A screen shows a demonstration of SenseTime Group's SenseVideo pedestrian and vehicle recognition system at the company's showroom in Beijing. Facial recognition supporters in the US often argue that the surveillance technology is reserved for the greatest risks -- to help deal with violent crimes, terrorist threats and human trafficking. And while it's still often used for petty crimes like shoplifting, stealing $12 worth of goods or selling $50 worth of drugs, its use in the US still looks tame compared with how widely deployed facial recognition has been in China. A database leak in 2019 gave a glimpse of how pervasive China's surveillance tools are -- with more than 6.8 million records from a single day, taken from cameras positioned around hotels, parks, tourism spots and mosques, logging details on people as young as 9 days old. The Chinese government is accused of using facial recognition to commit atrocities against Uyghur Muslims, relying on the technology to carry out "the largest mass incarceration of a minority population in the world today."

Astronomers have applied artificial intelligence (AI) to ultra-wide field-of-view images of the distant Universe captured by the Subaru Telescope, and have achieved a very high accuracy for finding and classifying spiral galaxies in those images. This technique, in combination with citizen science, is expected to yield further discoveries in the future. A research group, consisting of astronomers mainly from the National Astronomical Observatory of Japan (NAOJ), applied a deep-learning technique, a type of AI, to classify galaxies in a large dataset of images obtained with the Subaru Telescope. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological classification.

A newly published study by University of Michigan researchers shows facial recognition technology in schools presents multiple problems and has limited efficacy. Led by Shobita Parthasarathy, director of the university's Science, Technology, and Public Policy (STPP) program, the research say the technology isn't suited to security purposes and can actively promote racial discrimination, normalize surveillance, and erode privacy while institutionalizing inaccuracy and marginalizing non-conforming students. The study follows the New York legislature's passage of a moratorium on the use of facial recognition and other forms of biometric identification in schools until 2022. The bill, which came in response to the launch of facial recognition by the Lockport City School District, was among the first in the nation to explicitly regulate or ban use of the technology in schools. That development came after companies including Amazon, IBM, and Microsoft halted or ended the sale of facial recognition products in response to the first wave of Black Lives Matter protests in the U.S. The Michigan University study -- a part of STPP's Technology Assessment Project -- employs an analogical case comparison method to look at previous uses of security technology like CCTV cameras and metal detectors as well as biometric technologies and anticipate the implications of facial recognition.

Oliver Hofmann and his research group at the Institute of Solid State Physics at TU Graz are working on the optimization of modern electronics. A key role in their research is played by interface properties of hybrid materials consisting of organic and inorganic components, which are used, for example, in OLED displays or organic solar cells. The team simulates these interface properties with machine-learning-based methods. The results are used in the development of new materials to improve the efficiency of electronic components. The researchers have now taken up the phenomenon of long-range charge transfer.

Researchers from the US National Institute of Standards and Technology found that face masks are causing facial recognition algorithms to fail as much as 50% of the time. In a report, the US National Institute of Standards and Technology found that face masks were thwarting even the most advanced facial recognition algorithms. Error rates varied from 5% to 50%, depending on an algorithm's capabilities. The results are troubling for the facial recognition industry which has been scrambling to develop algorithms that can identify people through their eyes and nose alone as people turn to face masks amid the coronavirus pandemic. The masks have caused trouble for facial recognition software prompting tech companies to adapt.

PHILADELPHIA - To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, PhD, an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning -- an approach first implemented by Google for keyboards' autocorrect functionality -- trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

A former Google engineer has been sentenced to 18 months in prison after pleading guilty to stealing trade secrets before joining Uber's effort to build robotic vehicles for its ride-hailing service. The sentence handed down Tuesday by U.S. District Judge William Alsup came more than four months after former Google engineer Anthony Levandowski reached a plea agreement with the federal prosecutors who brought a criminal case against him last August. Levandowski, who helped steer Google's self-driving car project before landing at Uber, was also ordered to pay more than $850,000. Alsup had taken the unusual step of recommending the Justice Department open a criminal investigation into Levandowski while presiding over a high-profile civil trial between Uber and Waymo, a spinoff from a self-driving car project that Google began in 2007 after hiring Levandowski to be part of its team. Levandowski eventually became disillusioned with Google and left the company in early 2016 to start his own self-driving truck company, called Otto, which Uber eventually bought for $680 million. He wound up pleading guilty to one count, culminating in Tuesday's sentencing.

There are many photos of Tom Hanks, but none like the images of the leading everyman shown at the Black Hat computer security conference Wednesday: They were made by machine learning algorithms, not a camera. Philip Tully, a data scientist at security company FireEye, generated the hoax Hankses to test how easily open source software from artificial intelligence labs could be adapted to misinformation campaigns. His conclusion: "People with not a lot of experience can take these machine learning models and do pretty powerful things with them," he says. Seen at full resolution, FireEye's fake Hanks images have flaws like unnatural neck folds and skin textures. But they accurately reproduce the familiar details of the actor's face like his brow furrows and green-gray eyes, which gaze cooly at the viewer.

The team has been focusing on improving a brain-computer interface, a device implanted beneath the skull on the surface of a patient's brain. This implant connects the human nervous system to an electronic device that might, for instance, help restore some motor control to a person with a spinal cord injury, or someone with a neurological condition like amyotrophic lateral sclerosis, also called Lou Gehrig's disease. The current generation of these devices record enormous amounts of neural activity, then transmit these brain signals through wires to a computer. But when researchers have tried to create wireless brain-computer interfaces to do this, it took so much power to transmit the data that the devices would generate too much heat to be safe for the patient. Now, a team led by electrical engineers and neuroscientists Krishna Shenoy, PhD, and Boris Murmann, PhD, and neurosurgeon and neuroscientist Jaimie Henderson, MD, have shown how it would be possible to create a wireless device, capable of gathering and transmitting accurate neural signals, but using a tenth of the power required by current wire-enabled systems.

Radiant Earth Foundation has released "LandCoverNet," a human-labelled global land cover classification training dataset. This release contains data across Africa, which accounts for 1/5 of the global dataset. Available for download on Radiant MLHub, the open geospatial library, LandCoverNet will enable accurate and regular land cover mapping for timely insights into natural and anthropogenic impacts on the Earth. Global land cover maps derived from Earth observations are not new, but the influx of open-access high spatial resolution Earth observations, such as that from the European Space Agency's Sentinel missions, coupled with improved computer power, encouraged the development of advanced algorithms. Machine learning models applied to high resolution remotely sensed imagery can classify land cover classes more accurately and faster, given the availability of high-quality training data.