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4 Experiments Where the AI Outsmarted Its Creators! 🤖

The paper "The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities" is available here:

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  • @MobyMotion says:

    Very important message at the end there. It’s something that Nick Bostrom calls “perverse instantiation” – and will be crucial to avoid in a future superintelligent agent. For example, we can’t just ask it to maximise happiness in the world, because it might capture everyone and place electrodes into the pleasure centre of our brains, technically increasing happiness vastly

    • @TwoMinutePapers says:

      Agreed. I would go so far as to say there is little reason to think a superintelligence would do anything else than find the simplest loophole to maximize the prescribed objective. Even rudimentary experiments seem to point in this direction. We have to be wary of that.

    • @MobyMotion says:

      Two Minute Papers absolutely. The only difference is that as the AI becomes more powerful, the loopholes become more intricate and difficult to predict

    • @michaelemouse1 says:

      So AI would be like a genie that grants you all your wishes, exactly as you ask, in a way that catastrophically backfires. This should be the premise of a sci-fi comedy already.

    • @RHLW says:

      I pretty much have to disagree. If such a thing can’t “think forward” over such a cheat, evaluate if it’s good or bad as evaluated from different angles/metrics, and figure out that the simple solution isn’t always the correct one, then it is not a “super intelligence”… it’s just a dumb robot.

    • @scno0B1 says:

      why would a robot not choose the simplest solution? we can see that a robot does come up with the simplest solutions 😛

  • @jonathanxdoe says:

    Me: “AI! Solve the world hunger problem!”
    Next day, earth population = 0.
    AI: “Problem solved! Press any key to continue.”

    • @MrNight-dg1ug says:

      John Doe lol!

    • @wisgarus says:

      John Doe
      One eternity
      Later

    • @michaelbuckers says:

      You jest, but limiting the population is literally the only way you can ensure that limited supply can be rationed to all people at a given minimum. China and India are neck deep in this, but first world doesn’t have this problem so they think it’s possible to just feed everyone hungry and that would magically not bankrupt everyone else (the hungry are bankrupt to start with).

      The truth is, poor people are poor because that’s what they’re worth in a fair and square free market economy. They have no skills and qualities to be rich, they don’t get rich through marketable merit and even if they become rich by chance, soon enough they lose all money and go back to being poor. Inequality is a direct consequence of people not being identical. Having the same reward for working twice as hard doesn’t sound appealing to me, much less living in a totalitarian society that forbids stepping out the line for half an inch in order to ensure equality.

    • @filippovannella4957 says:

      you definitely made my day! xD

    • @SomeshSamadder says:

      hence Thanos 😂

  • @DarcyWhyte says:

    Robots don’t “think” outside the box. They don’t know there is a box.

    • @davidwuhrer6704 says:

      That is the secret.
      The researches who formulated the problem thought there was a box.
      They expected the AI to think inside it.
      But the AI never knew about the box.
      There was no box.
      And the AI solved the problem as stated outside it.

    • @DarcyWhyte says:

      That’s right there’s no box. 🙂

    • @planetary-rendez-vous says:

      So you mean humans are conditioned to think inside a box

    • @Anon-xd3cf says:

      Darcy Whyte
      No the “robots” don’t know there is a “box” to think outside of…
      AI however are increasingly able to “think” for themselves both in and out of the proverbial *box*

    • @milanstevic8424 says:

      the error is simply in trying to describe a very simple “box” while not being able to reconstruct what’s actually described. people do this all the time, and this is why good teachers are hard to find.

      the box that the AI couldn’t circumvent was the general canvas, or in this case the general physics sandbox with gravity acceleration and a ground constraint. this is the experimental >reality<, along with the clears goals set by the scientists (use muscles and joints to move from A to B, minimize leg contact with the ground). it is the scientists' inability to investigate the problem space and imagine potential solutions that lead them to this issue. sometimes this is near impossible, as someone compared it with the halting problem, but more often than not, it's an issue of not being particularly imaginative. the skill of understanding the problem space is incredibly important in the fields of programming and game design. in other words, in creating very complex but fully interactive state machines, design of which tends to be impossible to grasp with limited human cognition, and therefore has to be explored strategically or systemically. in case of this particular AI problem, this should really be called "the jinn problem" -- or 'be careful what you wish for' -- similar to how Mulder (from the X files), in the episode "Je Souhaite" ( https://en.wikipedia.org/wiki/Je_Souhaite ) wished for a world’s peace only to be introduced with a world without humans. the wish is obviously fulfilled from the standpoint of a local minimum, which is exactly what the AI does, but it can be stated that any intelligent agent would do this. when a human does this in a loosely defined competitive domain — we call that cheating and/or exploiting.

      therefore i.e. slavery is a perfect solution for cheap workforce if only you’d expand your “box” or think outside of any ethical values.

      hence, the rise of laws* in “civilized” societies implores the existence of basically immature and intrinsically imbalanced rules, trying to punish any unwanted cheats and exploits in the system, to minimize exploitative behavior and further repercussions.

      thank god we don’t make games like that, as we typically root out the causes and/or explore better systemic solutions to imbalances.

