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Nature review: Scientific discovery in the age of artificial intelligence go.nature.com/45fUazH

"Deep Multimodal Fusion for Surgical Feedback Classification" by Rafal Dariusz Kocielnik, Elyssa Wong, Timothy Chu, Lydia Lin, Jiayun Wang, De-An Huang, Anima Anandkumar, Andrew Hung

🚀New AI Alert! Introducing BiasTestGPT, a user-friendly tool for detecting and analyzing social biases in Pretrained Language Models. It uses ChatGPT to generate diverse test sentences, enabling deeper insights into social biases in LLMs. 🔍BiasTestGPT stands out for its ability

#NeurIPS2023 dinner with my team and collaborators. Great to get everyone together after an energizing week!

🏆 Our paper "Deep Multimodal Fusion for Surgical Feedback Classification" won the best proceedings paper at ML4H! 🎉 tinyurl.com/deep-surgical-… We're revolutionizing #SurgicalTraining by automating feedback analysis using #MachineLearning. We've developed the first #AI

Our paper “Challenges in moderating disruptive player behavior in online competitive action games” together with #activision unveils novel insights about challenges in this area #OnlineGaming #GameModeration R. Michael Alvarez Prof. Anima Anandkumar Grant Cahill frontiersin.org/articles/10.33…

Excited to speak at the USC ISI seminar tomorrow on "Human-AI Interaction: From Supporting Surgical Training to Inspecting Social Bias in LLMs." Thank you for the invitation! isi.edu/events/4976/hu…

A number of my colleagues Caltech and I have put together a letter voicing our concern regarding CA SB 1047 (AI Safety Act). Sign the letter and show your support! docs.google.com/forms/d/e/1FAI… We call on @caltech students, postdocs, faculty, staff, and alumni to sign but also leave

Rafal Kocielnik and I are about to present some of our research with Activision! At Stanford University Trust and Safety Conference.

Thank you Gavin Newsom for vetoing SB 1047. We wrote a letter with many Caltech signatories laying down clear reasons for why this bill is flawed. We should be focused on regulating downstream applications rather than AI training. There are already many existing laws doing so

We are excited to share our latest work on debiasing large language models, accepted to COLM 2024 We introduce a lightweight, simple pipeline using ChatGPT to generate synthetic data for debiasing open-source LLMs through parameter-efficient fine-tuning. Our method outperforms

Election misinformation is rampant. Want to learn what the science says about dealing with election misinformation? Check out our guidelines for Dealing with Election Misinformation. #PreventElectionMisinformation youtu.be/oa4DL-q1_YU

Sequential Monte Carlo🤝Diffusion-based Samplers We present a simple yet powerful way of combining SMC with neural SDEs for sampling problems: Sequential Controlled Langevin Diffusions (SCLD) Exciting work led by Junhua Chen who is also currently looking for PhD positions! See🧵

Join us at the NeurReps workshop this Saturday to hear about geometry, symmetries, diffusion models, representations, brain, and world models. NeurIPS Conference

Excited to share that our paper, "Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment," won a Best Paper Award 🏆 at #ML4H2024! Join us on Monday (12/16) at ML4H for our spotlight talk at 1:50pm and poster session at 2:15pm. We propose a

Please check our paper, “Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment,” at #ML4H2024! (Poster #27 at ) We propose a novel framework for detecting teaching interactions in the operating room from raw recordings, achieving an F1 of

Our latest paper on "Human AI collaboration for unsupervised categorization of live surgical feedback" has arrived at Nature Digital Medicine npj Digital Medicine Led by our postdoc Rafal Kocielnik @caltech this paper explores how live surgical feedback in the OR can be evaluated by AI