Recent LLM forecasters are getting better at predicting the future. But there's a challenge: How can we evaluate and compare AI forecasters without waiting years to see which predictions were right? (1/11)

I am in beautiful Vancouver for #NeurIPS2024 with those amazing folks! Say hi if you want to chat about ML privacy and security (or speciality ☕)

🔥 I'm thrilled that I'll be spending next year in the group of @floriantramer.bsky.social at ETH Zurich, working on privacy and memorization in ML 🔥 (Not an announcement, just what I usually do. It's a really great group full of incredibly amazing people that I am thrilled to work with every day!)

If it's curated by @javirandor.com then you know it's amazing!

Zurich is a great place to live and do research. It became a slightly better one overnight! Excited to see OAI opening an office here with such a great starting team 🎉

Ensemble Everything Everywhere is a defense against adversarial examples that people got quite exited about a few months ago (in particular, the defense causes "perceptually aligned" gradients just like adversarial training) Unfortunately, we show it's not robust... arxiv.org/abs/2411.14834