generative AI

Amazon and Carnegie Mellon University launch strategic AI Innovation Hub

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Amazon and Carnegie Mellon University launch strategic AI Innovation Hub

Amazon and Carnegie Mellon University (CMU) today announced the launch of the CMU-Amazon AI Innovation Hub, reinforcing a shared commitment to advance cutting-edge research through partnerships between academia and the private sector. The hub builds on existing collaborations between Carnegie Mellon and Amazon and will include research in generative artificial intelligence, robotics, natural language processing … Read more

Science in Foundation Models Age

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Science in Foundation Models Age

Deep Learning’s transformative influence has been very clear with Advent and rapid adoption of large language models (LLMs). LLMS and other so-called Foundation models are potentially powerful tools to promote a wide range of pure and used sciences. However, despite the role of foundation models playing in language and computer vision, however, there has been … Read more

Generative using AI to improve extremely multilable classification

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Generative using AI to improve extremely multilable classification

For years, Amazon researchers have examined the topic of extremely multilable classification (XMC) or classification of input when the space for possible classification categories is large – says millions of labels. Along the way we have advanced modern art several times. But the previous work was in the setting of a classic classification problem, where … Read more

Amazon Builds First Foundation Model for Multirobot -Coordination

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Amazon Builds First Foundation Model for Multirobot -Coordination

Large language models and other foundation models have introduced a new paradigm in AI: Large models trained in a self-given up overall fashion-ninen Data Annotation required for huge amounts of data can learn general comet trays that allow them to perform a number of tasks. The most prominent examples of this paradigm are in language, … Read more

A better path to throwing large language models

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A better path to throwing large language models

In recent years, large language models (LLMs) have revolutionized the area of natural-language processing and made significant contributions to computer vision, speech recognition and language translation. One of the keys to LLMS ‘efficiency has been the extremely large data sets they were training. The trade -off is extremely large model sizes that lead to slower … Read more

Three challenges in machine -based reasoning

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Three challenges in machine -based reasoning

Generative AI has made the last few years the most exhibition time in my 30+annual career in mechanized reasoning. Why? The Becuse computer industry and even the public are now eager to talk about ideas that those of us who work in logic have been passionate about for years. The challenges of language, syntax, semantics, … Read more

Pushing the limits of Secure AI: Winners of Amazon Nova Ai Challenge

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Pushing the limits of Secure AI: Winners of Amazon Nova Ai Challenge

Sales January 2025 Ten Elite University teams from all over the world participated in the first Amazon Nova AA challenge, Amazon Nova Ai Challenge trusted AI. Today we are proud to announce the winners and runners of this global competition: Defense Winner: Team Purpcorn Plan, University of Illinois Urbana-Champaign Attack team winner: Purcl Team, Purdue … Read more

ACL 2023: Computational Linguistics in the Age of Large Language Models

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ACL 2023: Computational Linguistics in the Age of Large Language Models

Asy is everywhere, big language models are an important topic of conversation at this year’s meeting in Association for Computational Linguistics (ACL). Yang Liu, a senior main scientist with Alexa AI and generally meat at this year’s meeting in Association for Computational Linguistic. “We have several sessions about large language models that were not a … Read more

Pruning network nodes on the go to improve llm efficiency

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Pruning network nodes on the go to improve llm efficiency

Foundation Models (FMS) such as large language models and vision-language models are growing in popularity, but their energy ineffective and calculation costs are still an obstacle to wider implementation. To tackle these challenges, we offer a new architecture that, in our experience, reduced an FM’s infernic time by 30%while we had its accuracy. Our architecture … Read more

FALSSEVECT: Reducing overgrowth in LLMS through evaluation of reasoning security

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FALSSEVECT: Reducing overgrowth in LLMS through evaluation of reasoning security

Large language models (LLMs) have come a long way in enforcing responsible-IA standards through robust security mechanisms. However, this mechanism often fails on the side of bail, leading to overfusals – occurrences where the model refuses to answer perfectly benign requests. Although this overcautious behavior can reduce LLMS ‘usefulness in nuanced real world contexts such … Read more