DataRobot
APRIL 21, 2022
They are used for different applications, but nonetheless they suggest that the development in infrastructure (access to GPUs and TPUs for computing) and the development in deep learning theory has led to very large models. The natural follow-up question is if this increase in computing requirements has led to an increase in accuracy.
Stanford AI Lab Blog
DECEMBER 6, 2021
We’re excited to share all the work from SAIL that’s being presented at the main conference , at the Datasets and Benchmarks track and the various workshops , and you’ll find links to papers, videos and blogs below. Altman, Ron O.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Stanford AI Lab Blog
APRIL 25, 2022
Feel free to reach out to the contact authors directly to learn more about the work that’s happening at Stanford!
The AI Alignment Forum
FEBRUARY 7, 2025
Interpretability benchmarks: Wed like to support more benchmarks for interpretability research. A benchmark should consist of a set of tasks that good interpretability methods should be able to solve. Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark by Pan et al. by Liu et al.
Google Research AI blog
NOVEMBER 28, 2022
Ruoxi Sun , Hanjun Dai , Adams Yu Drawing Out of Distribution with Neuro-Symbolic Generative Models Yichao Liang, Joshua B.
The AI Alignment Forum
MARCH 28, 2025
In this episode, I speak with Jason Gross about his agenda to benchmark interpretability in this way, and his exploration of the intersection of proofs and modern machine learning. Or according to our singular learning theory friends, the local learning coefficients should be small and that implies this thing about this.
Expert insights. Personalized for you.
145 
Let's personalize your content