I happen to use it on a daily basis. 4.6-opus-high to be specific.
The other day it surmised from (I assume) the contents of my clipboard that I want to do A, while I really wanted to B, it's just that A was a more typical use case. Or actually: hardly anyone ever does B, as it's a weird thing to do, but I needed to do it anyway.
> but it is indistinguishable from actual reasoning
I can distinguish it pretty well when it makes mistakes someone who actually read the code and understood it wouldn't make.
Mind you: it's great at presenting someone else's knowledge and it was trained on a vast library of it, but it clearly doesn't think itself.
The suggestion it gave me started with the contents of the clipboard and expanded to scenario A.
Being honest; I probably have to write some properly clever code or do some actual design as a dev lead like… 2% of my time? At most? The rest of the code related work I do, it’s outperforming me.
Now, maybe you’re somehow different to me, but I find it hard to believe that the majority of devs out there are balancing binary trees and coming up with shithot unique algorithms all day rather than mangling some formatting and dealing with improving db performance, picking the right pattern for some backend and so on style tasks day to day.
It is true that models can happen to produce a sound reasoning process. This is probabilistic however (moreso than humans, anyway).
There is no known sampling method that can guarantee a deterministic result without significantly quashing the output space (excluding most correct solutions).
I believe we'll see a different landscape of benefits and drawbacks as diffusion language models begin to emerge, and as even more architectures are invented and practiced.
I have a tentative belief that diffusion language models may be easier to make deterministic without quashing nearly as much expressivity.
Citation needed.
I am sure the output of current frontier models is convincing enough to outperform the appearance of humans to some. There is still an ongoing outcry from when GPT-4o was discontinued from users who had built a romantic relationship with their access to it. However I am not convinced that language models have actually reached the reliability of human reasoning.
Even a dumb person can be consistent in their beliefs, and apply them consistently. Language models strictly cannot. You can prompt them to maintain consistency according to some instructions, but you never quite have any guarantee. You have far less of a guarantee than you could have instead with a human with those beliefs, or even a human with those instructions.
I don't have citations for the objective reliability of human reasoning. There are statistics about unreliability of human reasoning, and also statistics about unreliability of language models that far exceed them. But those are both subjective in many cases, and success or failure rates are actually no indication of reliability whatsoever anyway.
On top of that, every human is different, so it's difficult to make general statements. I only know from my work circles and friend circles that most of the people I keep around outperform language models in consistency and reliability. Of course that doesn't mean every human or even most humans meet that bar, but it does mean human-level reasoning includes them, which raises the bar that models would have to meet. (I can't quantify this, though.)
There is a saying about fully autonomous self driving vehicles that goes a little something like: they don't just have to outperform the worst drivers; they have to outperform the best drivers, for it to be worth it. Many fully autonomous crashes are because the autonomous system screwed up in a way that a human would not. An autonomous system typically lacks the creativity and ingenuity of a human driver.
Though they can already be more reliable in some situations, we're still far from a world where autonomous driving can take liability for collisions, and that's because they're not nearly as reliable or intelligent enough to entirely displace the need for human attention and intervention. I believe Waymo is the closest we've gotten and even they have remote safety operators.
I'm not sure if I'm up to date on the latest diffusion work, but I'm genuinely curious how you see them potentially making LLMs more deterministic? These models usually work by sampling too, and it seems like the transformer architecture is better suited to longer context problems than diffusion