George He

Amazon AGI, Aaron Langford, Aayush Shah et al. · amazon-science

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highly-capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks. Amazon Nova Lite is a low-cost multimodal model that is lightning fast for processing images, video, documents and text. Amazon Nova Micro is a text-only model that delivers our lowest-latency responses at very low cost. Amazon Nova Canvas is an image generation model that creates professional grade images with rich customization controls. Amazon Nova Reel is a video generation model offering high-quality outputs, customization, and motion control. Our models were built responsibly and with a commitment to customer trust, security, and reliability. We report benchmarking results for core capabilities, agentic performance, long context, functional adaptation, runtime performance, and human evaluation.

Pablo Ducru, Jonathan Raiman, Ronaldo Lemos et al.

This article investigates how AI-generated content can disrupt central revenue streams of the creative industries, in particular the collection of dividends from intellectual property (IP) rights. It reviews the IP and copyright questions related to the input and output of generative AI systems. A systematic method is proposed to assess whether AI-generated outputs, especially images, infringe previous copyrights, using a similarity metric (CLIP) between images against historical copyright rulings. An examination (economic and technical feasibility) of previously proposed compensation frameworks reveals their financial implications for creatives and IP holders. Lastly, we propose a novel IP framework for compensation of artists and IP holders based on their published "licensed AIs" as a new medium and asset from which to collect AI royalties.

George He, Sami Oueida, Tucker Ward

Gaze and face tracking algorithms have traditionally battled a compromise between computational complexity and accuracy; the most accurate neural net algorithms cannot be implemented in real time, but less complex real-time algorithms suffer from higher error. This project seeks to better bridge that gap by improving on real-time eye and facial recognition algorithms in order to develop accurate, real-time gaze estimation with an emphasis on minimizing training data and computational complexity. Our goal is to use eye and facial recognition techniques to enable users to perform limited tasks based on gaze and facial input using only a standard, low-quality web cam found in most modern laptops and smart phones and the limited computational power and training data typical of those scenarios. We therefore identified seven promising, fundamentally different algorithms based on different user features and developed one outstanding, one workable, and one honorable mention gaze tracking pipelines that match the performance of modern gaze trackers while using no training data.