they hated it knew there had to be a better way.

    So, what we really need is lazier inventors. Only then will we get lazier robots.

    • asudox@programming.dev
      link
      fedilink
      English
      arrow-up
      6
      ·
      edit-2
      2 months ago

      Give me 10k€ a month without me working and I’ll gladly become one of the so called “lazy inventors”.

      • Corkyskog@sh.itjust.works
        link
        fedilink
        English
        arrow-up
        2
        ·
        2 months ago

        Smart companies do payout when employees find some way to save a bunch of money. I don’t know why so many companies have a “we own anything you invent clause in employment contracts” just stifles innovation.

    • Ledivin@lemmy.world
      link
      fedilink
      English
      arrow-up
      27
      ·
      edit-2
      2 months ago

      Yes, but game engines also hold the entire world inside themselves. There’s no guessing, no estimating, no making sure that what it’s looking at is actually a human or a bush - it already knows that.

      The problem with computer vision being lazy is that it can’t ignore something without understanding what it’s looking at, and it can’t understand what it’s looking at without analyzing the data. It’s a circular problem, and will be ridiculously hard to solve - the crux of the issue is that we as people are analyzing that same data, we just don’t realize it.

      • Boozilla@lemmy.world
        link
        fedilink
        English
        arrow-up
        21
        ·
        2 months ago

        Humans are bad at it, too. If you’ve ever ridden a bike or motorcycle, you quickly learn that car and truck drivers simply aren’t looking for 2 wheelers. And therefore they don’t see them. (I think this reinforces your point).

        • Catoblepas@lemmy.blahaj.zone
          link
          fedilink
          English
          arrow-up
          11
          ·
          2 months ago

          Anyone who has been a pedestrian is also well acquainted with this. Cars watch for and expect other cars, not people (bikes, mopeds, etc). The amount of times I’ve almost been hit by someone who is staring intently at oncoming traffic and flooring it without looking anywhere else is too damn high.

          • Boozilla@lemmy.world
            link
            fedilink
            English
            arrow-up
            2
            ·
            2 months ago

            That’s also true. It’s less of a problem in pedestrian-heavy walkable cities and towns. But in the average American city or town covered in stroads where car is king, it’s a big problem.

        • JustARaccoon@lemmy.world
          link
          fedilink
          English
          arrow-up
          1
          ·
          2 months ago

          They are, but we’ve mostly got our subconscious doing it still, it’s not that we’re always doing big tasks we just have dedicated processes for it, so maybe that’s one way to tackle the problem, specialised processes for sorting data types that engages the main process to do the processing of said data.

      • MonkderVierte@lemmy.ml
        link
        fedilink
        English
        arrow-up
        1
        ·
        edit-2
        2 months ago

        Analyzing lazily by defalt and only take a closer look at the interesting bits, like the brain does with optical?