Tesla executives defended the automaker's semi-self-driving system on Monday after it came under scrutiny following a deadly crash involving a Tesla Model S in Texas this month. CEO Elon Musk rejected suggestions that the company's Autopilot was to blame. "This is completely false," he said, adding that journalists who suggested Autopilot was at fault "should be ashamed of themselves." After the crash, Harris County Precinct 4 Constable Mark Herman told multiple outlets, including the Wall Street Journal and Consumer Reports, that investigators were 99.9% sure that no one was behind the wheel when the vehicle crashed. The National Transportation Safety Board and the National Highway Traffic Safety Administration are investigating the crash.

In the study, the researchers developed a method to improve testing strategies during epidemic outbreaks and with relatively limited information be able to predict which individuals offer the best potential for testing. "This can be a first step towards society gaining better control of future major outbreaks and reduce the need to shutdown society," says Laura Natali, a doctoral student in physics at the University of Gothenburg and the lead author of the published study. Machine learning is a type of artificial intelligence and can be described as a mathematical model where computers are trained to learn to see connections and solve problems using different data sets. The researchers used machine learning in a simulation of an epidemic outbreak, where information about the first confirmed cases was used to estimate infections in the rest of the population. Data about the infected individual's network of contacts and other information was used: who they have been in close contact with, where and for how long.

Tesla's Autopilot system can "easily" be used to drive the automaker's vehicles without anyone behind the wheel, Consumer Reports said in a new demonstration. The magazine conducted the study on a test track after a widely publicized Tesla Model S crash in Texas on Saturday when two people were killed in a wreck that sparked an hours-long blaze. Local authorities said it appeared no one was in the driver's seat. The National Transportation Safety Board and National Highway Traffic Safety Administration have opened investigations into the incident. Tesla's Autopilot system enables automatic steering, accelerating and braking on roads with lanes, but it does not work in all situations.

Machine learning could provide up an extra hour of warning time for debris flows along the Illgraben torrent in Switzerland, researchers report at the Seismological Society of America (SSA)'s 2021 Annual Meeting. Debris flows are mixtures of water, sediment and rock that move rapidly down steep hills, triggered by heavy precipitation and often containing tens of thousands of cubic meters of material. Their destructive potential makes it important to have monitoring and warning systems in place to protect nearby people and infrastructure. In her presentation at SSA, Ma?gorzata Chmiel of ETH Zürich described a machine learning approach to detecting and alerting against debris flows for the Illgraben torrent, a site in the European Alps that experiences significant debris flows and torrential events each year. Seismic records from stations located in the Illgraben catchment, from 20 previous debris flow events, were used to train an algorithm to recognize the seismic signals of debris flow formation, accurately detecting early flows 90% of the time. The machine learning system was able to detect all 13 debris flows and torrential events that occurred during a three-month period in 2020.

Lee Sedol, a world-class Go Champion, was flummoxed by the 37th move Deepmind's AlphaGo made in the second match of the famous 2016 series. So flummoxed that it took him nearly 15 minutes to formulate a response. The move was strange to other experienced Go players as well, with one commentator suggesting it was a mistake. In fact, it was a canonical example of an artificial intelligence algorithm learning something that seemed to go beyond just pattern recognition in data -- learning something strategic and even creative. Indeed, beyond just feeding the algorithm past examples of Go champions playing games, Deepmind developers trained AlphaGo by having it play many millions of matches against itself.

Using machine learning methods, researchers at TU Graz can predict the structure formation of functionalized molecules at the interfaces of hybrid materials. Now they have also succeeded in looking behind the driving forces of this structure formation. The production of nanomaterials involves self-assembly processes of functionalized (organic) molecules on inorganic surfaces. This combination of organic and inorganic components is essential for applications in organic electronics and other areas of nanotechnology. Until now, certain desired surface properties were often achieved on a trial-and-error basis. Molecules were chemically modified until the best result for the desired surface property was found.

Music recommendation algorithms could be amplifying the industry's existing gender bias problem, according to a study that proposes a new method allowing greater exposure for female artists. The existance of a gender bias in the music industry is not unknown. For instance, a study of the top five music charts in the UK between the years 1960-1995 showed how popular music is affected by a large gender inequality with a bias in listening preferences towards male artists. A recent analysis of music festivals found that lineups are heavily skewed towards male performers and this bias is also said to be prevalent in music streaming apps like Spotify. A growing number of people use streaming platforms to listen to music, and these apps use algorithms to recommend songs based on the users' listening habits.

The European Union on Wednesday unveiled strict regulations to govern the use of artificial intelligence, a first-of-its-kind policy that outlines how companies and governments can use a technology seen as one of the most significant, but ethically fraught, scientific breakthroughs in recent memory. Presented at a news briefing in Brussels, the draft rules would set limits around the use of artificial intelligence in a range of activities, from self-driving cars to hiring decisions, school enrollment selections and the scoring of exams. It would also cover the use of artificial intelligence by law enforcement and court systems -- areas considered "high risk" because they could threaten people's safety or fundamental rights. Some uses would be banned altogether, including live facial recognition in public spaces, though there would be some exemptions for national security and other purposes. The rules have far-reaching implications for major technology companies including Amazon, Google, Facebook and Microsoft that have poured resources into developing artificial intelligence, but also scores of other companies that use the technology in health care, insurance and finance.

Registered reports have been proposed as a way to move from eye-catching and surprising results and toward methodologically sound practices and interesting research questions. However, none of the top-twenty artificial intelligence journals support registered reports, and no traces of registered reports can be found in the field of artificial intelligence. Is this because they do not provide value for the type of research that is conducted in the field of artificial intelligence? Registered reports have been touted as one of the solutions to the problems surrounding the reproducibility crisis. They promote good research practices and combat data dredging1.

A collaboration from UoC Berkeley, Stanford University and Facebook offers a deeper and more granular picture of the actual state of poverty in and across nations, through the use of machine learning. The research, entitled Micro-Estimates of Wealth for all Low-and Middle-Income Countries, is accompanied by a beta website that allows users to interactively explore the absolute and relative economic state of fine-grained areas and pockets of poverty in low and middle-income countries. The framework incorporates data from satellite imagery, topographic maps, mobile phone networks and aggregated anonymized data from Facebook, and is verified against extensive face-to-face surveys, for purposes of reporting relative wealth disparity in a region, rather than absolute estimates of income. A map of global poverty, weighted towards the most affected areas. The system has been adopted by the government of Nigeria as a basis for administering social protection programs, and runs in tandem with the existing framework from the World Bank, the National Social Safety nets Project (NASSP).