High-resolution radar and night vision cameras may help scientists protect bats from untimely deaths at wind farms, according to new research. Researchers are using these technologies to provide more specific details about the number of bats killed by wind turbines in Iowa. These details will improve scientists' understanding of bat activity and potentially save their lives, said Jing Teng, a graduate researcher at the University of Iowa who presented the work this week at the 2019 American Geophysical Union Fall Meeting in San Francisco. This work has broad impacts, according to Teng. "The more bats you kill, the more insects you have on farms; then, farmers will put more pesticides; and then, people will eat more pesticides," he said.

Machine learning is a complex process. You build a model, test it in laboratory conditions, then put it out in the world. After that, how do you monitor how well it's tracking what you designed it to do? Arthur wants to help, and today it emerged from stealth with a new platform to help you monitor machine learning models in production. The company also announced it had closed a $3.3 million seed round, which closed in August.

California-based startup Plus.ai claims to have completed a cross-country trip with a prototype autonomous truck. While a human backup driver and a safety engineer were onboard for the entire 2,800-mile trip, Plus.ai claims the truck was in autonomous mode most of the time. This wasn't just a test run, either: the truck hauled a refrigerated trailer loaded with cargo for Land O'Lakes. Perishable cargo gave Plus.ai an added incentive to ensure its tech worked. The company couldn't simply abort the trip, and a human driver taking over would have been a public relations nightmare.

The need for reskilling and retraining due to the impact of artificial intelligence (AI) and automation technology will be massive, affecting more than 120 million workers across the world's 12 largest economies, according to IBM's Institute for Business Value. In a report entitled The enterprise guide to closing the skills gap, the institute indicated that while only 41% of employers have the required people, skills and resources in place to execute their business strategies effectively today, the situation will only get worse as demand for new – particularly soft – skills continues and expertise focused around repetitive, rules-based activities becomes progressively obsolete. "By 2030, the global talent shortage could reach more than 85 million people," the study says. "To be clear, the issue is not a shortage of workers, but a shortage of workers with the right skills." To make matters worse, although the so-called "half-life" of professional skills was formerly estimated at between 10 and 15 years, the half-life of a learned skill today is estimated to be a mere five years, and is potentially even less for technical expertise. So skills learned now will only be half as valuable in five years' time, which means that finding ways to continually update and refresh them will become an increasing imperative.

Artificial intelligence is here, and here to stay. Whether you realize it or not, you feel its impact through the marketing appeals you receive online or in the mail; in the placement, packaging, and pricing of items in a supermarket; and in a myriad of other ways. AI is also embedded in life insurance operations, helping agents match products with prospective clients with a precision that was previously unimaginable. It's understandable, however, that some life agents might be apprehensive about the growth of AI in a field that prides itself on providing thoughtful, individualized solutions to the unique situation of each household. Things will certainly change as AI advances in life insurance, but agents that embrace AI and the changes it brings will actually find themselves to be more valuable to the carriers and customers who rely on them.

PITTSBURGH/DETROIT (Reuters) - Sky's the limit optimism about self-driving cars is giving way to tougher questions about how expensive automotive artificial intelligence will ever make a profit. Those are questions the founders of Argo AI - and automaker partners Ford Motor Co and Volkswagen AG (VOWG_p.DE) - are betting they can answer by taking a different road than more highly valued rivals. They are steering away from building a robotaxi fleet and focusing instead on getting paid by the mile by customers that will use robot vehicles for multiple purposes, including delivering goods or transporting groups of people in vans. The self-driving systems developer led by Bryan Salesky, who got his start developing automated vehicles for a Defense Department sponsored competition 12 years ago, is at the center of a multibillion-dollar bet by its auto giant partners that autonomous vehicle technology must be good for more than replacing taxi drivers. "I hate the word robotaxi," Salesky said in a rare interview at Argo's Pittsburgh headquarters.

Twelve years later, even some of his former Carnegie Mellon University teammates have become business competitors of Salesky, who with CMU alumnus and faculty adviser Peter Rander founded Argo AI and went on to attract substantial investments from Ford Motor Co and Volkswagen AG (VOWG_p.DE). At the 2007 self-driving competition staged by DoD's Defense Advanced Research Projects Agency (DARPA) in remote Victorville, California, Salesky's CMU team and one from rival Stanford University included the future founders of at least four self-driving startups. Those competitors were Chris Urmson and Drew Bagnell of self-driving vehicle startup Aurora, Dave Ferguson of Nuro, Apex.ai's Jan Becker and Anthony Levandowski of Pronto.ai. Sebastian Thrun, who with Levandowski and Urmson helped build Google's self-driving business, also participated in the 2007 DARPA Urban Challenge, as did Dmitri Dolgov, who now heads engineering at Google's self-driving spinout Waymo.

It is the present-day darling of the tech world. The current renaissance of Artificial Intelligence (AI) with its sister discipline Machine Learning (ML) has led every IT firm worth its salt to engineer some form of AI onto its platform, into its toolsets and throughout its software applications. IBM CEO Ginni Rometty has already proclaimed that AI will change 100 percent of jobs over the next decade. And yes, she does mean everybody's job from yours to mine and onward to the role of grain farmers in Egypt, pastry chefs in Paris and dog walkers in Oregon i.e. every job. We will now be able to help direct all workers' actions and behavior with a new degree of intelligence that comes from predictive analytics, all stemming from the AI engines we will now increasingly depend upon.

Wired editor Maria Streshinsky speaks to computer and data science experts Kai-Fu Lee and Fei-Fei Li. We're told constantly that artificial Intelligence (AI) is ever-rising in its ubiquity, seeping into every industry, finding its place in all aspects of the business-- enabling us to work in different ways; in some cases, threatening to take over our roles entirely. Stats such as recruitment firm Robert Walters', which predicts AI will give rise to 133 million new jobs across the globe in the future, can sound vague and far off in a distant future, while things probably haven't seemed to have changed much at our desks. But rest assured, hype aside, the'age of AI' is drawing closer, and the evidence lies in businesses' eagerness to invest in the talent to make it happen. The AI specialist now represents the fastest-growing role in the United States over the last four years.

In deep learning, a subset of a type of artificial intelligence called machine learning, computer models essentially teach themselves to make predictions from large sets of data. The raw power of the technology has improved dramatically in recent years, and it's now used in everything from medical diagnostics to online shopping to autonomous vehicles. But deep learning tools also raise worrying questions because they solve problems in ways that humans can't always follow. If the connection between the data you feed into the model and the output it delivers is inscrutable--hidden inside a so-called black box--how can it be trusted? Among researchers, there's a growing call to clarify how deep learning tools make decisions--and a debate over what such interpretability might demand and when it's truly needed.