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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NeurIPS 2024 Workshop on Efficient Natural Language and Speech Processing (ENLSP-IV)2024Speculative 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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NeurIPS 2024 Workshop on Time Series in the Age of Large Models2024Modern 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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NeurIPS 2024 Workshop on Time Series in the Age of Large Models2024Research 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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2024We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoning capabilities and world knowledge of large language models, and augmented by the Segment Anything Model, VideoLISA generates temporally consistent segmentation masks in videos based on language instructions. Existing image-based
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NeurIPS 2024 Workshop on Intrinsically-Motivated and Open-Ended Learning2024Reinforcement Learning (RL) has achieved state-of-the-art performance in station-ary environments with effective simulators. However, lifelong and open-world RL applications, such as robotics, stock trading, and recommendation systems, change over time in adversarial ways. Non-stationary environments pose challenges for RL agents due to constant distribution shifts from the training data, leading to deteriorating
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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.
