Sam Marks leads Anthropic's Cognitive Oversight team, a subteam of Alignment Science. Sam's research focuses on settings where understanding something about ...

Talks from NDIF and Bau Lab at the NeurIPS 2025 Mech Interp Workshop

YouTube video by NDIF Team

Deepti Ghadiyaram is an Assistant Professor at Boston University in the Department of Computer Science, with affiliated appointments in Electrical and Comput...

In this talk, Chris Wendler presents his recent work on using sparse autoencoders for diffusion models. In this work, they train SAEs on SDXL Turbo, finding ...

NDIF is a research computing project that enables researchers and students to crack open the mysteries inside large-scale AI systems.

Do you have a research project where you plan to study many different models? NDIF will soon be deploying model hot-swapping, which will enable users to access any HuggingFace model remotely via NDIF....

09:30 AM - 09:40 AM: Opening Remarks (David Bau) 09:40 AM - 10:00 AM: Keynote 1: Lee Sharkey: "Mech Interp: Where should we go from here?" 10:00 AM - 10:10 A...

About:The New England Mechanistic Interpretability (NEMI) workshop aims to bring together academic and industry researchers from the New England and surround...

We're a research computing project cracking open the mysteries inside large-scale AI systems. The NSF National Deep Inference Fabric consists of a unique combination of hardware and software that provides a remotely-accessible computing resource for scientists and students to perform detailed and reproducible experiments on large pretrained AI models, such as open large language models. We aim to make AI interpretability research more accessible through this channel by publishing lectures and educational content covering real interpretability research.

David Bau is an Assistant Professor of Computer Science at Northeastern University's Khoury College. His lab studies the structure and interpretation of deep...

Contribute to ARBORproject/arborproject.github.io development by creating an account on GitHub.

AI companies don't fully understand how their models work and they aren’t making the training data available to outside researchers.