Upgraded the models for Muse and Meta Al, bringing Active Memory and Active Focus from Muse Wonder to everyone. In Muse, these new specialized models are 2.4x less likely to forget critical info and are 67% better at self correcting and completing everyday tasks which means less repeating yourself and fewer dead ends.View quoted post on X
More on the models: research.meta.ai/blog/specializ…
Introducing Voice. Talk to your Muse and Muse talks back, so you can go from “what should I cook?” to cooking, just like that ✨View quoted post on X
It’s almost incomprehensible how wide the distance is between someone who’s working at the frontier with AI and basically everyone else right now.View quoted post on X
The crazy thing is these lines are not converging, but only growing farther apart. If the curve of AI progress had leveled off, it would certainly have given most people and companies time to catch up or implement their workflows in a standardized way. The technology would have neutralized competitive advantage because everyone would be using it the same way.
But what’s happened is the opposite. The curve of AI progress is basically going vertical right now, which means if you had codified your processes 3-6 months ago you’re likely already behind again. Now imagine most companies that kicked into gear a year ago and thought that they were done. Even further behind now.
I’ve never seen a technology in my life and career that improves at this rate, which consequently means you need to upgrade your expectations at a similar rate. Our brains weren’t wired for this, and our organizational change management processes certainly weren’t either.
With any form of recursive self improvement, things just get wilder. It’s a time when you will need to be constantly reevaluating the things that didn’t work just a couple months ago to see if they now can be solved. AI native companies certainly have a strong advantage due to this right now, so now it comes down to can the bigger companies keep up. Wild times.
Claim: Humans & AI are better together if we want to recursively (co-)improve to a brighter future for *humanity*
Instead of racing towards methods for AI replacing us, work on how to make AI work *with* us!
New paper that makes 4 contributions here:
1) The role of humans in directing research agents: Autobenchmark
2) Reward hacking in autoresearch agents: HackBench
3) Training agents to interact with humans: AutoInteract
4) Training agents to communicate with humans: unslopping AI
Full Paper: arxiv.org/abs/2610.38882
Blog posts for each section:
1) facebookresearch.github.io/RAM/blogs/auto…
2) facebookresearch.github.io/RAM/blogs/hack…
3) facebookresearch.github.io/RAM/blogs/auto…
4) facebookresearch.github.io/RAM/blogs/unsl…View quoted post on X
Meet Lor.
We’re building Lor to do the work of an entire AI lab. Today, its agents write our code, run experiments, and iterate on the results for days, on any machine, hundreds at a time.
Now in private beta ↓View quoted post on X
