I think that's exactly the standpoint of Yann LeCun.
> The progress using narrowly intelligent algorithms to drive cars in a generalizable way was not as fast as the early gains in places the narrow algorithms could solve, thus the progress in using generally intelligent algorithms for any other field must also slow down.
Man, is this how 99% of the population works? They have a heuristic for anything that sounds computer sciencey and apply it for anything and everything that sounds similar without bothering to check if their heuristic actually applies?
> I’m reminded of
They were drawing a comparison, not saying "x will definitely happen because y"
The breathless astroturf articles, the brain-dead talking head conversations, the endless flood of obvious misinformation about industry uptake, the nauseating LinkedIn puff-pieces about PREPARING YOUR ENTERPRISE OR ELSE... it's a hostile information economy at best, and its being driven by the same people who brought you bullshit like self-driving, crypto and the Metaverse... for the exact same reasons (to get rich).
There's no epistemic credibility left in the tech industry in general. Maybe the Dario's or whoevers of the world believed what they were talking about at one point, but I don't believe a single word I read from Anthropic or OpenAI for the same reason I don't believe anything I read from Russian state media. I know it's being produced for reasons that have nothing to do with the truth.
As someone who interacts with the technology on a daily basis at a non-trivial level... I'm not seeing anything that would cure my skepticism about GenAI. The information economy surrounding it is broken. The fundamentals don't line up. The hype about "nearby capabilities" is identical to the hype of generations after generations of hype in the AI space (dating at least back to Turing). It smells like a bad bet.
I’m in the process of dumping codex for more open harnesses that don’t add problematic new features every week. Because Anthropic insists that I have to use their harness with the subscription, eventually I’m either going to wrap `claude -p` to get away from the crappy TUI or else I’ll just unsubscribe and use other models. Their bad UX has alienated me as a paying customer.
Listed a few in my other comment
Like of course codex is the most powerful and "just" run `npx blah blah` but no, I'm not running randomly piped code on my personal computer and even if I didn't care about privacy it means I can only run this locally with my laptop on. "leave your laptop on and access from your phone" is not a good idea. It only adds to the surface area of confusion. Ux no good.
Went too far? Oops, it's not accessible from the Claude Code UI anymore! You can get the agent to dredge up the info from the session logs, but you'd better hope they put the pieces together correctly.
Also, you can't rewind to a steering message. Want to make sure you can rewind to it? Wait for the agent to settle, or stop them, enter your message, and hope you don't throw them off their current train of thought.
To see a proper rewind UI, see how Pi does it: Every message is available, forked conversation history is preserved. It has its own issues, but is miles ahead of Claude Code in this regard.
And so many more. I tried codex and it was lacking in many UX aspects. Remember we got from a text-paragraph-only terminal to this at breakneck speed.
Eg - I asked for a recipe and it says leave x for y time - there's actually deeplinks that link to your clock timer with the time preset! And to change quantities, there's input boxes in the response that update all quantities accordingly.
Many such cases of use-case specific UX
This is partly due to the multiplicative scaling factor -- the number of tokens; the number of transformer layers; the number of features; etc.
I suspect that for models to have a step upgrade either/both of 1) a new/improved architecture model; and 2) better training of smaller parameter models are needed. I say the latter as I think that with the larger weights there is a lot of unused/redundant parameters.
https://finance.yahoo.com/sectors/healthcare/articles/godfat...
Progress is spiky and not guaranteed. The capital costs have also ballooned to the point where the rate has to level out
That's a pretty bold use of "has to". None of the financing behind AI seems sane, yet it continues to defy rational expectations. Reminds me of the comment "the market can remain irrational longer than you can stay solvent". It's not the exact meaning but has the same feeling. The circular financing going on does not help anything either
I think newer models are more likely to produce the usuable output, almost always, but its still not great. The frequency in which you get usable results leads one to believe that quality is improving.. No usability is improving but the result is still meh.
Also put an llm in an environment without an lsp or compiler to check against and let me know how capable you think they are on their own. The "loop" is doing a lot of heavy lifting here.
We don't see any indication that the LLM companies are slowing down and go for better code.
Instead it just looks like a cash grab until the AI bubble bursts.