New audio-processing technology is making entertainment more accessible for millions of viewers.
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.
AGI SVP Rohit Prasad on how to mix your data with Amazon's training data at every stage — deep customization without catastrophic forgetting.
How agentic systems work under the hood — and how AWS’s new AgentCore framework implements their essential components.
Competitive-agent architecture develops security protections at machine speed, reducing weeks of work to hours.
Customer-obsessed science
Research areas
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December 5, 20256 min readA multiagent architecture separates data perception, tool knowledge, execution history, and code generation, enabling ML automation that works with messy, real-world inputs. -
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November 20, 20254 min read
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Featured news
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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Led by David Luan and Pieter Abbeel, the lab will focus on developing new foundational capabilities for enabling useful AI agents.
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CIKM 2024 Workshop on GenAI and RAG Systems for Enterprise2024Security controls are mechanisms or policies designed for cloud-based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls. We specifically focus on generating Gherkin codes
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NeurIPS 2024 Workshop on Time Series in the Age of Large Models2024Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN [12, 6] and MQT [1] overreact to demand peaks by carrying over the elevated PE demand into subsequent Post-Peak-Event (PPE) periods, resulting in significantly over-biased
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NeurIPS 2024 Workshop on Time Series in the Age of Large Models2024Anomaly detection in industrial sensor data is challenging as sensor readings are frequently affected by routine operations, leading to sudden changes that may not indicate actual issues. This makes it difficult to distinguish between normal and anomalous behavior. With a few expert-labeled anomalies, we aim to leverage these sparse labels to improve sensor anomaly detection. Besides the issue of limited
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NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)2024Speculative decoding is a method for accelerating inference in large language models (LLMs) by predicting multiple tokens using a smaller ‘draft model’ and validating them against the larger ‘base model.’ If a draft token is inconsistent with what the base model would have generated, speculative decoding ‘backtracks’ to the last consistent token before resuming generation. This is straightforward in autoregressive
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NeurIPS 2024 Workshop on Time Series in the Age of Large Models2024Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to "obvious" model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surges in customer demand in the lead up to major exogenous events like holidays for clearly
Collaborations
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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.
