This is rapidly becoming less and less true unfortunately

Underlying kernel aside, I think that the Steamdeck’s SteamOS is an excellent example of how “easy to use” != “smaller feature-set”. I’ve heard countless times from apple dudes that the reason that their stuff allegedly “just works” is because of the lack of some functionally that if present would overwhelm the user. You know, as if ios and android don’t share fundamentally the same user interface principles. But they do have a point, a green user can be overwhelmed when presented with a huge feature set all at once. Yet, despite SteamOS literally having a full-blown desktop environment, the UI frankly is way less confusing than my Xbox. It just goes to show that it’s not about the number of features, it’s about how they’re presented. Power users don’t mind digging into a (well designed) settings menu to enable some advanced functionality, and keeping those advanced features and settings (with reasonable defaults) hidden around the corner behind an unlocked door helps the newbie get started with confidence.

Reference: https://youtu.be/fXZj4Wy58Pk?si=s3te3a9tq-J4muWi

What programming language is Afungus Amongus?

I just had an Amazon package delayed for a week it says. It doesn’t name names but…

A small number of deliveries may arrive a day later than anticipated due to a third-party technology outage.

Machine learning is a subset of artificial intelligence, along with things like machine perception, reasoning, and planning. Like I said in a different thread, ai is a really, really broad term. It doesn’t need to actually be Jarvis to be AI. You’re thinking of general ai

“Aware of its surroundings” is a pretty general phrase though. You, presumably a human, can only be as aware as far as your senses enable you to be. We (humans) tend to assume that we have complete awareness of our surroundings, but how could we possibly know? If there was something out there we weren’t aware of, well we aren’t aware of it. What we know as our “surroundings” is a construct the brain invents to parse our own “raw sensor data”. To an LLM, it “senses” strings of tokens. That’s its whole environment, it’s all that it can comprehend. From its perspective, there’s nothing else. Basically all I’m saying is that you seem to be taking awareness-of-surroundings to mean awareness-of-surroundings-like-a-human, when it’s much more broad than that. Arguably uselessly broad, granted, but the intent of the phrase is to say that an AI should observe and react flexibly.

Really all “AI” is just a handwavy term for “the next step in flexible, reactive computing”. Today that happens to look like LLMs and diffusion models.

AI is broader term then you think, it goes back to the beginnings of modern computing with Alan Turing. You seem to be thinking about the movie definition of AI, not the academic.

Not sure why you’re being downvoted, this might be the best feature of Google photos

Seriously, the field of artificial intelligence has been around since the beginning of computer science, since Alan Turing founded it after coming up with the modern computer. Frankly, if you ask me, anyone complaining about LLMs being referred to as AI has been watching too many movies. AI != Human-but-metal and it never has. Going by the Wikipedia article, to be considered AI, a machine just has to perceive it’s environment and learn - degree notwithstanding.

Of course this definition is pretty vague, so in practice AI tends to refer to the cutting edge of flexible computer algorithms. Many now-mundane algorithms much simpler than today’s LLMs (like A* and genetic algorithms) were once considered AI for their flexible logic. At some point the Internet decided that it doesn’t count unless it’s literally Jarvis, but that’s a very stingy definition of a very broad field.

Not sure why you’re being downvoted. Gaming PC’s are expensive and a luxury! It makes sense economically. With consoles there’s an incentive to sell hardware cheap to get people into the ecosystem. With the exception of the steamdeck, there’s no such incentive for PC’s: if the hardware is worth x amount, you can bet your ass you’ll have to pay at least that. Yeah games are generally cheaper on PC, but not by much, and the barrier to entry is much lower for consoles. Hell, the PC I just built from used parts and Amazon deals cost me $800 (not including accessories), and while the processor and ram is almost certainly better than the ps5’s, the graphics are about on par, if anything slightly worse. You can find used ps5’s for less than $400. Is there really a used PC out there that can touch that?

Go into desktop mode, there’s a bedrock launcher in the package manager store thing, I forgot the names of both of those things, but search “Minecraft” and you should find it. Anyway, it basically loads the Android version of the game. It works pretty well. I play bedrock because everyone I play with is on Xbox

The feeling before writing a piece of code in an empty file isn’t unlike the feeling I get when I’m about to step into the hot tub. Once I’m in I’m good, but I really have to psyche myself up to get in

Edit: Come to think of it, it’s the same with writing

Which bible passage is that again?

Thank you very much, I’ve been noticing it’s been getting a lot of good little updates recently

And even if they do need to train a model, transfer learning is often a viable shortcut

I always have to watch it 20 times before I continue scrolling

Even on Windows obs is the best performing option, last I tried (which was a few years ago granted)

I don’t agree with the position of banana

You’re right, it’s very much context dependent, and I appreciate your incite on how this clash between psychology and computer science muddies the terms. As a CS guy myself who’s just dipping my toes into NN’s, I lean toward the psychology definition, where intelligence is measured by behavior.

In an artificial neural network, the algorithms that wrangle data and build a model aren’t really what makes the decisions, they just build out the “body” (model, generator functions) and “environment” (data format), so to speak. If anything that code is more comparable to DNA than any state of mind. Training on data is where the knowledge comes from, and by making connections the model can “reason” a good answer with the correlations it found. Those processes are vague enough that I don’t feel comfortable calling them algorithms, though. It’s pretty divorced from cold, hard code.