And Gen X is the Forgotten Generation.
At the same time Gen X actually may have had the best time. Relatively peaceful times, nice social cohesion, at the same time less parental governance and control, and generating some of the most creative music/movies/games.
Sometimes headache inducing housing prospects (from someone that never got to see them)
They remember us when they need their computers fixed
Odd thing to say about a generation that created computer games from scratch by writing machine code in their bedrooms.
Who?
…and the “Me Generation” is nicknamed Boomers. Their original name seems much more fitting than the nickname THEY GAVE THEMSELVES.
“Boomer” is still an acurate descriptor if you apply it to their tempers and emotional tolerance for minor inconveniences.
It comes from the baby boom. They’re dying out now
The slop generation
My daughter is 12. So one of first of gen Alpha. I keep trying to stress to her to learn as much as possible about AI and computers as possible and she really has no interest in either one.
Just trying to prep her for the future that awaits her. And worried she’s not listening
Just trying to prep her for the future that awaits her. And worried she’s not listening
So, a normal parent-child relationship
Glad to see that traditions are still strong.
Fuck AI, they (gen alpha) have no interest because they’re smart enough to know it’s replacing them. It’s NOT smart (LLMs), and anyone who thinks AI is intelligent is a fucking idiot!
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.
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
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.
[…] 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
Here in eastern asia gen z also sometimes refer to strawberry generation by gen y because there’s a lot of soft and spoiled brat, which is ironically raised by gen y.
Yeah, as a millenial from south east asia I recently heard that too. discussed it with my mom and my brother who’s arguably very late millenial early gen z, those gen y only look at the “bad” aspect of a strawberry. where they treat them without care and brute force, of course you’re going to squish them. treat strawberries, both the fruit and the supposed fragile and soft gen z, with care and respect, then you can reap some sweet strawbs. but it should of course goes that everyone should treat each other with care and respect in general.
