Jimmy Gomez is a California Democrat, a Harvard graduate and one of the few Hispanic lawmakers serving in the US House of Representatives. But to Amazon's facial recognition system, he looks like a potential criminal. Gomez was one of 28 US Congress members falsely matched with mugshots of people who've been arrested, as part of a test the American Civil Liberties Union ran last year of the Amazon Rekognition program. Nearly 40 percent of the false matches by Amazon's tool, which is being used by police, involved people of color. This is part of a CNET special report exploring the benefits and pitfalls of facial recognition.

MOSCOW – It's mission over for a robot called Fedor that Russia blasted to the International Space Station, the developers said Wednesday, admitting he could not replace astronauts on spacewalks. There's nothing more for him to do there, he's completed his mission," Yevgeny Dudorov, executive director of robot developers Androidnaya Tekhnika, told RIA Novosti news agency. The silvery anthropomorphic robot cannot fulfill its assigned task to replace human astronauts on long and risky space walks, Dudorov said. Fedor -- short for Final Experimental Demonstration Object Research -- was built to assist space station astronauts. A storm of publicity surrounded Fedor's space odyssey and provided some light relief for Russia's beleaguered space industry. In the last year it has seen the unprecedented failure of a manned launch and continuing delays on construction of the Vostochny spacepad where President Vladimir Putin upbraided officials last week. But Fedor turned out to have a design that does not work well in space -- standing 180 centimeters (six feet) tall, its long legs were not needed on space walks, Dudorov said. The Russian space agency said the legs were immobilized during the trip and Fedor was not programmed to grab space station hand rails to move about in microgravity. Dudorov said developers were sketching out plans for a replacement "that must suit the demands of working on the outside of the ship." Fedor, officially Skybot F-850, rocketed to the ISS on Aug. 22, entering the orbiting laboratory five days later. On the station, the robot posed holding a Russian flag and for hugs with cosmonauts who were assigned to train it before touching down back on Earth on Monday. A final tweet posted in an account in the robot's name said: "Now I'm in my case.

Like a flying fish gliding above the water's surface, a robot can now propel itself out of water into flight. Mirko Kovac and his colleagues at Imperial College London have developed a robot that can lift itself out of water and travel through the air for up to 26 metres. The robot weighs 160 grams and could be used for monitoring the ocean sampling. It could take water samples by jumping in and out of the water in cluttered environments, avoiding obstacles such as ice in cold regions or floating objects after a flood. "In these situations, it's important to fly there quickly, take a sample and come back," says Kovac. The flying fish robot consists of a small tank that refills passively with water from its aquatic surroundings.

There might be disadvantages to using tried and trusted methods.Credit: Science Photo Library Like most research fields, materials science has embraced'big data', including machine-learning models and techniques. These are being used to predict new materials and properties, and devise routes to existing drugs and chemicals. But machine learning requires training data, such as those on reagents, conditions and starting materials. These are usually gleaned from the literature, and are human-generated. The choice of reagents that researchers use could come, for example, from experience or from previously published work. It might be based on a recommendation passed from supervisor to graduate student, or simply on how easy reagents are to find or buy.

On the one hand, autonomous vehicles offer an excellent opportunity to rethink how American cities operate, down to each lane line, crosswalk, and curb. Two years ago, the National Association of City Transportation Officials, representing 81 North American cities, published its first planning guide to self-driving vehicles, highlighting the possibilities. If everyone moves around on electric-powered transit and robotaxis, no one needs to own a car. No one needs to park a car. So that first version outlined an elegant--albeit fanciful--vision of the cities of the future.

A study by researchers from the University of Rochester found an exodus of artificial intelligence (AI) professors from North American universities to the private sector has reduced the prospect that graduate students will found new AI companies. Those graduates who did start a company usually attracted less venture capital, with the field of deep learning especially affected, according to "Artificial Intelligence, Human Capital, and Innovation," by Michael Gofman and Zhao Jin. This academic attrition could hinder innovation and economic expansion over time, the researchers suggest. The technology industry mostly ignored deep learning's potential until 2010, but interest grew as the Internet produced more data and new computer chips reduced the analytical burden. Large tech companies have hired many academic specialists, including two recent recipients of the ACM A.M. Turing Award honored for their work on neural networks.

Computer vision is one of the most popular applications of artificial intelligence. Image classification, object detection and object segmentation are some of the use cases of computer vision-based AI. These techniques are used in a variety of consumer and industrial scenarios. From face recognition-based user authentication to inventory tracking in warehouses to vehicle detection on roads, computer vision is becoming an integral part of next-generation applications.

Virtual Reality (VR) is opening up exciting new frontiers in the development of video games, paving the way for increasingly realistic, interactive and immersive gaming experiences. VR consoles, in fact, allow gamers to feel like they are almost inside the game, overcoming limitations associated with display resolution and latency issues. An interesting further integration for VR would be emotion recognition, as this could enable the development of games that respond to a user's emotions in real time. With this in mind, a team of researchers at Yonsei University and Motion Device Inc. have recently proposed a deep-learning-based technique that could enable emotion recognition during VR gaming experiences. Their paper was presented at the 2019 IEEE Conference on Virtual Reality and 3-D User Interfaces.

There is a new race in Silicon Valley involving Artificial Intelligence and no it's not HealthTech, FinTech, Voice Commerce or involve Google, Facebook or Microsoft... this race involves the brain and more specifically brain-computer interfaces. This race also involves technology royalty, the US government, billion dollar defence companies, a big connection to PayPal and years of medical research to better understand the human brain and implant devices that could make a consumer brain-computer interface a reality. The race is called "Neural implants, merging the human brain with AI" So what exactly are neural implants? Brain implants, often referred to as neural implants, are technological devices that connect directly to a biological subject's brain – usually placed on the surface of the brain, or attached to the brain's cortex. A common purpose of modern brain implants and the focus of much current research is establishing a biomedical prosthesis circumventing areas in the brain that have become dysfunctional after a stroke or other head injuries.[1]

WASHINGTON DC: Researchers discovered that modern machine learning, a branch of artificial intelligence may augment the traditional way of diagnosing kidney disease. Pathologists often classify various kidney diseases on the basis of visual assessments of biopsies from patients' kidneys; however, machine learning has the potential to automate and augment the accuracy of classifications. In one study, a team led by Pinaki Sarder, PhD and Brandon Ginley, BS (Jacobs School of Medicine and Biomedical Sciences at the University at Buffalo) developed a computational algorithm to detect the severity of diabetic kidney disease without human intervention. The algorithm examined a digital image of a patient's kidney biopsy at the microscopic level and extracted information on glomeruli, the small blood vessels of the kidney that filter waste from the blood for excretion. These structures are known to become progressively damaged and scarred over the course of diabetes, reported the study published in the journal -- journal of the American Society of Nephrology.