Yeah, I’m in the same boat.

There’s also PeerTube, the Fediverse counterpart to YouTube. Unfortunately, while there’s some good stuff you can find (and some re-uploads of YouTube), there’s just not as much content. I’d imagine the userbase is pretty small, too.

I use a cheap VPS to host my email server. It’s a bit easier than running it solely at home, but there’s a lot of annoying work to “verify” yourself. Once you get your DNS records good, you shouldn’t be blocked after that (unlike a home server). It only costs me $5/month plus the domain, which I think is money well spent. Doing the admin work to make sure I’m secure still needs to happen, but I don’t mind that work and find it fun.

YES! I study AI, and this is exactly how I feel!

Side note-One of my favorite things to do is ask people what their use case for using AI is, and watch them sputter out “uh…emails and productivity and things.”

My thoughts exactly. Growth is a byproduct of quality. Similarly, if the Fediverse grows too much and quality starts to slip, we should also let it shrink until quality comes back. I think our aim should be quality, and anything else is just a side effect.

You’re getting downvoted, but you’re right. And that is the reason that using proprietary software and SaaS is a problem. If I’m only buying the right to use a copy of something as a company sees fit, then I’m not really buying anything. I’m essentially paying a company a tribute to use their software in their way.

Decades ago, it was the same way, but it felt different. We got physical media, and we could do what we wished with the files: modify them, delete them, etc. Hell, the EULAs for some '90s and early '00s software even said you could use the software in perpetuity, and we could use software in anyway we saw fit. The biggest constraint was on selling copies. Back then, and even now, that seems pretty reasonable. (Though, as an aside, it would have been better to also get access to the source code, but I digress.)

Now, we have to use company’s software exactly how they want us to use it. Personally, I refuse to go along with this (as much as I can), so I have migrated most of my digital life to FLOSS.

Not necessarily. The Free and Libre Open Source Software (FLOSS) movement is a thing. Most of the Fediverse is FLOSS, and I doubt there’s anyone who can take Lemmy or Mastodon closed source and buy every instance and then stop pop-up instances. It does require quite a bit of work, though, so it is difficult.

I think the real challenging thing is that a great FLOSS service needs to attract attention and care. When I bring up Fediverse/FLOSS alternatives to software my friends complain about, I’m met with lukewarm-at-best reactions, generally due to networking effects (I think).

Like @Crack0n7uesday@lemmy.world said, it’s on government phones. The thinking goes that TikTok, which is a Chinese company, is exporting too much data from US government devices. In other words, the government is worried the Chinese are spying. Given the amount of data that the TikTok app actually collects, the fear is probably not unreasonable. All corporate-owned social media collects way too much data, but TikTok really is next level from what I’ve read.

I think a big difference, though, is that there is political force to ending TikTok. The US government has no major issues with Twitter, Facebook, Reddit, etc. existing. Remember, there’s actual legislation banning TikTok. Whether that makes a real difference or not, well, I guess we just wait and see. Personally, I think they all should go down in flames.

I work/study in AI, and it is completely over-hyped. For one thing, the C-suite can’t wrap it’s head around the fact that AI != LLM; they all seem to think all AI is just LLMs. On top of that, they are way too eager to throw humans out of the loop.

That said, I think LLM applications, even in their current form, are super useful in development and business practices. I myself use it to increase my productivity in coding. But, I use it as an augmentation rather than a replacement. One of my friends put it best the other day, “LLMs are like a junior dev to your senior dev. You need to be hyper-specific, and you need to check it’s output.” In other words, it’s great for off-loading some work, but it isn’t going to completely replace humans.

With that said, I’m a bit annoyed that other AI fields are being over-shadowed by LLMs. There’s a ton of other interesting work being done in those fields that is super useful and important. All of them, though, are not going to replace humans but rather augment and make humans more productive. I’ve found that an AI-Human team is most effective.

Building a signup wizard to use that information to select a instance would seem to be the best approach.

That’s actually not a bad idea. I’m not on board with mining contacts, but I think there’s a simple, transparent way to do this that can actually be fun: a personality quiz. Sure, if someone knows what instance to join already, they can override this. But if they don’t, they get like five questions, and then they are matched to an instance.

Except that scaling alone won’t lead to AGI. It may generate better, more convincing text, but the core algorithm is the same. That “special juice” is almost certainly going to come from algorithmic development rather than just throwing more compute at the problem.

I mean, that’s more-or-less what I said. We don’t know the theoretical limits of how good that text generation is when throwing more compute at it and adding parameters for the context window. Can it generate a whole book that is fairly convincing, write legal briefs off of the sum of human legal knowledge, etc.? Ultimately, the algorithm is the same, so like you said, the same problems persist, and the definition of “better” is wishy-washy.

Cool, Bill Gates has opinions. I think he’s being hasty and speaking out of turn and only partially correct. From my understanding, the “big innovation” of GPT-4 was adding more parameters and scaling up compute. The core algorithms are generally agreed to be mostly the same from earlier versions (not that we know for sure since OpenAI has only released a technical report). Based on that, the real limit on this technology is compute and number of parameters (as boring as that is), and so he’s right that the algorithm design may have plateaued. However, we really don’t know what will happen if truly monster rigs with tens-of-trillions of parameters are used when trained on the entirety of human written knowledge (morality of that notwithstanding), and that’s where he’s wrong.