David Bau (@davidbau) 's Twitter Profile
David Bau

@davidbau

Computer Science Professor at Northeastern, Ex-Googler. Believes AI should be transparent. @[email protected] @davidbau.bsky.social baulab.info

ID: 19357631

linkhttps://baulab.info calendar_today22-01-2009 20:03:10

357 Tweet

4,4K Followers

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MIT CSAIL (@mit_csail) 's Twitter Profile Photo

As AI models become more powerful, auditing them for safety & biases is crucial — but also challenging & labor-intensive. Can we automate and scale this process? MIT CSAIL researchers introduce "MAIA," which iteratively designs experiments to explain AI systems' behavior:

As AI models become more powerful, auditing them for safety & biases is crucial — but also challenging & labor-intensive. Can we automate and scale this process?

MIT CSAIL researchers introduce "MAIA," which iteratively designs experiments to explain AI systems' behavior:
David Bau (@davidbau) 's Twitter Profile Photo

Very excited to see the launch of applied AI interpretability startup @goodfireAI by Tom McGrath and Eric Ho and Dan Balsam. The ndif.us team is collaborating with their technical efforts, which take advantage of the nnsight.net API.

Koyena Pal @ VLDB 2024 (@kpal_koyena) 's Twitter Profile Photo

A huge thank you to everyone who made #NEMI2024 a success! 🎉It was an honor to be the student lead organizer for this dynamic event, with insightful discussions and great networking. Special thanks to David Bau, Max Tegmark, Jannik Brinkmann, Kenneth Li, and Eric J. Michaud! #AI

A huge thank you to everyone who made #NEMI2024 a success! 🎉It was an honor to be the student lead organizer for this dynamic event, with insightful discussions and great networking. Special thanks to <a href="/davidbau/">David Bau</a>, <a href="/tegmark/">Max Tegmark</a>, <a href="/BrinkmannJannik/">Jannik Brinkmann</a>, <a href="/ke_li_2021/">Kenneth Li</a>, and <a href="/ericjmichaud_/">Eric J. Michaud</a>! #AI
Aaron Mueller (@amuuueller) 's Twitter Profile Photo

Thanks Tal! 📜 In this paper, we provide a theoretically grounded review of causal (which, imo, ⊇ mechanistic) interpretability. We argue that this gives a more cohesive narrative of the field, and makes it easier to see actionable open directions for future work! 🧵

Jaden Fiotto-Kaufman (@jadenfk23) 's Twitter Profile Photo

🚀 New NNsight features launching today! If you’re conducting research on LLM internals, NNsight 0.3 is now available. This update introduces advanced features, offering deeper insights for complex investigations into model behavior. 👇 Here’s what’s new: colab.research.google.com/github/ndif-te…

🚀 New NNsight features launching today! If you’re conducting research on LLM internals, NNsight 0.3 is now available. This update introduces advanced features, offering deeper insights for complex investigations into model behavior.

👇 Here’s what’s new: colab.research.google.com/github/ndif-te…
David Bau (@davidbau) 's Twitter Profile Photo

What is the goal of interpretability in AI? I spoke a bit about this at the recent far.ai alignment workshop: youtube.com/watch?v=AIfmSx… The event had excellent talks from many others, worth a view, linked in the thread.

David Bau (@davidbau) 's Twitter Profile Photo

"As someone who develops against LLMs, interpretability and transparency are everything to me" OpenAI is strangling their own ecosystem. Any AI companies that are serious about building a platform need to understand that transparency is the oxygen that sustains innovation.