Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

OpenAI made computer use 10x faster in a year. Tibo breaks down what changed under the hood: “It’s not that we made the model sample faster. It’s both the harness and the model.” “How much does it need to think before it can take the next action reliably? That’s...

46,224 Aufrufe • vor 9 Tagen •via X (Twitter)

3 Kommentare

Profilbild von Kyle Jeong
Kyle Jeongvor 9 Tagen

@thsottiaux

Profilbild von 小森
小森vor 9 Tagen

@thsottiaux Faster is nice. The part I care about is the think-before-click budget—hard approval queue for money/auth, or just shave tokens until it guesses wrong once?

Profilbild von LucasH Sketch
LucasH Sketchvor 9 Tagen

@thsottiaux a second agent babysitting the first agent is such a funny solution but hey, if it works it works

Ähnliche Videos

Alexandr Wang, Meta's Chief AI Officer, on why Meta can no longer simply open-source its frontier model: As part of standing up Meta Superintelligence Labs, the team rewrote its internal risk doctrine. "One of the things that we did as part of Meta Superintelligence Labs is we updated our what we call our advanced AI scaling framework which is really our view of what are the risks that we see in developing these very powerful models and how do we want to handle those risks as we see them in early testing." They then ran their frontier model through it, and published what came back. "We published a lot of what we saw in the process of training Muark in our preparedness report and some of the things that we saw is that it actually triggered some high risk areas in the course of early training particularly around biorisk but also a number of the risks were elevated." The trigger came during early training, well before launch or red-teaming. Biorisk was the standout, with several other categories rising alongside it. Alexandr Wang is clear this isn't specific to Meta: "This is something I think the entire industry has seen as the models have improved pretty dramatically over the past year so we certainly aren't the only ones to see a host of these risks show up as we scaled up the models and as we sort of kept pushing the frontier of research." Which brings him to the real fork in the road: the difference between shipping a model inside a product and handing out the weights. "When we launched a model like New Spark in a product, we have a lot of ways to mitigate some of these risks and ensure that we're able to launch it in a safe and responsible way. It's much harder to do that when you open source a model and people can use that model in all sorts of contexts that we may not have full understanding of." A product is a controlled surface. You can filter, monitor, rate-limit, patch and revoke. An open-weights release is a one-way door: once the file is out, the deployment context and the mitigations both stop being yours. So Meta is building something different for release: "So we're in the process right now of developing models that we believe are fit and safe to be open source while still maintaining as much of the performance capabilities as possible."

Big Brain AI

16,374 Aufrufe • vor 2 Monaten