Lifeboat Foundation

It’s 2020. Why can’t we binge Netflix as our cars drive us down the highway? Well, we’ve made progress, but not at the pace once promised. While some cars offer automated driving modes, you’re not to take your eyes off the road or hands from the wheel. Volvo wants to remedy that.

The company isn’t promising 100% self-driving cars in the near future. Instead, they’ll make mainstream cars that reliably drive themselves on highways—totally autonomously, no human attention needed. For a brand built on safety, and in light of autopilot accidents in recent years, it’s notable the company thinks that’s possible in the not-too-distant future.

To make it happen, Volvo said this week that it would begin adding lidar to production cars in 2022. They’ll also develop self-driving software to integrate lidar, cameras, radar, and back-up vehicle control systems. Once the software, dubbed Highway Pilot, is deemed safe, it’ll be sent out as an update to customers who opt in.

This raises the question of whether AI — defined as algorithms that mimic human intelligence — can deliver on its potential, and when. The answer is crucial because AI could become the ultimate industry disrupter, threatening tens of millions of jobs in Asia as business processes are automated. In addition, AI is the subject of intense rivalry between the US and China.

Unicorns abound but enthusiasm has dimmed. Will AI fulfil its potential?

Then there is the COVID-19 Open Research Dataset (CORD-19), a multi-institutional initiative that includes The White House Office of Science and Technology Policy, Allen Institute for AI, Chan Zuckerberg Initiative (CZI), Georgetown University’s Center for Security and Emerging Technology (CSET), Microsoft, and the National Library of Medicine (NLM) at the National Institutes of Health (NIH).

The goal of this initiative is to create new natural language processing and machine learning algorithms to scour scientific and medical literature to help researchers prioritize potential therapies to evaluate for further study. AI is also being used to automate screening at checkpoints by evaluating temperature via thermal cameras, as well as modulations in sweat and skin discoloration. What’s more, AI-powered robots have even been used to monitor and treat patients. In Wuhan, the original epicenter of the pandemic, an entire field hospital was transitioned into a “smart hospital” fully staffed by AI robotics.

Any time of great challenge is a time of great change. The waves of technological innovation that are occurring now will echo throughout eternity. Science, technology, engineering and mathematics are experiencing a call to mobilization that will forever alter the fabric of discovery in the fields of bioengineering, biomimicry and artificial intelligence. The promise of tomorrow will be perpetuated by the pangs of today. It is the symbiosis of all these fields that will power future innovations.

In an effort to create first-of-kind microelectronic devices that connect with biological systems, University of Maryland (UMD) researchers are utilizing CRISPR technology in a novel way to electronically turn “on” and “off” several genes simultaneously. Their technique, published in Nature Communications, has the potential to further bridge the gap between the electronic and biological worlds, paving the way for new wearable and “smart” devices.

“Faced with the COVID-19 pandemic, we now have an even deeper understanding of how ‘smart’ devices could benefit the general population,” said William E. Bentley, professor in UMD’s Fischell Department of Bioengineering and Institute for Bioscience and Biotechnology Research (IBBR), and director of the Robert E. Fischell Institute for Biomedical Devices. “Imagine what the world would be like if we could wear a device and access an app on our smartphone capable of detecting whether the wearer has the active virus, generated immunity, or has not been infected. We don’t have this yet, but it is increasingly clear that a suite of technologies enabling rapid transfer of information between biology and electronics is needed to make this a reality.”

With such a device, this information could be used, for example, to dynamically and autonomously conduct effective contact tracing, Bentley said.

Intel’s fifth-generation Loihi chip uses neuromorphic computing to learn faster on less training data than traditional artificial intelligence techniques — including how to smell like a human does and make accurate conclusions based on a tiny dataset of essentially just one sample.

“That’s really one of the main things we’re trying to understand and map into silicon … the brain’s ability to learn with single examples,” Mike Davies, the director of Intel’s Neuromorphic Computing Lab, told me recently on The AI Show podcast. “So with just showing one clean presentation of an odor, we can store that in this high dimensional representation in the chip, and then it allows it to then recognize a variety of noisy, corrupted, occluded odors like you would be faced with in the real world.”

In her new column covering neuromorphic engineering, intelligent robotics, and AI hardware, Sunny Bains looks at attempts to increase connectivity by creating three dimensional systems.

A real-time deep-learning model is proposed to classify the volume of cuttings from a shale shaker on an offshore drilling rig by analyzing the real-time monitoring video stream. As opposed to the traditional, time-consuming video-analytics method, the proposed model can implement a real-time classification and achieve remarkable accuracy. The approach is composed of three modules. Compared with results manually labeled by engineers, the model can achieve highly accurate results in real time without dropping frames.

Introduction

A complete work flow already exists to guide the maintenance and cleaning of the borehole for many oil and gas companies. A well-formulated work flow helps support well integrity and reduce drilling risks and costs. One traditional method needs human observation of cuttings at the shale shaker and a hydraulic and torque-and-drag model; the operation includes a number of cleanup cycles. This continuous manual monitoring of the cuttings volume at the shale shaker becomes the bottleneck of the traditional work flow and is unable to provide a consistent evaluation of the hole-cleaning condition because the human labor cannot be available consistently, and the torque-and-drag operation is discrete, containing a break between two cycles.

This study also analyzes the market status, market share, growth rate, future trends, market drivers, opportunities and challenges, risks and entry barriers, sales channels, distributors and Porter’s Five Forces Analysis. Neural Network Software market report all-inclusively estimates general market conditions, the growth prospects in the market, possible restrictions, significant industry trends, market size, market share, sales volume and future trends. The report starts by an introduction about the company profiling and a comprehensive review about the future events, sales strategies, Investments, business marketing strategy, future products, new geographical markets, customer actions or behaviors with the help of 100+ market data Tables, Pie Charts, Graphs & Figures spread through Pages for easy understanding. Neural Network Software market report has been designed by keeping in mind the customer requirements which assist them in increasing their return on investment (ROI and this research also provides a deep insight into the activities of key players such as Starmind, NeuralWare, Slagkryssaren AB, AND Corporation, Slashdot Media, XENON Systems Pty Ltd, Xilinx Inc and others. and others.

Get Full PDF Sample Copy of Report (Including Full TOC, List of Tables & Figures, Chart) at @ https://www.databridgemarketresearch.com/request-a-sample/?d…are-market

Global neural network software market is set to witness a healthy CAGR of 35.70% in the forecast period of 2019 to 2026.

A review of the state of the art from 2014 to 2019.