      * obviously this does not include laws that govern rules made for political and regulatory reasons, as these are implemented to achieve something else.

  • @davidwuhrer6704 says:

    This reminds me of the old story of the computer that was asked to design a ship that would cross the English Channel in as short a time as possible.
    It designed a bridge.

    • @HolbrookStark says:

      Tbh a bridge made of a super long boat floating in the middle of the English Channel tip to tip with the land masses would be the most lit bridge on earth 🔥

    • @clokky1672 says:

      This really made my chuckle.

    • @thehiddenninja3428 says:

      Well, there was no size restriction.
      It was tasked to have the lowest time between the back end touching point A and the front end touching point B.
      Obviously the lowest time is 0; where it’s already touching both points

    • @AverageBrethren says:

      @@HolbrookStark thats a lot of material. It’s a pipedream.

    • @HolbrookStark says:

      @@AverageBrethren there was a time people would have said the same about ever building a bridge across the English Channel at all. Really, using a floating structure might use a lot less material and be a lot cheaper than the other options for how to do it

  • @JoshuaBarretto says:

    This reminds me of a project I worked on 2 years ago. I evolved a neural control system for a 2D physical object made of limbs and muscles. I gave it the task of walking as far as possible to the right in 30 seconds. I expected the system to get *really* good at running.

    Result? The system found a bug in my physics simulation that allowed it to accelerate to incredible speeds by oscillating a particular limb at a high frequency.

    • @milanstevic8424 says:

      we’d do it too if only there was such a glitch in the system.
      actually we exploit the nature for any such glitch we can find.
      thankfully the universe is a bit more robust than our software, and energy conservation laws are impossibly hard to circumvent.

    • @ThatSkyAmber says:

      give it’s joints a speed limit more on par with a human’s..? or anyway, below the critical value needed for the exploit.

    • @Moreoverover says:

      Reminds me of what video game speedrunners do, finding glitches is goal number uno.

    • @jetison333 says:

      @@milanstevic8424 honestly I dont think it would be too far off to call computers and other advanced technology as exploits. I mean, we tricked a rock into thinking.

    • @milanstevic8424 says:

      @@jetison333 I agree, even though rocks do not think (yet).

      But what is a human if not just a thinking emulsion of oil (hydrocarbons) and water? Who are we to exploit anything that wasn’t already made with such a capacity? We are merely discovering that rocks aren’t what we thought they were.

      Given additional rules and configurations, everything appears to be capable of supernatural performance, where supernatural = anything that exceeds our prior expectations of nature.

      “Any sufficiently advanced technology is indistinguishable from magic”

      Which is exactly the point at which we begin to categorize it as extraordinary, instead of supernatural, until it one day just becomes ordinary…

      It’s completely inverse, as it’s a process of discovery, thus we’re only getting smarter and more cognizant of our surroundings. But for some reason, we really like to believe we’re becoming gods, as if we’re somehow leaving the rules behind. We’re hacking, we’re incredible… We’re not, we’re just not appreciating the rules for what they truly are.

      In my opinion, there is much more to learn if we are ever to become humble masters.

  • @harry356 says:

    We had a bunch of aibo robots play hide and seek and train an ai. They stopped hiding quickly, we thought something is wrong, we made an error in our programming. It took us a while to find out that they learned to stay at the starting point so they where immediately free when the countdown stopped. They found a loophole in the rules. Incredible fun.

  • @laurenceperkins7468 says:

    Reminds me of one of the early AI experiments using genetic algorithm adjusted neural networks. They ran it for a while and there was a clear winner that could solve all the different problems they were throwing at it. It wasn’t the fastest solver for any of the cases, but it was second-fastest for all or nearly all of them.

    So they focused their studies on that one, and turned the others lines off. At which point the one they were studying ceased being able to solve any of the problems at all. So they ripped it apart to see what made it tick and it turns out that it had stumbled upon a flaw in their operating system that let it monitor what the other AIs were doing, and whenever it saw one report an answer it would steal the data and use it.

  • @Zorn101 says:

    AI is like a 4-year-old sorting butterfly pictures.
    If I just tare up and eat the picture. the sorting is done!

  • @Moonz97 says:

    This is so hilarious. I remember programming a vehicle that was tasked with avoiding obstacles. It had controls over the steering wheel only, and it was always moving forward. To my surprise, the bot maximized its wall avoidance time by going in circles. I find that so funny lol.

  • @NortheastGamer says:

    “The AI found a bug in the physics engine” So basically it did science.

  • @amyshaw893 says:

    Reminds me of something I saw where some people were training an AI to play Qbert, and at one point it found a secret bonus stage that nobody had ever found before

  • @teddywoodburn1295 says:

    I heard about an ai that was trained to play Tetris, the only instruction it was given was to avoid dying, eventually the ai just learned to pause the game, therefore avoiding dying

    • @zserf says:

      Source: https://www.youtube.com/watch?v=xOCurBYI_gY
      Tetris is at 15:15, but the rest of the video is interesting as well.

