New research indicates that AI agents learn compressible models of data, which don’t have enough space to enable memorization.
How the Verus "program verifier", which automatically checks code against a mathematical specification of its functionality, helps increase security assurance in software projects.
Ten years after we founded the Automated Reasoning Group, mathematical logic has moved from academic research into production services that secure millions of customer workloads — demonstrating that systems can be provably correct, not just probably correct.
PatientAgentBench generates a synthetic patient health record, a realistic clinical vignette, and a patient agent that converses with the AI system under evaluation, to capture what a patient-facing agent actually has to do.
A new chiplet architecture, custom die-to-die connectivity, and support for DDR5-8800 memory and the latest PCIe gen6 interconnects improve performance by 25% for general-purpose and agentic AI workloads.
Customer-obsessed science
Research areas
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September 21, 202611 min readThree new papers from Amazon Bio Discovery address bottlenecks in AI-driven antibody engineering, from benchmarking binding predictors to experimentally validating de novo design. -
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August 21, 20269 min read
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July 30, 20268 min read
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The Build on Trainium program is a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.
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 20232023Policy Optimization (PO) is one of the most popular methods in Reinforcement Learning (RL). Thus, theoretical guarantees for PO algorithms have become especially important to the RL community. In this paper, we study PO in adversarial MDPs with a challenge that arises in almost every real-world application – delayed bandit feedback. We give the first near-optimal regret bounds for PO in tabular MDPs, and
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ACL 2023 Workshop on Lexical and Computational Semantics and Semantic Evaluation2023We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MULTICONER 2).1 Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MULTICONER V2 dataset, composed of
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STOC 20232023We consider the problem of online service with delay on a general metric space, first presented by Azar, Ganesh, Ge and Panigrahi (STOC 2017). The best known randomized algorithm for this prob-lem, by Azar and Touitou (FOCS 2019), is 𝑂 (log2 𝑛)-competitive, where 𝑛 is the number of points in the metric space. This is also the best known result for the special case of online service with deadlines, which
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ECIR 20232023Graph Convolutional Networks have recently shown state-of-the-art performance for collaborative filtering-based recommender systems. However, many systems use a pure user-item bipartite interaction graph, ignoring available additional information about the items and users. This paper proposes an effective and general method, TextGCN, that utilizes rich textual information about the graph nodes, specifically
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KDD 2023 International Workshop on Mining and Learning from Time Series (MileTS)2023We introduce a new distribution-free multi-horizon forecast. As such, it integrates product-level forecast at fixed lead times, spans, and quantiles with the ability for a user to request a forecast at any other lead time, span or quantile via piecewise linear interpolation with exponential extrapolation for the tails of the distribution. The selected algorithm leads to a reduction in weighted quantile
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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.
