No it’s not, and part of that is the current legislative laissez-faire in the US that put its regulatory bodies on a hiatus. Under normal circumstances, this stuff should have been under much more scrutiny and regulations. I’m not saying that the state should control what LLMs do or who’s access to them, but they could very much tackle the deceptive marketing, environmental and societal impact, unsound financing, abnormal market consolidation, and mitigate the overall financial risk.

I mean, unless you’ve got a hardware accelerator (which won’t be a thing before multiple years from AV2’s release, and possibly more due to its complexity), it will be measurably much worse than current codecs.

Ballsy of you to assume that it got “easier for normies”, the answer probably lies in asking “easier for what?”. Computers have been redefined as consumption devices in a scheme to extract maximal profit out of their captive users. So it’s certainly “easy” for a growing number of users baited into dark patterns to hand over their credit card details and get addicted to antisocial networks and whatnot. But using modern devices as traditional computers, with intent, as a productive tool? It got much harder, except maybe for the Linux users, normies and “geeks” alike.

I still have mine, it’s a decent audiobook player for the kids, it even has a jack, a replaceable battery, induction charging and is so nimble. And did I mention the full keyboard? I hate that we followed Apple design ideals, for the sake of having today overpowered dumb, heavy , fragile and inconvenient consumption-only bricks.

It doesn’t make any sense for large group chats: there’s just no secrecy to them. And that will cut you off from desired capabilities like server-side search. Not that I think that Zulip makes any sense in general when there are federated protocols that work and scale as well, but that’s another discussion.

I use my Trilium instance (notes-taking app, PKMS, …) for that kind of stuff: https://triliumnotes.org/

You can have a journal with day notes following a template with data-entry forms, which makes weight/mood/location/whatever tracker very easy to keep track of as a routine.

Is that another overpowered smartphone SoC running a wasteful android with a thin UI and dumbed-down music player? If so, let me just repurpose an old smartphone and save the planet from some e-waste. If not, kudos.

Maybe I’m the weird one here, but when I look at fiio, and they are essentially smartphones (beefy Exynos or Qualcomm SoCs, running a bloated Android), minus some connectivity apps and a mobile antenna, I’d rather just use a phone, thanks. Most portable music players have this minimalistic appeal of devices that were engineered for just one single purpose and to do it well, and fiio certainly isn’t having that vibe.

They may or may not, what makes it relevant nonetheless is the real loophole and the ethical/legal questions that ensue.

Perhaps that’s why I don’t work in marketing, but that would buy so much goodwill and interest from the early fans if they were just embracing it. The first game is very lovable, and people loving it would probably have a look at the new one and that’s essentially free advertising. The only reason I see them doing this is because they know already that it’s bad, like, very bad.

Yes, the point is LLMs are AI.

So, we’ve gone full circle. LLMs is a sub-category within the collection of ML techniques within the collection of AI techniques. Point is, AI is just a term here. A label, that indicates nothing about the abilities of LLMs to exert any form of intelligence or reasoning. In other words, the whole field of AI could (should?) have been named “computational statistics” or “mathematics applied to numerical datasets” (or whatever else you want, really…), and LLMs would absolutely belong to “CoStats”/“MAND” fields, for the same reason we say they relate to “AI” today, it’s just that nobody would be silly enough to call them “artificially intelligent”.

ML is AI too. But sales didn’t call it that because AI had the reputation of “just brute force with some heuristics”.

What sales are you even thinking about? What do even presume the market for ML algorithms to be? Nobody was shopping for support vector machines as a service, or spending tokens on linear regression, or using convolutional neural networks via API before the current LLM craze. What the field is experiencing right now is unheard of. Never before did the private sector jump gun on a niche technique and spent trillions to package, anthropomorphise and market it as if “AI has finally been figured out, and we happen not only to own it, but also to sell it to you”. This deceptive rhetoric would be a much tougher sell if some early computer scientists hadn’t happened to name their field “AI”.

Was there even a point you wanted to make? I’m not sure exactly where you are heading with all this.

I mean, “machine learning” was a marketing term invented exactly to avoid the A in AI.

No, it’s not. ML is a sub-category within the collection of AI techniques that describes those algorithms whose behaviour is the result of fitting a training data-set to a pre-defined model by minimising an agreed-upon error function. For the longest of times, we were just calling that “statistics”, and many ML techniques and algorithms predate computers by centuries. Your mean-squares curve fitting? …qualifies as ML. That is to say, ML is all AI, but not all AI is ML.

LLMs are no different than function fitting with mean-squares. They are not magical, they are not black-boxes: they are fully described and completely predictable.

I mean, have fun re-defining artificial intelligence to suit your narrative. Here’s what the scientific consensus stands at: https://en.wikipedia.org/wiki/Artificial_intelligence

It’s also no secret that the companies involved in the LLM craze (OpenAI, Anthropic, Microsoft, …) diligently muddy the waters as a marketing trick to sell their product for more than what it is, that’s precisely what I’m calling out here: we can collectively do better/know better than that.

Since it’s a topic that comes back often on /c/selfhosted@lemmy.world I didn’t want to open new floodgates, but I can only warmly recommend https://triliumnotes.org/ :-)

ok, but there’s not much substance to your comment besides unsubstantiated “zealotry” towards obsidian and some general hot takes against lemmy and the FOSS community through which it emerged.

Maybe you could start listing out a few aspects and features of obsidian that you deem so important and unique, and I’m sure that you may discover a few very compelling alternatives.

As far as I’m concerned, I’m all set with https://triliumnotes.org/ . It’s not just a more versatile and capable note taking app, it’s also one that I can deploy simultaneously “local first” and “as a web service”, so my notes are reachable everywhere (even where I’m not allowed to install the heavy client).

Joplin is reasonably good as long as you don’t use so much metadata to keep things organised. It’s also pretty rigid, and hence limiting. If you want something with the superficial simplicity of joplin, but that would scale up to your needs, I recommend giving https://triliumnotes.org/ a good look.

If you drop the plaintext requirement (which IMO is anachronistic, if not for the necessity to fend against a potentially turning hostile developer in a close-source set-up), you may find https://triliumnotes.org/ liberating.

If you must stick to the “notes as plain text files” paradigm, siyuan is better than obsidian in about every aspect, and logseq in other, more niche ones. Trilium is better than them all (IMHO), being the only one that does “note as data” correctly and efficiently (you don’t have the same data model divide like seen in notion between notes and databases).

You could, I don’t know, use an open source note taking app? I mean, it’s not like obsidian has some unique and unmatched capabilities ¯⁠\⁠_⁠(⁠ツ⁠)⁠_⁠/⁠¯

Add ty to the tally. Fuck.

Also, LLMs are not AI (in the sense that most people would deem meaningful): there’s no reasoning involved, just a convincing illusion of it, served by extensive knowledge compaction and next token prediction. That is not to say that LLMs are useless, or that the sort of all-powerful AI people fantasize about cannot exist (I really don’t know, no people do). That is to say that it’s not for now, and that before it happens, we will see another long AI winter before the emergence of something fundamentally more convincing than LLMs.