Trained on millions of hours of data from Amazon fulfillment centers and sortation centers, Amazon’s new DeepFleet models predict future traffic patterns for fleets of mobile robots.
A new philosophy for developing LLM architectures reduces energy requirements, speeds up runtime, and preserves pretrained-model performance.
Amazon VP and distinguished scientist Byron Cook explains how AWS's new Automated Reasoning checks address key challenges in automated reasoning: translating natural to structured language, defining truth, and definitive reasoning.
Using ensembles of agents to generate and refine interactions annotated with chains of thought improves performance on a battery of benchmarks by an average of 29%.
Language models inspired by specialized processing regions in the brain offer significant time and cost savings.
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July 29, 2025New cost-to-serve-software metric that accounts for the full software development lifecycle helps determine which software development innovations provide quantifiable value.
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Created for students keen to build their career in machine learning, the fifth edition of the program is now open for all eligible students from recognized institutes in India.
University teams battle to harden and hack AI coding assistants in head-to-head tournament
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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.
The company's new state-of-the-art foundation models deliver frontier intelligence and industry-leading price performance.
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Demand 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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Anomaly 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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Speculative 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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Modern 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
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Research on neural networks for time series has mostly focused on developing models that learn patterns about the target signal without the use of additional auxiliary or exogenous information. In applications such as selling products on a marketplace, the target signal is influenced by these variables, and leveraging exogenous variables is important. In particular, knowing that a product would go into
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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.
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We partner with particular academic organizations across the world for deep and sustained collaborations in multiple research areas of mutual interest.
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We hire world-class academics to work on large-scale technical challenges, while they continue to teach and conduct research at their universities.
