As face coverings become the norm amid the coronavirus pandemic, Japanese startup Donut Robotics has developed an internet-connected "smart mask" that can transmit messages and translate from Japanese into eight other languages. The white plastic c-mask fits over standard face masks and connects via Bluetooth to a smartphone and tablet application that can transcribe speech into text messages, make calls, or amplify the mask wearer's voice. "We worked hard for years to develop a robot and we have used that technology to create a product that responds to how the coronavirus has reshaped society," said Taisuke Ono, the chief executive of Donut Robotics. Donut Robotics' engineers came up with the idea for the mask as they searched for a product to help the company survive the pandemic. When the coronavirus struck, it had just secured a contract to supply robot guides and translators to Tokyo's Haneda Airport, a product that faces an uncertain future after the collapse of air travel.

In times of crisis, we all want to know where the robots are! And young roboticists just starting their careers, or simply thinking about robotics as a career, ask us'How can robotics help?' and'What can I do to help?'. Cluster organizations like Silicon Valley Robotics can serve as connection points between industry and academia, between undergrads and experts, between startups and investors, which is why we rapidly organized a weekly discussion with experts about "COVID-19, robots and us" (video playlist). During our online series, we heard from roboticists directly helping with all sorts of COVID-19 response, like Gui Cavalcanti of Open Source Medical Supplies and Alder Riley of Helpful Engineering. Both groups are great examples of the incredible power of people working together.

Learn Computer Vision. Introduction course to Computer Vision with Python. Make Computer Vision Apps? Learn Computer Vision theory? Build a strong portfolio with Computer Vision & Image Processing Projects? Looking to add Computer Vision algorithms in your current software project ? Whatever be your motivation to learn Computer Vision, I can assure you that you’ve come to the right course. You get. Complete course with 1 hour of video tutorials, Source code for all examples in the course. What you'll learn. Use basic Computer Vision techniques. Do image processing. Build: Image Similarity app, Face Detection app and Object Detection app! Master Computer Vision! .

However, as with machine learning, the team needs people to literally "submit" the sounds of their coughs.

Covid-19 has changed the course of healthcare for the foreseeable future. Healthcare workers, doctors' rooms, and equipment inventories were stretched thin even before the pandemic took hold, with patients waiting weeks or months to get a doctor's appointment or book a surgery. Today, these resources are nearing the breaking point. With physicians' offices and testing facilities closed for in-person appointments, and elective surgeries put on hold, it has become incredibly complicated for patients to find the care they need for pre-existing or non-Covid-19 health issues. They might not be able to see their usual doctor or maybe they're simply trying to find a provider for the first time.

Last week we announced a new strategic partnership with Microsoft to further shape the future of AI and analytics in the cloud. This commitment will make it easy for SAS customers to move their analytics workloads to the cloud. And it will introduce SAS technologies to millions of Azure customers through APIs and deeper integrations that can enhance existing applications with analytics. To help illustrate how you can use SAS on Azure, I am sharing three inspiring examples from a recent SAS hackathon. Participants in this event were challenged to solve problems related to the United Nations Global Goals for Sustainable Development using SAS Viya .

JCMR recently announced market survey which covers overall in-depth study including additional study on COVID-19 impacted market situation on Global Artificial Intelligence Software Market. The Research Article Entitled Global Artificial Intelligence Software Market provides very useful reviews & strategic assessment including the generic market trends, upcoming & innovative technologies, industry drivers, challenges, regulatory policies that propel this Universal market place, and major players profile and strategies. The research study provides forecasts for Artificial Intelligence Software investments till 2029. There are following 15 Chapters to display the Global Artificial Intelligence Software Market. Table of Contents 1 Market Overview 1.1 Global Artificial Intelligence Software Introduction 1.2 Market Analysis by On-Premise Cloud-based 1.3 Market Analysis by Voice Processing Text Processing Image Processing 1.4 Market Analysis by North America, Europe, China, Japan, Rest of the World 1.5 Market Dynamics 1.5.1 Market Opportunities 1.5.2

Boston will become the second largest city in the US to ban facial recognition software for government use after a unanimous city council vote. Following San Francisco, which banned facial recognition in 2019, Boston will bar city officials from using facial recognition systems. The ordinance will also bar them from working with any third party companies or organizations to acquire information gathered through facial recognition software. The ordinance was co-sponsored by Councilors Ricardo Arroyo and Michelle Wu, who were especially concerned about the potential for racial bias in the technology, according to a report from WBUR. 'Boston should not be using racially discriminatory technology and technology that threatens our basic rights,' Wu said at a hearing before the vote.

Belief propagation (BP) algorithm is a widely used message-passing method for inference in graphical models. BP on loop-free graphs converges in linear time. But for graphs with loops, BP's performance is uncertain, and the understanding of its solution is limited. To gain a better understanding of BP in general graphs, we derive an interpretable belief propagation algorithm that is motivated by minimization of a localized $\alpha$-divergence. We term this algorithm as $\alpha$ belief propagation ($\alpha$-BP). It turns out that $\alpha$-BP generalizes standard BP. In addition, this work studies the convergence properties of $\alpha$-BP. We prove and offer the convergence conditions for $\alpha$-BP. Experimental simulations on random graphs validate our theoretical results. The application of $\alpha$-BP to practical problems is also demonstrated.

Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually work in a trace-by-trace fashion on seismic data, utilizing little to no information from the spatial structure of seismic images. We propose a deep learning-based seismic inversion workflow that models each seismic trace not only temporally but also spatially. This utilizes information-relatedness in seismic traces in depth and spatial directions to make efficient rock property estimations. We empirically compare our proposed workflow with some other sequence modeling-based neural networks that model seismic data only temporally. Our results on the SEAM dataset demonstrate that, compared to the other architectures used in the study, the proposed workflow is able to achieve the best performance, with an average $r^{2}$ coefficient of 79.77\%.