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Challenges in Deployable Generative AI
Swami Sankaranarayanan · Thomas Hartvigsen · Camille Bilodeau · Ryutaro Tanno · Cheng Zhang · Florian Tramer · Phillip Isola

Fri Jul 28 12:00 PM -- 08:00 PM (PDT) @ Meeting Room 313
Event URL: https://deployinggenerativeai.github.io/ »

Generative modeling has recently gained massive attention given high-profile successes in natural language processing and computer vision. However, there remain major challenges in deploying generative models for real-world impact in domains like healthcare and biology. This is a challenging agenda that requires collaboration across multiple research fields and industry stakeholders. This workshop aims to advance such interdisciplinary conversations around challenges in deploying generative models – the lessons learned by deploying large language models could be impactful for high stakes domains like medicine and biology. Specifically, we will solicit contributions that prioritize (1) Multimodal capabilities in generative modeling, (2) Deployment-critical features in generative models such as Safety, Interpretability, Robustness, Ethics, Fairness and Privacy, and (3) Human facing evaluation of generative models. The topic of generative modeling is extremely relevant to the core audience of ICML. Modern generative models impact several fields outside machine learning and hence responsible deployment of such powerful algorithms has become a major concern of researchers in academia and industry alike. ICML, being the flagship conference of Machine learning, is the perfect place to facilitate this cross disciplinary sharing of knowledge.

Author Information

Swami Sankaranarayanan (Massachusetts Institute of Technology)
Thomas Hartvigsen (MIT)
Camille Bilodeau (University of Virginia)

Dr. Bilodeau is currently an assistant professor in Chemical Engineering at the University of Virginia. She received her B.S. and M.S. from Northwestern University and her Ph.D. from Rensselaer Polytechnic Institute, both in Chemical and Biological Engineering. During her Ph.D., she received the Lawrence Livermore Advanced Simulations and Computation Graduate Fellowship, through which she carried out research at Lawrence Livermore National Laboratory. Her research explores the intersection between artificial intelligence and molecular simulations with the goal of designing new molecules and materials.

Ryutaro Tanno (DeepMind)
Cheng Zhang (Microsoft Research, Cambridge)
Florian Tramer (ETH Zürich)
Phillip Isola (MIT)

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