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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CIKM 2024 Workshop on Generative AI for E-commerce2024Large Language Models (LLMs) have been employed as crowd-sourced annotators to alleviate the burden of human labeling. However, the broader adoption of LLM-based automated labeling systems encounters two main challenges: 1) LLMs are prone to producing unexpected and unreliable predictions, and 2) no single LLM excels at all labeling tasks. To address these challenges, we first develop fast and effective
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CIKM 2024 Workshop on Data-Centric AI, EMNLP 2024 Workshop on Multilingual Representation Learning2024Dense retrieval systems are commonly used for information retrieval (IR). They rely on learning text representations through an encoder and usually require supervised modeling via labelled data which can be costly to obtain or simply unavailable. In this study, we introduce a novel unsupervised text representation learning technique via instruction-tuning the pre-trained encoder-decoder large language models
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ICDM 20242024In e-commerce recommender systems, providing product suggestions to customers that are often bought together, which is called “complementary recommendation,” not only improves customer experience but also boosts business impact. However, in practice, it is highly challenging to efficiently extract the complementary relations between the items due to noisy and low coverage of the co-purchased records in
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2024The large-scale deployment of robotic manipulation systems in warehouses has highlighted the rare but costly problem of robot-induced object damage. We present a system that uses a classification model to predict whether an object will get damaged during robotic manipulation. The model uses object attributes retrieved from warehouse information systems as well as attributes available at our robotic workcell
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Heterogeneous graph neural networks (HGNNs) excel in cap-turing graph topology and structural information. However, they are ineffective in processing the textual components present in nodes and edges and thus producing suboptimal performance in downstream tasks such as node-classification. Additionally, HGNNs lack in their explanatory power and are considered black-box. Although, Large Language models
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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.
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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.
