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.
To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor.
Reasoning models can generate seven to 10 times as many tokens as necessary on simple tasks, creating unsustainable costs at scale. Amazon's vision for metacognitive AI could fundamentally shift how models allocate computational resources.
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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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Hosted by Dr. 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.
Meet the 63 Amazon Research Award (ARA) recipients, who represent 41 universities in 8 countries.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2024Prompt-based learning has been demonstrated as a compelling paradigm contributing to large language models’ tremendous success (LLMs). Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning. In this work, we tackle the problem of training a robot to understand multimodal prompts, interleaving vision signals with text descriptions
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NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)2024Large language models (LLMs) have achieved remarkable performance on various natural language processing tasks, but training LLMs at scale is extremely resource-intensive, requiring substantial computational power, memory, and energy consumption. This has motivated research into efficient training methods, particularly during the pre-training phase. There are two main approaches to approximate full-rank
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2024Online paging is a fundamental problem in the field of online algorithms, in which one maintains a cache of 𝑘 slots as requests for fetching pages arrive online. In the weighted variant of this problem, each page has its own fetching cost; a substantial line of work on this problem culminated in an (optimal) 𝑂(log 𝑘)-competitive randomized algorithm, due to Bansal, Buchbinder and Naor (FOCS’07). Existing
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RecSys 2024 Workshop on Causality, Counterfactuals & Sequential Decision-Making (CONSEQUENCES)2024Modern e-commerce services frequently target customers with incentives or interventions to engage them in their products such as games, shopping, video streaming, etc. This customer engagement increases acquisition of more customers and retention of existing ones, leading to more business for the company while improving customer experience. Often, customers are either randomly targeted or targeted based
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MLNextG242024In wireless communications, collaborative spectrum sensing is a process that leverages radio frequency (RF) data from multiple RF sensors to make more informed decisions and lower the overall risk of failure in distributed settings. However, most research in collaborative sensing focuses on homogeneous systems using identical sensors, which would not be the case in a real world wireless setting. Instead
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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.
