Whoosh pods have their own motors and autonomous navigation systems. Imagine gliding above city traffic in a sleek, autonomous pod, bypassing congested streets and reaching your destination in record time. This is the promise of Whoosh, an innovative urban transit system set to debut in 2026. Whoosh represents a paradigm shift in urban transportation, offering a solution that's as efficient as it is futuristic. GET SECURITY ALERTS, EXPERT TIPS - SIGN UP FOR KURT'S NEWSLETTER - THE CYBERGUY REPORT HERE While it may look similar at first glance, this clever Kiwi invention offers a unique blend of on-demand service, direct routing and privacy that sets it apart from traditional public transportation.

We introduce RealitySummary, a mixed reality reading assistant that can enhance any printed or digital document using on-demand text extraction, summarization, and augmentation. While augmented reading tools promise to enhance physical reading experiences with overlaid digital content, prior systems have typically required pre-processed documents, which limits their generalizability and real-world use cases. In this paper, we explore on-demand document augmentation by leveraging large language models. To understand generalizable techniques for diverse documents, we first conducted an exploratory design study which identified five categories of document enhancements (summarization, augmentation, navigation, comparison, and extraction). Based on this, we developed a proof-of-concept system that can automatically extract and summarize text using Google Cloud OCR and GPT-4, then embed information around documents using a Microsoft Hololens 2 and Apple Vision Pro. We demonstrate real-time examples of six specific document augmentations: 1) summaries, 2) comparison tables, 3) timelines, 4) keyword lists, 5) summary highlighting, and 6) information cards. Results from a usability study (N=12) and in-the-wild study (N=11) highlight the potential benefits of on-demand MR document enhancement and opportunities for future research.

This paper presents a scalable online algorithm to generate safe and kinematically feasible trajectories for quadrotor swarms. Existing approaches rely on linearizing Euclidean distance-based collision constraints and on axis-wise decoupling of kinematic constraints to reduce the trajectory optimization problem for each quadrotor to a quadratic program (QP). This conservative approximation often fails to find a solution in cluttered environments. We present a novel alternative that handles collision constraints without linearization and kinematic constraints in their quadratic form while still retaining the QP form. We achieve this by reformulating the constraints in a polar form and applying an Alternating Minimization algorithm to the resulting problem. Through extensive simulation results, we demonstrate that, as compared to Sequential Convex Programming (SCP) baselines, our approach achieves on average a 72% improvement in success rate, a 36% reduction in mission time, and a 42 times faster per-agent computation time. We also show that collision constraints derived from discrete-time barrier functions (BF) can be incorporated, leading to different safety behaviours without significant computational overhead. Moreover, our optimizer outperforms the state-of-the-art optimal control solver ACADO in handling BF constraints with a 31 times faster per-agent computation time and a 44% reduction in mission time on average. We experimentally validated our approach on a Crazyflie quadrotor swarm of up to 12 quadrotors. The code with supplementary material and video are released for reference.

Listen to Dr. Swami Sivasubramanian, Vice President, Amazon Machine earning, and other speakers on the latest key development and innovations in AWS AI & ML. There are new product & service launches, customer stories, demos, and more in this 2-hour Machine Learning keynote session. If you're interested to find out more on the past re: Invent Machine Learning keynote, the full video session and blogs are available below. Hugging Face is a fast-growing, popular, open-source AI/ML community hub for Natural Language Processing (NLP) models, datasets, as well as community ML apps, demo spaces. I am very keen to learn how I can quickly train a Hugging Face transformer NLP model on Amazon SageMaker with just a few lines of code using PyTorch or TensorFlow with SageMaker's distributed training libraries in this workshop.

We're excited to announce our line-up for the first of our Cabin Chat series which kicks off June 22-26. This week-long program and will include five 60-90-minute intensive sessions designed to help you find solutions for recovery. Your registration grants you access to all five sessions, which will be held live and will be available on-demand later that day. Sessions are now available on-demand. During this opening keynote, commercial aerospace industry analysts, Frost & Sullivan, provide updates to their recently published 2020 outlook and growth opportunities for airlines, airports, original equipment manufacturers (OEMs), the supply chain, and aftermarket suppliers.