A new framework provides a statistical method for estimating the likelihood of catastrophic failures in large language models in adversarial conversations.
Isabelle/HOL's balance of expressiveness, automation, and scalability enabled the world's first formally verified cloud hypervisor.
A single, optimized LLM unifies what previously required multiple models and can serve as a reasoning partner for medical chemists.
The Antibody Developability Benchmark is powered by one of the most diverse antibody datasets in public literature, enabling transparent performance evaluation for AI-guided antibody design.
Low-rank adaptation, data augmentation, and chain-of-thought reasoning are among the techniques enabling accent-free polyglot outputs, improved expressiveness, and reliable synthesis.
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April 8, 20266 min readAmazon’s RuleForge system uses agentic AI to generate production-ready detection rules 336% faster than traditional methods. -
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Now open across seven research areas, including Agentic AI and Robotics. Applicants receive unrestricted funds, AWS promotional credits, and training resources. Submission deadline is May 6
Hosted by Danielle Perszyk, cognitive scientist at Amazon's AGI Lab, the podcast features conversations with leading AI researchers about the breakthroughs needed to achieve general intelligence.
Challenge pushes teams to demonstrate measurable gains in secure-coding performance while building AI agents that advance real-world utility and reliability at scale.
Initiative will fund over 100 doctoral students researching machine learning, computer vision, and natural-language processing at nine universities.
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2024Set theory is foundational to mathematics and, when sets are finite, to reasoning about the world. An intelligent system should perform set operations consistently, regardless of superficial variations in the operands. Initially designed for semantically-oriented NLP tasks, large language models (LLMs) are now being evaluated on algorithmic tasks. Because sets are comprised of arbitrary symbols (e.g. numbers
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NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)2024Speculative decoding aims to speed up autoregressive generation of a language model by verifying in parallel the tokens generated by a smaller draft model. In this work, we explore the effectiveness of learning-free, negligible-cost draft strategies, namely N-grams obtained from the model weights and the context. While the predicted next token of the base model is rarely the top prediction of these simple
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2024We introduce Condition-Aware Self-Supervised Learning Representation (CASSLR), a generalist conditioning model broadly applicable to various speech-processing tasks. Compared to standard fine-tuning methods that optimize for downstream models, CA-SSLR integrates language and speaker embeddings from earlier layers, making the SSL model aware of the current language and speaker context. This approach reduces
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2024Prompt-based learning has been demonstrated as a compelling paradigm contributing to large language models’ tremendous success (LLMs). Inspired by their success in language tasks, existing research has leveraged LLMs in embodied instruction following and task planning. In this work, we tackle the problem of training a robot to understand multimodal prompts, interleaving vision signals with text descriptions
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NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)2024Large language models (LLMs) have achieved remarkable performance on various natural language processing tasks, but training LLMs at scale is extremely resource-intensive, requiring substantial computational power, memory, and energy consumption. This has motivated research into efficient training methods, particularly during the pre-training phase. There are two main approaches to approximate full-rank
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Gretchen Ertl
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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
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