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July 10, 20265 min readHydroShear, a new physics-based simulator, teaches robots how to use their sense of touch to perform complex manipulation tasks, in a way that transfers seamlessly to the real world. -
July 9, 202610 min read -
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Featured news
Hosted by Danielle Perszyk, cognitive scientist at Amazon, the podcast features researchers tackling the hardest problems in agentic AI — from building reliable perception systems to designing training environments that mirror human learning.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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ICML 2026 Workshop on Deep Learning for Code (DL4C)2026Recent agentic approaches to LLM-based kernel generation have achieved strong results on CUDA, yet emerging AI accelerators such as AWS Trainium and Inferentia remain unaddressed. Writing kernels for these chips via the Neuron Kernel Interface (NKI) is particularly challenging due to a multi-engine architecture, tile-based programming with a fixed 128-element partition dimension, and explicit memory management
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2026Large language models can memorize information that must be removed–ranging from copyright-sensitive content (e.g., book chapters) to personally identifiable information (e.g., income)–to ensure responsible and compliant behavior. Unlearning has emerged as an efficient alternative to full retraining, aiming to remove specific knowledge. However, users may still expect model to leverage the removed information
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2026E-commerce assistants must go beyond product search to support idea inspiration, criteria formation, comparison, and tool-grounded fact-checking over non-linear shopping journeys. Teaching these behaviors into deployable latency-constrained models is bottlenecked by post-training data: trajectories must cover the full agentic workflow with diversity and fidelity, yet desired outputs are open-ended (often
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2026Large-scale AI evaluation increasingly relies on aggregating binary judgments from K annotators, including LLMs used as judges. Most classical methods, e.g., Dawid-Skene or (weighted) majority voting, assume annotators are conditionally independent given the true label Y ∈ {0, 1}, an assumption often violated by LLM judges due to shared data, architectures, prompts, and failure modes. Ignoring such dependencies
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ACM FAccT 20262026Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in 'rich-get-richer' dynamics and a homogenization of visible content, but can also lead to misalignment of recommendations with individual users' preferences for popular or niche content. This work studies popularity bias through the lens of user-recommender
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Gretchen Ertl
The program offers unrestricted funds and other resources to support research at academic institutions and non-profit organizations in areas that align with our mission.
A global university competition to drive secure innovation in generative AI technology, which focuses on responsible AI and large language model coding security.
Credit: Wolfram Scheible
We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
Courtesy of Pai-Ling Yin
We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
