They're also important because physical touch is a need in and of itself. No matter what, in the end, we are still animals, with animalistic needs. You can't just think or will yourself out of them

You just start with math, there's plenty of math the average person learns in an average school curriculum that you could re-"invent". After that people would start taking you more seriously

Now they might consider you heretical or blasphemous still. If you played your cards right you could try to present yourself as a messenger from god although I personally really wouldn't want to go that route

If you went back 10000 years you definitively would have become an inventor or philosopher of sorts (assuming you could speak their language)

Well, I guess it depends on your education, but we get taught things in elementary school that wouldn't have been invented or discovered for thousands of years back then. And some technologies you likely could make without many pre-requisite technologies themselves

I do agree with that!

I sure would hope that those AI overviews are cached. Querying an LLMs for every repeated search sounds very wasteful

I would argue you should understand the tool you're using

"it is easier to imagine the end of the world than the end of capitalism"

I thought that was a relatively common thing under intense exertion?

As someone within this field, yup, lmfao

Me and all of my friends within compsci are all "yeah but I love my job" and they all talk about their co-workers who seem to hate programing and problem solving

And unfortunately it's really starting to seem like I have to start using LLMs as part of my job to keep up, and it's so sad

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

[...] 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

I typically just sit on my phone while they work, and possibly chat with them if they feel like chatting

Idk, gaming on my computer feels like "too much", if that makes sense, but that may just be because of negative associations from when people watched me sit on the PC in the past. Being on your phone feels more socially acceptable and you don't get so "lock in" if that makes sense

That said, I think it's perfectly acceptable to be om your computer yeah

How can people possibly afford that??

Cat water bowl

I do listen to "both" sides! That's exactly why I'm a leftist!

I don't get why centrists think that you have to be "centrist" to listen to both sides, or why doing so makes you a centrist.

Furries are like one of the main customers of artists

I don't think it's a criticism? It's more about highlighting the slight absurdity of super-high tech power generation still using the same method that has been used since the very start of electricity generation. A turbine spun by evaporated water.

"why should disabled people have a life outside of work?"

The thing with AI, is that it mostly only produces trash now.

But look back to 5 years ago, what were people saying about AI? Hell, many thought that the kind of art that AI can make today would be impossible for it to create! ..And then it suddenly did. We'll, it wasn't actually suddenly, and the people in the space probably saw it coming, but still.

The point is, we keep getting better at creating AIs that do stuff we thought were impossible a few years ago, stuff that we said would show true intelligence if an AI can do them. And yet, every time some new impressive AI gets developed, people say it sucks, is boring, is far from good enough, etc. While it slowly, every time, creeps on closer to us, replacing a few jobs here and there in the fringes. Sure, it's not true intelligence, and it still doesn't beat humans, but, it beats most, at demand, and what happens when inevitably better AIs get created?

Maybe we're in for another decades long AI winter.. or maybe we're not, and plenty more AI revolutions are just around the corner. I think AIs current capabilities are frighteningly good, and not something I expected to happen this soon. And the last decade or so has seen massive progress in this area, who's to say where the current path stops?

Except she still gets royalties and uses those to donate to political organizations, so you know