Last time I asked AI something, it started its response with "Yes, it's certainly possible to x with y." and closed its response with "in conclusion, unfortunately, it's not possible to x with y". In the same session, it told me one must press shift to get the number 1. I'm simultaneously amazed at its ability to generate what it can and disappointed at how it falls short so routinely. It'll get there eventually I'm sure, but I'm pretty dubious when people say they get a lot of value out of it.
People are used to Googling something and reading whole threads about how something can/can't be done before an actual sound conclusion, how to do something but it's actually completely wrong, and other similar behaviors so it's not really that big a leap to do the same with an LLM giving you a single response.
That said, single pass LLMs tend to do this kind of thing but a lot of the more useful things are best done on chain of thought type ones, where they are given some time to reflect on options before they have to start generating the start of the final response.
Those comment sections have dates, sometimes even version numbers, and often caveats like "this worked for me, don't know if it will for you" or "it'll work until the next update". During my interactions with LLMs so far none of them offered any if these caveats or specifics about what version of the software this will work in, even when asked explicitly.
People keep saying "it will get there eventually," or some variety thereof, and I just gotta keep reminding - that is an as-yet unproven claim. It may never get there! Just because we've seen some pretty astounding leaps in capabilities so far, _does not mean_ that we will continue to see them, nor that we'll ever hit the fabled land of "no more mistakes," or hell, even "no more obvious mistakes."
I'm not saying I think it won't (though I _suspect_ it won't), I'm just saying we don't have any actual proof that it _will,_ we're all just running on assumptions right now.
To me, I see LLMs as the same type of revolution that dragon naturally speaking was to voice to text.
A huge leap forward over existing models, but we've spent the last two (three?) decades trying to close the remaining gap left by dragon in the voice to text problem space, and haven't much progress to show.
I think LLMs are likely to be like that. They are a huge jump over previous models of NLP, but I don't see them improving enough to matter to indicate they'll ever make it to AGI
The limitation lies in Transformer architecture itself, therefore AGI was never a possibiblity.
It's wonderful, but not miraculous; And that's okay.
At this point big techs are just milking the hype of what already reached it's boundaries.
I dunno I'm confident enough to say transformer based architectures have really reached their boundaries quite yet but I would agree the hype milks a bit past where it seems transformers alone will take things.
> but we've spent the last two (three?) decades trying to close the remaining gap left by dragon in the voice to text problem space, and haven't much progress to show.
Who says it's possible to close the gap? Humans are certainly not capable of doing perfect speech-to-text. You can sit someone down with a song recording and the ability to replay it as much as they want and there's no guarantee they'll ever be able to tell you what the lyrics are.