    • @theshermantanker7043 says:

      That’s what i used to do XD

      But it got boring after a while

    • @teddywoodburn1295 says:

      @DarkGrisen that’s true, but the person creating the program basically told the ai that it was about not dying, rather than getting a high score

    • @Ebani says:

      @DarkGrisen There is no difference then. By not dying it will get an infinite score eventually so a high score by itself is meaningless, not dying turns out to be the best factor to predict a high score.
      He could’ve easily just removed the pause function too but it’s funny to see the results he got

    • @teddywoodburn1295 says:

      @DarkGrisen exactly, I think the lesson in that is that you have to think about what you’re actually telling the ai to do

  • @theshermantanker7043 says:

    In other words the AI has learnt the ways of video game speedrunners

  • @alansmithee419 says:

    The idea of thinking outside the box is limited to humans. The box is something our minds put in place – it is a result of how our brains work. The ai doesn’t have a box meaning it can find the best solution, but also meaning there are many many more things that it could try that it needs to slog through.
    We need that box, otherwise we’d be so flooded with ideas that our brains wouldn’t be able to sift through them all.
    Our limitations allow us to function, but the way computers work means such a box would be detrimental to them.
    – sincerely, not a scientist.

    • @EGarrett01 says:

      A “box” is simply a method that appears to be the first step towards generating the best result. But it can be a problem because there are often methods that don’t immediately seem to lead to the right direction but which ultimately produce a better result, like a walking physics sim spinning its arm in place super-fast until it takes off like a helicopter and can travel faster than someone walking.

      If AI are working through successive generations, it will have periods or groups of results that follow a certain path that produces better things short-term, this is the same as people “thinking in the box.” But if it is allowed to try other things that are inefficient at first and follow them multiple steps down the line, it then ends up being able to think outside the box.

    • @alansmithee419 says:

      @@EGarrett01 as far as I understand it, the box is the range of human intuition, and thinking outside of it is essentially going against the common way of human thinking. The ai doesn’t have intuition, nothing limiting its ideas or method of thought, therefore it has no box.
      Though honestly the proverbial box has never really had a definition, and its meaning could be interpreted any number of ways. I suppose both of our definitions are equally valid.

    • @xvxee7561 says:

      You have this hella backwards

    • @honkhonk8009 says:

      No, its because we would have past experiences influence decisions in the form of common sense.

    • @duc2133 says:

      @@alansmithee419 Ya’ll are trying to sound too deep. It just means that these experiments didn’t set enough factors to be practical. A robot flipping on its side woudn’t be practical, or the numerous other jokes on this thread — pushing the earth far away from the sun to “solve global warming” doesn’t make sense because its fucking stupid — the experimenter needed to set certain limitations for the computer to come up with a sensible solution. These robots aren’t lacking “intuition” its just a bad computer that needs to be programmed better.

  • @anthonyhadsell2673 says:

    Human: Reduce injured car crash victims
    Ai: Destroys all cars
    Human: Reduce injured car crash victims without destroying cars
    Ai: Disables airbag function so crashes result in death instead of injury

    • @pmangano says:

      Human: Teaches AI that death is result of injury
      AI: Throw every car with passengers in a lake, no crash means no crash victims, car is intact.

    • @decidueyezealot8611 says:

      Humans then drown to death.

    • @Solizeus says:

      Humans: Teaches IA not to damage the car or it’s passengers.
      IA: Disable the ignition, avoiding any damage.
      Humans: Stop that too.
      IA: Turns on Loud Bad music and drive in circles to make the passengers want to leave or turn the car off

    • @noddlecake329 says:

      This Is basically what they did in WWI they noticed an increase in head injuries when they introduced bullet proof helmets and so they made people stop wearing helmets. The problem was that the helmets were saving lives and leaving only an injury

    • @anthonyhadsell2673 says:

      @@noddlecake329 survivor bias. When they took all the holes the found in planes that were shot and layed them over one plan in ww2 they noticed the edge of the winds and a few other areas being shot more so they assumed they should reinforce those areas, the issue was that they were looking at the planes that survived and really they needed to reinforce the areas that did have bullet holes

  • @mrflip-flop3198 says:

    “Okay AI, I want you to solve global warming.”
    “Right away, now moving _Earth_ from the solar system. Caution: You may experience up to 45Gs.”

  • @renagonpoi5747 says:

    “If there are no numbers, there’s nothing to sort… problem solved.”

    I think a few more iterations and we’ll have robot overlords.

  • @josephoyek6574 says:

    AI: You have three wishes
    Me: *sweats

  • @iLeven713 says:

    It’s funny how these reinforcement learning models kind of act like Genie’s from folklore, with a “be careful what you ask for” twist

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