Collin Kaufman at the University of Illinois at Urbana-Champaign and his colleagues built biological robots using 3D-printed muscles made of lab-grown mouse cells. But on their own, the muscles can't do much – what is needed is some way to control them.

Washington, DC (CNN Business)Tesla has said its latest version of Autopilot, its autonomous driving software, is able to stop at traffic lights. But some Tesla drivers are learning it doesn't just stop at red lights, it appears to slow down for green lights, too. Last Friday, Tesla drivers first reported receiving a software update that included "Traffic Light and Stop Sign Control," which is designed to slowdown and stop the vehicle for visible traffic lights or stop signs. Tesla (TSLA) describes the software as being in "beta," meaning it's unfinished and still officially in testing. It's designed to gradually improve as the artificial intelligence that powers it learns from the data that's being collected as Tesla cars drive on public roads, according to a notification in Tesla vehicles when the system is first activated.

Urology fellow, Jeremy Fallot, and nurse, Shauna Harnedy, assist in robotic surgery by Ruban Thanigasalam (out of view) in Sydney, Australia.Credit: Ken Leanfore for Nature Loved by surgeons and patients alike for its ease of use and faster recovery times, the da Vinci surgical robot is less invasive than conventional procedures, and lacks the awkwardness of laparoscopic (keyhole) surgery. But the robot's US$2-million price tag and negligible effect on cancer outcomes is sparking concern that it's crowding out more affordable treatments. There are more than 5,500 da Vinci robots globally, manufactured by California-based tech giant, Intuitive. The system is used in a range of surgical procedures, but its biggest impact has been in urology, where it has a market monopoly on robot-assisted radical prostatectomies (RARP), the removal of the prostate and surrounding tissues to treat localized cancer. Uptake in the United States, Europe, Australia, China and Japan for performing this procedure has been rapid.

Scientists are scrambling to work out what effect specific measures, such as social distancing, have in slowing the spread of COVID-19.Credit: Ivan Romano/Getty Hong Kong seems to have given the world a lesson in how to effectively curb COVID-19. With a population of 7.5 million, it has reported just 4 deaths. Researchers studying Hong Kong's approach have already found that swift surveillance, quarantine and social-distancing measures, such as the use of face masks and school closures, helped to cut coronavirus transmission -- measured by the average number of people each infected person infects, or R -- to close to the critical level of 1 by early February. Working out the effectiveness of the unprecedented measures implemented worldwide to limit the spread of the coronavirus is now one of scientists' most pressing questions. Researchers hope that, ultimately, they will be able to accurately predict how adding and removing control measures affects transmission rates and infection numbers.

After testing on public roads, Tesla is rolling out a new feature of its partially automated driving system designed to spot stop signs and traffic signals. The update of the electric car company's cruise control and auto-steer systems is a step toward CEO Elon Musk's pledge to convert cars to fully self-driving vehicles later this year. But it also runs contrary to recommendations from the U.S. National Transportation Safety Board that include limiting where Tesla's Autopilot driving system can operate because it has failed to spot and react to hazards in at least three fatal crashes. In a note sent to a group of Tesla owners who were picked to test the stop light and sign recognition feature, the company said it can be used with the Traffic Aware Cruise Control or Autosteer systems. The feature will slow the car whenever it detects a traffic light, including those that are green or blinking yellow.

Despite the huge contributions of deep learning to the field of artificial intelligence, there's something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn't emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data. Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers. In his keynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for "self-supervised learning," his roadmap to solve deep learning's data problem.

Artificial intelligence has become a focus of certain ethical concerns, but it also has some major sustainability issues. Last June, researchers at the University of Massachusetts at Amherst released a startling report estimating that the amount of power required for training and searching a certain neural network architecture involves the emissions of roughly 626,000 pounds of carbon dioxide. This issue gets even more severe in the model deployment phase, where deep neural networks need to be deployed on diverse hardware platforms, each with different properties and computational resources. MIT researchers have developed a new automated AI system for training and running certain neural networks. Results indicate that, by improving the computational efficiency of the system in some key ways, the system can cut down the pounds of carbon emissions involved--in some cases, down to low triple digits.

In the summer of 1956, a small group of mathematicians and computer scientists gathered at Dartmouth College to embark on the grand project of designing intelligent machines. The ultimate goal, as they saw it, was to build machines rivaling human intelligence. As the decades passed and AI became an established field, it lowered its sights. There were great successes in logic, reasoning, and game-playing, but stubborn progress in areas like vision and fine motor-control. This led many AI researchers to abandon their earlier goals of fully general intelligence, and focus instead on solving specific problems with specialized methods.

The last decade has seen a vast increase both in the diversity of applications to which machine learning is applied, and to the import of those applications. Machine learning is no longer just the engine behind ad placements and spam filters; it is now used to filter loan applicants, deploy police officers, and inform bail and parole decisions, among other things. The result has been a major concern for the potential for data-driven methods to introduce and perpetuate discriminatory practices, and to otherwise be unfair. And this concern has not been without reason: a steady stream of empirical findings has shown that data-driven methods can unintentionally both encode existing human biases and introduce new ones.7,9,11,60 At the same time, the last two years have seen an unprecedented explosion in interest from the academic community in studying fairness and machine learning. "Fairness and transparency" transformed from a niche topic with a trickle of papers produced every year (at least since the work of Pedresh56 to a major subfield of machine learning, complete with a dedicated archival conference--ACM FAT*). But despite the volume and velocity of published work, our understanding of the fundamental questions related to fairness and machine learning remain in its infancy.

Women are underrepresented in the fields of science, technology, engineering, and mathematics (STEM) in most countries, including Germany and the U.S.29,32 This was demonstrated in several surveys investigating the proportion of women in the STEM fields for specific populations. Some of these studies, for example, investigated the number of enrolled students10,30 or the percentage of female professors at universities. Other studies analyzed the disparities in research funding.23 Nearly all these surveys selected a particular population of women in consideration of their university degree or their nationality.11,34 Like many other studies investigating the gender gap and its reasons in science, these surveys are usually based on data records from several kinds of registrations or enrollments, for example, the enrollment as student or doctoral student, the registration of finished doctoral theses or the membership as professor in a certain country.1,14,16,28 However, researchers at the postdoctoral level or industrial researchers are often not registered and unfortunately drop out of the surveys.