https://garymarcus.substack.com/p/two-critical-updates-re-as...
As always, PR hype. Goalposts have not moved.
Guys, please use critical thinking. The haters don't hate by default, we hate because we're gaslit about this stuff every day and it's annoying. Extraordinary claims require proof, and they're not giving us information that would be essential to knowing if this is actually significant or not.
Remember October 2024 Pelicans [1] ? It's been only less than 2 years.
We don't know what will come in the next 2 years. But the progress doesn't seem to stop for now.
[1] https://simonwillison.net/2024/Oct/25/pelicans-on-a-bicycle/
People are skeptical of the announcement because the room include several PHDs in math and physics. The prompts are not published so we can see how generic the starting prompt is.
If I could take one out of context quote from this whole thread as a response to TFA, it would be this one.
I've been analyzing thousands of x86 traces and the AI made sense of them at a scale no previous human or algorithm could do before - I've been reading assembly everyday for half of my life. I'm doing prototypes in minutes what would take hours or days before. AI is answering questions in such a precise way in so many domains, learning something has never been easier.
This is "just ok" for you ? What do you need exactly ? god omniscience ? come on, can't we just appreciate for what it is and be excited about the future ?
He literally says it's an impressive feat in the second article.
That is the intended purpose of hype.
He's polite, but he's basically saying there's potentially a lot of smoke and mirrors. I agree.
I remember the time when he insisted that diffusion-based image generators trained on Internet scale data will never be able to make an image of a horse riding an astronaut. Today you can generate 4K video of that.
A lot of his predictions have held up very well.
The only way that is PR hype is if you're invoking the insane conspiracy that frontier AI labs are just buying off results that would otherwise be career defining for a mathematician, just for marketing.
The posts you linked are urging caution regarding the exaggerated e/acc-esque lies peddled by people like Musk, not that the models haven't proven themselves as having genuine ability to contribute to research in some areas.
They still do things that I find incredibly annoying and “dumb”. And I still have to clean up messes they make quite often.
But on the whole they are clearly smarter than before. No extraordinary claims needed. I just try to learn how the tool works and how to use it effectively.