The connected vehicle is reshaping our view of mobility and transportation. The goal is how rapidly companies can get the information to fleet managers, deploy predictive analytics and machine learning to report fast, and prevent or save the costs of the downtime. The industry is fast changing from the regular telematics used for data collection to a single gateway to provide connectivity to all the peripherals of the vehicle. The Connected Vehicle can communicate with the cloud and/or the transport infrastructure, and broadcast relevant information (e.g. Use cases for this data include real-time congestion reporting and forecasting based on GPS traces.

THE FUNDAMENTAL assumption of the computing industry is that number-crunching gets cheaper all the time. Moore's law, the industry's master metronome, predicts that the number of components that can be squeezed onto a microchip of a given size--and thus, loosely, the amount of computational power available at a given cost--doubles every two years. For many comparatively simple AI applications, that means that the cost of training a computer is falling, says Christopher Manning, an associate director of the Institute for Human-Centered AI at the University of Stanford. But that is not true everywhere. A combination of ballooning complexity and competition means costs at the cutting edge are rising sharply.

Wendy Gonzalez, interim CEO of Samasource, poses with Agents in Nairobi, Kenya. Samasource employees ... [ ] young Kenyans and Ugandans to work in the AI supply chain, upskilling them up for a career in technology. Conscious consumers demand fair-trade when it comes to products like coffee, and when it's quality coffee, they are even willing to pay more for it. When it comes to our technology products though, many consumers don't even know that "fair-trade" is possible. Behind many acts of AI "magic," there is a human in the loop.

Prior to this pandemic, startups had been positioned for rapid growth. However, currently, the ecosystem is struggling to survive due to challenges in raising capital in this outbreak. This has been attested by Nasscom's recent survey which stated that majority of startups in India are soon going to heavily bear the brunt of this pandemic as 70% of them have less than three months of cash runway in the bank. Another 22% can barely make it to the end of 2020. Earlier this year, major venture capital firms such as Accel, Lightspeed, Sequoia, Nexus Venture Partners, among others, cautioned startups in an open letter about the difficult times ahead in terms of funding.

Intel and the National Science Foundation (NSF), joint funders of the Machine Learning for Wireless Networking Systems (MLWiNS) program, today announced recipients of awards for research projects into ultra-dense wireless systems that deliver the throughput, latency and reliability requirements of future applications – including distributed machine learning computations over wireless edge networks. Institutions: University of Illinois Urbana-Champaign and University of Washington Project Leads: Pramod Viswanath (University of Illinois Urbana-Champaign) and Sewoong Oh (University of Washington) Project Description: This project will use deep learning applications in the physical layer of communications systems, which will enable researchers to: 1) study the operation of new neural-network based, nonlinear channel codes through jointly trained encoders and decoders, 2) integrate information-theory, which can reduce the number of parameters to be learned and improve the training efficiency of communication systems, to create non-linear codes in feedback channels, and 3) design a family of non-linear neural codes for interference networks.

The AI in Enterprise Accounting Market has witnessed continuous growth in the past few years and is projected to grow even further during the forecast period (2020-2025). The assessment provides a 360 view and insights, outlining the key outcomes of the industry. These insights help the business decision-makers to formulate better business plans and make informed decisions for improved profitability. In addition, the study helps venture or private players in understanding the companies more precisely to make better-informed decisions. The AI in Enterprise Accounting Market study covers current status, % share, future patterns, development rate, SWOT examination, sales channels, to anticipate growth scenarios for years 2020-2025.

A novel machine learning model developed by researchers at Michigan State University suggests that mutations to the SARS-CoV-2 genome have made the virus more infectious. As infections continue to surge across the United States, the concern is that any slight mutation could have drastic consequences. The model, developed by lead research Guo-Wei Wei, professor in the Department of Biochemistry and Molecular Biology, analyzed SARS-CoV-2 genotyping from more than 20,000 viral genome samples. The researchers analyzed mutations to the spike protein -- a protein primarily responsible for facilitating infection -- and found that five of the six known virus subtypes are now more infectious. As with any virus, many mutations are ultimately benign, posing little to no risk to infected patients.

Databricks Inc., the big-data and machine learning company that leads the commercial development of Apache Spark, today put its MLflow project into the hands of the Linux Foundation. MLflow is a machine learning operations or MLOps platform that the company first open-sourced two years ago. The software gives developers a programmatic way to handle all of the pieces of a machine learning project, from construction, to training, fine-tuning, deployment, management and revision. MLflow is used to track all of the datasets, model instances, model parameter and algorithms developers use in their machine learning projects. That enables them to be versioned, stored in a central repository and then repackaged so they can be used in other projects as required.

Network science is an academic field that aims to unveil the structure and dynamics behind networks, such as telecommunication, computer, biological and social networks. One of the fundamental problems that network scientists have been trying to solve in recent years entails identifying an optimal set of nodes that most influence a network's functionality, referred to as key players. Identifying key players could greatly benefit many real-world applications, for instance, enhancing techniques for the immunization of networks, as well as aiding epidemic control, drug design and viral marketing. Due to its NP-hard nature, however, solving this problem using exact algorithms with polynomial time complexity has proved highly challenging. Researchers at National University of Defense Technology in China, University of California, Los Angeles (UCLA), and Harvard Medical School (HMS) have recently developed a deep reinforcement learning (DRL) framework, dubbed FINDER, that could identify key players in complex networks more efficiently.

In the past decade, cars have become smarter in ways we previously never thought possible. They have gained internet connections, formed successful relationships with our smartphones via CarPlay and Android Auto, and learned a whole host of new semi-autonomous driving skills. They have also turned into the largest'device' we own, morphing from simply a mode of transport, into a smart gadget with abilities of our phones, smartwatches and computers. Next, they'll get much better at communicating with those products, and with the services – especially health and wellbeing services – we use everyday. And, of course, they'll get even safer too.