I work in this field. In my company, we use smaller, specialized models all the time. Ignore the VC hype bubble.

Funnily enough, this is also my field, though I am not at uni anymore since I now work in this area. I agree that current literature rightfully makes no claims of AGI.

Calling transformer models (also definitely not the only type of LLM that is feasible - mamba, Llada, … exist!) “fancy autocomplete” is very disingenuous in my view. Also, the current boom of AI includes way more than the flashy language models that the general population directly interacts with, as you surely know. And whether a model is able to “generalize” depends on whether you mean within its objective boundaries or outside of them, I would say.

I agree that a training objective of predicting the next token in a sequence probably won’t be enough to achieve generalized intelligence. However, modelling language is the first and most important step on that path since us humans use language to abstract and represent problems.

Looking at the current pace of development, I wouldn’t be so pessimistic, though I won’t make claims as to when we will reach AGI. While there may not be a complete theoretical framework for AGI, I believe it will be achieved in a similar way as current systems are, being developed first and explained after.

In the case of reasoning models, definitely. Reasoning datasets weren’t even a thing a year ago and from what we know about how the larger models are trained, most task-specific training data is artificial (oftentimes a small amount is human-generated and then synthetically augmented).

However, I think it’s safe to assume that this has been the case for regular chat models as well - the self-instruct and ORCA papers are quite old already.

The goalpost has shifted a lot in the past few years, but in the broader and even narrower definition, current language models are precisely what was meant by AI and generally fall into that category of computer program. They aren’t broad / general AI, but definitely narrow / weak AI systems.

I get that it’s trendy to shit on LLMs, often for good reason, but that should not mean we just redefine terms because some system doesn’t fit our idealized under-informed definition of a technical term.

Ah yes Mr. Professor, mind telling us how you came to this conclusion?

To me you come off like an early 1900s fear monger a la “There will never be a flying machine, humans aren’t meant to be in the sky and it’s physically impossible”.

If you literally meant that there is no such thing yet, then sure, we haven’t reached AGI yet. But the rest of your sentence is very disingenuous toward the thousands of scientists and developers working on precisely these issues and also extremely ignorant of current developments.

No, at least not in the sense that “hallucination” is used in the context of LLMs. It is specifically used to differentiate between the two cases you jumbled together: outputting correct information (as is represented in the training data) vs outputting “made-up” information.

A language model doesn’t “try” anything, it does what it is trained to do - predict the next token, yes, but that is not hallucination, that is the training objective.

Also, though not widely used, there are other types of LLMs, e.g. diffusion-based ones, which actually do not use a next token prediction objective and rather iteratively predict parts of the text in multiple places at once (Llada is one such example). And, of course, these models also hallucinate a bunch if you let them.

Redefining a term to suit some straw man AI boogeyman hate only makes it harder to properly discuss these issues.

You’re approaching this from a point where it’s already too late.

If you’re not capable of taking proper care of your pet, don’t get a pet in the first place. Picking up the shit your dog left in a public place is part of owning a dog.

If your kid has a baseball game the next day, don’t go drinking today. That’s the selfish part. Although I would argue if you do get drunk, you kind of just have to deal with it and go to your kids game regardless.

There seems to be a kinda active ryujinx fork, but I agree that the switch emulation scene got decimated by Nintendo’s abhorrent legal practices.

Still, I’m sure it won’t take long after the switch 2 comes out for a working emulator to appear.

Don’t get me wrong, trump sucks, his tariffs are completely stupid, the US is probably fucked on so many levels.

But.

Don’t buy the switch 2. It’s an overpriced piece of shit with even more overpriced games. Honestly just buy a steam deck and run yuzu.

Removed by mod

I think I have some bad news about your ex gf 💀

They probably confused the R1 Qwen distill with something else. Afaik there is no 32b model from DeepSeek directly.

Are you really this braindead or are you just on Xi’s or Putin’s payroll?