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Generation Y is nicknamed Millennials, Generation Z is the Smartphone Generation, Generation Alpha may be called GenAI. (infosec.pub)
submitted 2 weeks ago by to c/showerthoughts@lemmy.world
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[–] 2 points 2 weeks ago* (last edited 2 weeks ago) (3 children)

I'm not remotely a parent, but I wouldn't say that's the worst. Not everyone is into computers/ tech, and that's okay! Even if "AI" becomes magically reliable, affordable, cheap, and good, robotics is still REALLY hard*. So it's not like AI robots will instantly obsolete every job. As my uncle says "AI will never be able to rotate my tires or change my oil".

I wouldn't trust AI anywhere remotely near healthcare. Doctors, nurses, pharmacists, psychologists, veterinarians, etc. aren't going away anytime soon. The way they work will almost surely change, but the need isn't going anywhere.

Even in fields like art, where AI already has a non-zero market share, there will always be value in something made by human hands. I keep thinking of things like street art and murals. Like, sure, you could crib the design from AI and some people might be okay with that, but I imagine the folks with the cash to commission murals would generally want something with more emotional resonance** than "I put in word salad and got this out", but even then there's still skill in being able to paint said mural.

Sure, I'd argue that everyone using a tool should know exactly how and when to use it and what its limitations are, but that's true regardless of if we're talking about AI, table saws, hot glue guns, or hair driers.

* there's an excellent Tom Scott video on all the innovation that goes into strawberry farming, including robots to pick the berries. One of the things that is mentioned is building the robot so that it doesn't cost a fortune, because you can't just strap on every sensor in existence and then expect it to be more cost effective than paying humans to do the job (which they're currently better at anyway).

** Hell, that's about my approach to Kickstarter as of late: I don't back anything unless there's a disclaimer that they're not using AI.

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  • [–] 3 points 2 weeks ago (2 children)

    Yeah sure, and I said that robots would never know how to code or how to create images from words in my lifetime either, and here we are

    Frankly, the potential of the technology is uncertain enough that I think it is really important to learn as much as one can about these emerging technologies

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  • [–] 1 point 2 weeks ago (1 child)

    Fair, I'll never argue against learning as much as one can, but I'm also an intrinsically curious adult like that. That said, in the context of a 12 year old kid not interested in tech, you can lead a horse to water, but you can't make it drink, even if it is really important water that prepares horses for the future.

    I'd also like to raise the semi-philosophical rhetorical: does the robot know how to code or create images?

    I'd argue that LLMs have the equivalent of text prediction on steroids as trained by pattern matching all the code on GitHub and Stack Overflow.* The second you do something outside the training data (like work with a company's super secret internal tooling, use a library that's fallen out of favor, or worse yet use a library with a confusingly similar name to another more popular library) that "knowledge" is less than useless.

    Similarly, spitting out an image that looks like the training data doesn't imply an understanding of composition, lightning, color, alignment. leading lines, or any of the other things that make for a compelling image/ photograph/ painting/ etc. Nor does it implicitly translate to being able to create that image in any medium other than digital.

    * there's technically also a semantic argument to be made about robots being able to code since the advent of the compiler, but I know that's not what you meant.

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  • [–] 2 points 2 weeks ago* (last edited 2 weeks ago)

    [...] can't make it drink, even if it is really important water that prepares horses for the future.

    Yeah, that's true enough

    As for

    Does the robot know how to code or create images?

    I would posit that it doesn't really matter, what matter is that the output is good enough to be used, and thus it will be used. Which is what makes it really important to properly learn these tools, how they work, their limitations, etc. because sooner or later, you are most likely going to be forced into using them as part of your job. As that's how our society operates. If it it increases productivity, there are really grave incentives to use it

    The second you do something outside the training data [...] that "knowledge" is less than useless.

    Similarly, spitting out an image that looks like the training data [...] or any of the other things that make for a compelling image/ photograph/ painting/ etc.

    You know, I used to agree with this a lot, but as more things have been happening, more results have been published, more development in this space has occurred, more advanced models being released, and me reading more into it.. I'm not so sure anymore. Turns out that there really are results coming out of this, and that I was simply.. wrong. At this point I believe that I was wrong about this technology.

    LLMs in software can be pointed at documentation and "learn" how to use some internal tooling, even if it's not in the database at all. Apparently they can solve unsolved problems like in math or computer science too. It really looks like you can get novel output and research from them, even if still somewhat limited and expensive (and some nuance how valid some results are etc.), but the fact that we're even here, seriously discussing whether a system we created did research, is beyond imagination

    I'd argue that LLMs have the equivalent of text prediction on steroids as trained by pattern matching

    And there's one counterargument to this that I've heard which was interesting. And it goes something like

    "imagine your task is to predict the next word of a research article about something that's not done before, over and over, until you have a complete research article. And you optimize for the highest degree of believability and accuracy. How would you go about this? You could of course just put together many reasonably sounding words good enough, but it won't seem very believable/accurate for a research article on something novel if it doesn't match what is actually true. So, to improve accuracy, you might want to spend time actually trying to understand the problem, maybe even do research, and work through different solutions, until you find the best match. Basically, in order to predict the most likely next word, you're gonna want to dedicate resources and space into a "real" understanding, and build the prediction off of that. In other words, the prediction mechanism is just a convenient vehicle to promote actual, more advanced, processes. Yes, all it does is predict the next word, but in order to predict the next word with the highest amount of accuracy, it is useful to, well, reason about it."

    Basically, this, together with the scaling hypothesis, posits that maybe, just maybe, if we make big enough models and give them enough time to "think" through problems (which reasoning models do, often for a difficult problem they're tasked to just sit there and work through whatever it is they're asked to do for hours before giving their actual response) then we might actually get something approaching understanding. Or at least close enough to not be easily distinguishable

    And believe me when I say that I used to think that they couldn't possibly understand or reason too. There are many societal level problems with the technology and we as a society are not ready for it, as well as my livelihood being threatened (or at least changing into something I find less exciting), and so I have a cognitive bias towards, well, denying that there really is something there. But honestly, I'm not so sure anymore. There really might be something there after all, and we're heading into something completely unprecedented. Which is why, now more than ever, it's incredibly important to properly understand and explore how to navigate a new world shaped by this

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