On the far side of an office park in a suburb of Seattle, a supercomputer is teaching itself to beat the stock market. But inside this austere enclosure, an incredible alchemy is taking place. Four hundred computers blink and hum as market data is digested at a rate of one quadrillion calculations per second, firing order requests to electronic traders in Chicago, 2,000 miles away. Outside the containment, a bank of 10 glowing monitors displays the results as money rolls back in. Even now, as the world economy slumps into a recession, Jeff Glickman and his boutique investment firm, J4 Capital, are quietly taking gains. "Suffice it to say we're making a profit in this market," he says.

Researchers at the University of British Columbia are compiling CT scans and chest X-rays from around the world to create a global dataset aimed at helping physicians determine the best treatment courses for people with COVID-19. Thanks to a partnership with Amazon Web Services, the UBC team is sharing its data online for free, with the goal of helping in the battle against the novel coronavirus by using predictive modelling to better diagnose the severity of the disease and improve treatment. Radiology resident Dr. William Parker and his research partner Dr. Savvas Nicolaou, a professor of radiology at UBC and the director of emergency and trauma radiology at Vancouver General Hospital, began collecting CT images from colleagues in multiple countries in January. They developed an artificial intelligence algorithm to better identify the percentage of lung tissue involvement and the subtle patterns of infection documented in the CT scans and what that indicates about how a patient may fare in the long run. Developing a better understanding of how the virus presents in CT images will help doctors identify which patients "will do better to go home and self-isolate and which ones may need more support, like ventilation and ICU admission," Parker told CTV's Your Morning on Friday.

The moon is a treasure trove of valuable resources. Gold, platinum, and many rare Earth metals await extraction to be used in next-generation electronics. But there's one resource in particular that has excited scientists, rocket engineers, space agency officials, industry entrepreneurs--virtually anyone with a vested interest in making spaceflight to distant worlds more affordable. If you split water into hydrogen and oxygen, and then liquefy those constituents, you have rocket fuel. If you can stop at the moon's orbit or a lunar base to refuel, you no longer need to bring all your propellant with you as you take off, making your spacecraft significantly lighter and cheaper to launch.

Greenpeace's report says Microsoft appears to be leading the way with the most oil and contracts, "offering AI capabilities in all phases of oil production." Amazon's contracts are more focused on pipelines, shipping and fuel storage, according to the report. Their tools have been deployed to speed up shale extraction, especially from the Permian Basin of Texas and New Mexico.

Among the challenges endured, the league has drawn criticism for its poor server performance as competitions moved to online-only from its usual lag-less LAN format. Its own players said playing conditions were unfair due to lag in which competitors saw different response times from servers, which impacted their gameplay. Dallas Empire's Ian "Crimsix" Porter called servers during matches held between April 10-12 "unplayable." Minnesota RØKKR's Justin "SiLLY" Fargo-Palmer also made light of the situation during that same weekend's event.

Leqi Liu, a Ph.D. student in the School of Computer Science's Machine Learning Department, has been chosen as a 2020 Open Phil AI fellow. She is one of 10 students across the U.S. to receive a fellowship. The Open Phil AI Fellowship, organized by the Open Philanthropy Project, supports the research of a small group of promising machine learning researchers over five years, and fosters that community with a culture of trust, debate, excitement and intellectual excellence. Liu's research, advised by Assistant Professor Zachary Lipton, aims to develop learning systems that can infer human preferences from their behaviors and help humans achieve their goals. In particular, she is interested in bringing theory from social sciences into algorithmic design.

Nintendo is taking legal action against hackers who sell software enabling people to play pirated video games. According to court documents obtained by Polygon, two lawsuits were filed last week in the US against alleged hackers in Ohio and Washington. The defendants, Nintendo's lawsuit claims, are associated with a group of anonymous hackers called'Team Xecuter' who provide the pirating products. According to court documents, the products allow people to circumvent'technological protection measures' designed to prevent Nintendo's games from being copied or accessed. Once Nintendo's safeguards are bypassed, players can download a modified operating system and play games that have been pirated.

Scientists have identified three biological clues in COVID-19 patients' blood samples to predict their risk of death with up to 90 per cent accuracy. Chinese researchers used machine learning to analyse blood samples of patients in Wuhan and identify the three biomarkers of mortality risk 10 days early. High levels of both an enzyme associated with tissue breakdown and a protein produced during inflammation, as well as low levels of lymphocyte white blood cells, were the key predictors. Using these markers for an early diagnosis of risk of death among people with COVID-19 is vital to prioritise care for the patients who are most at risk. The research provides a method to quickly predict patients at the highest risk, allowing them to receive urgent treatment and potentially reduce the mortality rate.

To prove just how useful Spot, Boston Dynamics' four-legged robot dog, can be, the New Zealand-based robotics company Rocos shared a video of Spot herding sheep across grassy pastures. This is the kind of work Rocos hopes to do as part of a partnership, announced today, with Boston Dynamics. Rocos plans to develop a system that will remotely manage Spot and automate fleets so that they can function independently. In addition to herding sheep, Spot robots might also harvest crops, inspect yields or create real-time maps, Rocos says. These capabilities are all possible now that Spot is more nimble, can handle rugged terrain and can carry infrared and LiDAR cameras.

When Doron Hazan '21 was drafted into the Israeli Defense Forces (IDF) after high school, he had the opportunity to join the army's intelligence unit. It was the obvious choice for the self-described "math and physics nerd" from Kiryat Shmona, a small town in Israel's Hula Valley just south of the Lebanese border. But Hazan was not one to make obvious choices. "All of my life I've been interested in human behavior," says Hazan, a junior who is enrolled in one of MIT's newest majors: computation and cognition, or Course 6-9. Launched in the fall of 2019, Course 6-9 is a joint curriculum offered by the Department of Electrical Engineering and Computer Science (EECS) and the Department of Brain and Cognitive Sciences (BCS).