Machine learning--a form of artificial intelligence based on the idea that computers can learn from data and make determinations with little help from humans--has the potential to improve our lives in countless ways. From self-driving cars to mammogram scans that can read themselves, machine learning is transforming modern life. It's easy to assume that using algorithms for decision-making removes human bias from the equation. But researchers have found that machine learning can produce unfair determinations in certain contexts, such as hiring someone for a job. For example, if the data plugged into the algorithm suggest men are more productive than women, the machine is likely to "learn" that difference and favor male candidates over female ones, missing the bias of the input.

When his father died last year, Richard Boyd began using artificial intelligence to model what his dad would be doing today if he had not passed away. "For the past eight or nine months, I can'visit him' and see what he's interested in," he says. The model found that on the Fourth of July his dad would have read about the Air Force, Vietnam, Donald Trump, guns and gun control, and the Santa Fe Railway. He likely would have attended an event at a local Veterans of Foreign Wars post, and created a home fireworks display. He calls this exercise "oddly therapeutic." Nor is he a character in a "Black Mirror" episode.

Top Pentagon official Michael Griffin sat down a few weeks ago with Air Force scientists at Wright Patterson Air Force Base in Ohio to discuss the future of quantum computing in the U.S. military. Griffin, the undersecretary of defense for research and engineering, has listed quantum computers and related applications among the Pentagon's must-do R&D investments. Quantum computing is one area where the Pentagon worries that it is playing catchup while China continues to leap ahead. The technology is being developed for many civilian applications, and the military sees it as potentially game-changing for information and space warfare. The U.S. Air Force particularly is focused on what is known as quantum information science.

A creepy surveillance start-up backed by the Chinese government is expanding its artificial intelligence-powered face-reading operations. Beijing firm Megvii has announced it is shifting its powerful Face facial recognition technology beyond China after securing a distributor in Thailand. Megvii's world-leading Face technology uses AI to identify people by their facial features, allowing police to spy on'anyone, anywhere' without them knowing. The software has already been used by Chinese police departments to arrest more than 3,000 fugitives by instantly scanning crowds of thousands in busy cities. The company says its software has many potential uses beyond law enforcement, including in financial services, retail, and identity verification.

It plans to launch a system in Japan in mid-2019 using GPS and gyro sensors, which sense rotational motion, to assess the best way to manage the power system of its Profia trucks - its largest diesel-hybrid models, marketed abroad as the 700 series.

Hong Kong Applied Science and Technology Research Institute (ASTRI) and Cyberport, the digital tech community comprising over 1,000 technology companies, partnered for the'ASTRI Technovation Day' -- aiming to introduce ASTRI to the Cyberport tenant companies

Kairos snaps up EmotionReader, which can scan faces in a crowd and tell how audiences are reacting. EmotionReader, an Enterprise Ireland-backed facial recognition start-up, has been acquired by Miami-based Kairos in an undisclosed "multimillion-dollar" deal. Artificial intelligence (AI) then analyses viewer attention and emotional response, enabling media and brand owners to collect actionable insights and analytics for video. 'In our mission to fix biases in today's face recognition algorithms, we're thrilled to welcome to Kairos some of the best deep-learning talent in the world' – BRIAN BRACKEEN The company is the brainchild of Dr Padraig O'Leary and Dr Stephen Moore, and it was founded only last year. Moore, working from his Singapore base, is understood to have built an impressive R&D team in the south-east Asian country.

It plans to launch a system in Japan in mid-2019 using GPS and gyro sensors, which sense rotational motion, to assess the best way to manage the power system of its Profia trucks - its largest diesel-hybrid models, marketed abroad as the 700 series. Along with existing automatic cruise-control technology, the system calculates ways to limit the impact of tailgating and other driving habits which can reduce mileage, improving fuel economy by 15 percent over diesel-only counterparts. "Fuel efficiency in large commercial trucks varies significantly given driving habits," Executive Vice President Shin Endo told reporters at Hino's research and development center on the outskirts of Tokyo. "With this system, the vehicle can achieve better mileage regardless of the skill of the driver." Hino plans to sell all-battery commercial trucks from around 2020, though its lineup initially will likely be limited to smaller models due to batteries' cost and weight considerations.

Researchers from the Universitat Autonoma de Barcelona, Carnegie Mellon University and International Institute of Information Technology, Hyderabad, India, have developed a technique that could allow deep learning algorithms to learn the visual features of images in a self-supervised fashion, without the need for annotations by human researchers. To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive information about every image. However, collecting and manually annotating these images requires huge amounts of time, resources, and human effort. "We aim to give computers the capability to read and understand textual information in any type of image in the real-world," says Dimosthenis Karatzas, one of the researchers who carried out the study, in an interview with Tech Xplore. Humans use textual information to interpret all situations presented to them, as well as to describe what is happening around them or in a particular image.

Researchers from the Universitat Autonoma de Barcelona, Carnegie Mellon University and International Institute of Information Technology, Hyderabad, India, have developed a technique that could allow deep learning algorithms to learn the visual features of images in a self-supervised fashion, without the need for annotations by human researchers. To achieve remarkable results in computer vision tasks, deep learning algorithms need to be trained on large-scale annotated datasets that include extensive information about every image. However, collecting and manually annotating these images requires huge amounts of time, resources, and human effort. "We aim to give computers the capability to read and understand textual information in any type of image in the real-world," says Dimosthenis Karatzas, one of the researchers who carried out the study, in an interview with Tech Xplore. Humans use textual information to interpret all situations presented to them, as well as to describe what is happening around them or in a particular image.