How can we rigorously evaluate and understand state-of-the-art progress in AI? Eureka is an open-source framework for standardizing evaluations of large foundation models, beyond single-score reporting and rankings. Learn more about the extended findings.
Egocentric videos provide a unique perspective into individuals' daily experiences, yet their unstructured nature presents challenges for perception. In this paper, we introduce AMEGO, a novel approach aimed at enhancing the comprehension of very-long egocentric videos. Inspired by the human's ability to maintain information from…
At the heart of the Damas-Hindley-Milner (HM) type system lies the abstraction rule which derives a function type for a lambda expression. This rule allows the type of the parameter to be "guessed", which allows for multiple possible types for functions like the identity function.…
Plans to create a centre, founded and funded by Microsoft and G42, that will "identify, develop and advance best practices and industry standards for the responsible use of AI in the Middle East and the Global South" were unveiled. Microsoft, based in Redmond, Washington, also…
From disaster recovery to conservation and healthcare, plenty of AI projects will greatly benefit humanity, Microsoft experts Juan M. Lavista Ferres and William B. Weeks say. Over the last few years, artificial intelligence (AI) has been firmly in the world's spotlight, and the rapidly advancing technology can…
Rigorous and reproducible evaluation of large foundation models is critical for assessing the state of the art, informing next steps in model improvement, and for guiding scientific advances in Artificial Intelligence (AI). Evaluation is also important for informing the increasing number of application developers that…
Instruction-tuned language models (LM) are able to respond to imperative commands, providing a more natural user interface compared to their base counterparts. In this work, we present Promptriever, the first retrieval model able to be prompted like an LM. To train Promptriever, we curate and…
Transformer-based Large Language Models (LLMs) have become increasingly important. However, due to the quadratic time complexity of attention computation, scaling LLMs to longer contexts incurs extremely slow inference speed and high GPU memory consumption for caching key-value (KV) vectors. This paper proposes RetrievalAttention, a training-free…
Large Language Models (LLMs) are widely used in healthcare, but limitations like hallucinations, incomplete information, and bias hinder their reliability. To address these, researchers released the Build Your Own expert Bot (BYOeB) platform, enabling developers to create LLM-powered chatbots with integrated expert verification. CataractBot, its…
DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored…
Investigating vulnerabilities in LLMs; A novel total-duration-aware (TDA) duration model for text-to-speech (TTS); Generative expert metric system through iterative prompt priming; Integrity protection in 5G fronthaul networks:
US Patent App. 18/118,849. This document relates to employing biosignals to evaluate predictions made by predictive models. For example, user attention can be inferred from a user attention signal such as gaze. When the user directs attention to a prediction output by a given predictive…
