As AI models grow larger, they become less insightful, not more. To ensure that they continue to learn, we need to reduce their inference time.
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.
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February 2, 202610 min readEvery 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. -
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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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2023 Conference on Digital Experimentation @ MIT (CODE@MIT)2023Online A/B tests have become an indispensable tool across all the technology industry: if performed correctly, “online” experiments can inform effective decision making and product development. It should therefore not be surprising that Gupta et al. [2019] estimates that online businesses alone collectively run hundreds of thousands of experiments annually. Modern online experiments are often run in marketplaces
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NeurIPS 2023 Workshop on Synthetic Data Generation with Generative AI2023Recent advancements in generative models have led to significant improvements in the quality of generated images, making them virtually indistinguishable from real ones. However, using AI generated images for training robust computer vision models for real-world applications, especially object detection in road scene perception, is still a challenge. AI generated images usually lack the required diversity
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EMNLP 20232023Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However, biases can manifest differently across various languages for individual attributes. As a result, it is critical to examine biases within each language and attribute. Of equal importance is to study how these biases compare across
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WACV 2024 Workshop on Image/Video/Audio Quality in Computer Vision and Generative AI2023The quality of recorded videos and images is significantly influenced by the camera’s field of view (FOV). In critical applications like surveillance systems and self-driving cars, an inadequate FOV can give rise to severe safety and security concerns, including car accidents and thefts due to the failure to detect individuals and objects. The conventional methods for establishing the correct FOV heavily
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ICSE 20232023Rust has risen in prominence as a systems programming language in large part due to its focus on reliability. The language’s advanced type system and borrow checker eliminate certain classes of memory safety violations. But for critical pieces of code, teams need assurance beyond what the type checker alone can provide. Verification tools for Rust can check other properties, from memory faults in unsafe
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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.
