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.
An ensemble of models, weighted according to their reluctance to flag anomalies, outperforms its predecessors.
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Research areas
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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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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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Large Language Models (LLMs) have brought with them an unprecedented interest in AI in society. This has enabled their use in several day to day applications such as virtual assistants or smart home agents. This integration with external tools also brings several risk areas where malicious actors may try to inject harmful instruc-tions in the user query (direct prompt injection) or in the retrieved information
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As computing environments become increasingly complex and distributed, the volume and complexity of security data generated across various systems have grown exponentially. Extracting useful insights from this security data is crucial for effective security analytics, anomaly detection, and threat identification. However, there is a lack of comprehensive evaluation benchmarks for assessing the performance
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CIKM 2024 Workshop on Graph Techniques for Adversarial Activity Analytics2024Graph outlier detection identifies substructures in graphs that significantly deviate from normal patterns. Traditional graph outlier detection methods are mostly limited to static graphs, which overlook the dynamic nature of real-world graphs and ignore temporal signals providing critical information for detecting outliers. Recently, Transformers revolutionized machine learning on time-series data. However
Academia
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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.
