How do you match AI warnings about future events with what actually happens? A KDD test-of-time award recognizes a 2014 approach that modern systems still need.
In-context learning enables a model that can solve forecasting tasks with an arbitrary number of dimensions in a zero-shot manner.
The pandemic turbo-charged retail growth — teams of scientists at Amazon forged a path forward to handle the scale.
Every NFL game generates millions of tracking data points from 22 RFID-equipped players. Seventy-five machine learning models running on AWS process that data in under a second, transforming football into a sport where every movement is measured, modeled, and instantly analyzed.
"Reinforcement learning gyms" train agents on the many low-level tasks that they must chain together to execute customer requests.
Customer-obsessed science
Research areas
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January 13, 20267 min readLeveraging existing environment simulators and reward functions based on verifiable ground truth boosts task success rate, even with small models and small training datasets. -
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December 29, 20256 min read
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Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
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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2026Visual compatibility recommendation systems aim to surface compatible items (e.g. pants, shoes) that harmonise with a user-selected product (e.g., shirt). Existing methods struggle in three key aspects: they rely on global CNN representations that overlook fine-grained local cues critical for visual pairing; they force all categories into a single latent space, ignoring the fact that compatibility rules
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CHIIR 20262026Taxonomies organize knowledge into hierarchical structures that support effective information seeking behaviors. However, developing taxonomies in fast-evolving domains like e-commerce remains a labor-intensive process. In this paper, we present an interactive system that assists users in expanding taxonomies through automated knowledge discovery from large text corpora. On the back end, our hybrid methods
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2026Claim verification is a core component of automated fact-checking systems, aimed at determining the truthfulness of a statement by assessing it against reliable evidence sources such as documents or knowledge bases. This work presents KG-CRAFT, a method that improves automatic claim verification by leveraging large language models (LLMs) augmented with contrastive questions grounded in a knowledge graph
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ECIR 20262026Composing messages in chatbot interactions is often time-consuming, making autocompletion an appealing way to reduce user effort. Different users have different preferences and therefore different expectations from autocompletion solutions. We study how personalization can improve the autocompletion process, evaluating four schemes defined along two axes: generation vs. ranking, and prior messages vs. external
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2026Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where irrelevant or redundant passages dominate. Current methods-fixed top-k retrieval, cross-encoder reranking, or policybased iteration-depend on static heuristics or costly reinforcement learning, failing to assess evidence sufficiency, detect subtle mismatches, or reduce redundancy, leading to hallucinations and poor grounding
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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.
