The U.S. is using every tool at its disposal to defeat the novel coronavirus, including artificial intelligence. American laboratories are harnessing AI to discover new therapeutics. The Food and Drug Administration approved an AI tool to help detect coronavirus in CT scans. And the White House led an initiative to create a database with more than 128,000 articles that scientists can analyze using AI to help understand the virus better and develop treatments.

Apple has acquired the machine learning startup Inductiv Inc., according to a new report from Bloomberg. The startup had been developing technology that uses artificial intelligence to identify and correct errors in datasets. The report explains that the engineering team from Inductiv has joined Apple "in recent weeks" to work on several different projects including Siri, machine learning, and data science. Apple issued its standard statement regarding the acquisition, saying it "buys smaller technology companies from time to time and we generally do not discuss our purpose or plans." The startup was founded by professors from Stanford University, the University of Waterloo, and the University of Wisconsin.

The "Sensors for Robotics: Technologies and Global Markets" report has been added to ResearchAndMarkets.com's offering This report sizes the market by technology, including sensors within the vision, touch, hearing, and movement segments. The top seven application areas are sized, forecast, and discussed in-depth. These include agriculture, appliances, automotive, healthcare, industrial, logistics, and military. In addition, the overall market and each application area are assessed on a worldwide and regional basis, including North America, Latin America, Europe, the Middle East and Africa, and Asia-Pacific. This report considers the economic slowdown caused by lockdown across the world owing to the COVID-19 pandemic.

According to Wikipedia, apophenia is "the tendency to mistakenly perceive connections and meaning between unrelated things" . It is also used as "the human propensity to seek patterns in random information". Whether it's a scientist doing research in a lab, or a conspiracy theorist warning us about how "it's all connected", I guess people need to feel like we understand what's going on, even in the face of clearly random information. Deep Neural Networks are usually treated like "black boxes" due to their inscrutability compared to more transparent models, like XGboost or Explainable Boosted Machines. However, there is a way to interpret what each individual filter is doing in a Convolutional Neural Network, and which kinds of images it is learning to detect.

If your work puts you in regular contact with technology vendors, you'll have heard terms such as artificial intelligence (AI), machine learning (ML), natural language processing and computer vision before. You'll have heard that AI/ML is the future, that the boundaries of these technologies are constantly being pushed and broadened, and that AI/ML will play an integral role in shaping this tech-forward era's most successful business models. As a technology leader, I've heard all these claims and more. To say that AI/ML will play an increasingly impactful role in business is no overstatement. According to a recent Forbes article, the machine learning market is poised to more than quadruple in the coming years.

"I would say everyone has read at least once an algorithmically produced article," said Robert Weissgraeber, CTO and Managing Director of AX Semantics. In many cases, readers don't see a difference between human- and bot-authored copy, Weissgraeber told Built In. His company, AX Semantics, is one of several -- including Narrative Science and Automated Insights -- exploring natural language generation, or automated writing. The technology can be used to generate product descriptions, quarterly earnings reports, fantasy football recaps and journalism. The Washington Post, for instance, has developed an AI-enabled bot, Heliograf, that helps generate election and sports coverage.

Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Credit: Daria Perevezentsev / University of Toronto Engineering Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.

Mobileye, Intel's driverless vehicle R&D division, today published a 40-minute video of one of its cars navigating a 160-mile stretch of Jerusalem streets. The video features top-down footage captured by a drone, as well as an in-cabin cam recording, parallel to an overlay showing the perception system's input and predictions. The perception system was introduced at the 2020 Consumer Electronics Show and features 12 cameras, but not radar, lidar, or other sensors. Eight of those cameras have long-range lenses, while four serve as "parking cameras" and all 12 feed into a compute system built atop dual 7-nanometer data-fusing, decision-making Mobileye EyeQ5 chips. Running on the compute system is an algorithm tuned to identify wheels and infer vehicle locations and an algorithm that identifies open, closed, and partially open car doors.

Practitioners in the arts labour under the misapprehension that the human factor of creativity would shield them from the depredations of artificial intelligence. It is assumed that like machines freed us from physical labour, machine intelligence would rid us of intellectual chores. They would put production line workers, bookkeepers, bank tellers and inventory managers out of work, but novelists and artists, and the marketing networks which have developed around their products, would be unharmed. A computer at Stanford which has digested the complete works of Shakespeare does almost passable knockoffs. In 2018, a neural network went on a journey across America and wrote a digital equivalent of Jack Kerouac's Beat classic On the Road.

Chinese ride-hailing giant Didi Chuxing Technology Co. raised more than $500 million in a funding round led by SoftBank Group Corp. for its autonomous-driving subsidiary, the company said Friday, as it competes with well-backed U.S. startups over self-driving technology. The fresh boost in capital led by SoftBank's Vision Fund 2, the successor to the Japanese tech investor's $100 billion Vision Fund, will be used to test, develop and deploy Didi's autonomous-driving technology, Didi said. The company is also planning to work...