Developing policy informed by science and technology is now more complex than ever. Policymakers must address supply chains, climate change, inequality, technological breakthroughs, misinformation and more. Using artificial intelligence (AI) to mine the literature could put policymaking on a sounder footing. Advanced big-data and natural-language-processing models enable decision makers to look beyond conventional indicators and expert discussions. Millions of scientific articles, patents and market reports can be readily analysed to identify megatrends or fading topics, and to provide predictive opportunities (see go.nature.com/31snkp5). Machine learning can create maps of national competencies and centres of excellence of science and technology.

These being pandemic times, a recent visit to the Silicon Valley offices of drone startup Skydio involved slipping past dumpsters into the deserted yard behind the company's loading dock. Moments later, a black quadcopter eased out of the large open door sounding like a large and determined wasp. Skydio is best known for its "selfie drones," which use onboard artificial intelligence to automatically follow and film a person, whether they're running through a forest or backcountry skiing. The most recent model, released last fall, costs $999. The larger and more severe-looking machine that greeted WIRED has similar autonomous flying skills but aims to expand the startup's technology beyond selfies into business and government work, including the military.

Cars have not been good for the environment, to put it lightly. Someday, self-driving cars will appear widely in the US. Wouldn't it be nice if they also helped reduce greenhouse gas emissions? Trouble is, making an electric car self-driving requires tradeoffs. Electric vehicles have limited range, and the first self-driving cars are expected to be deployed as roving bands of robotaxis, traveling hundreds of miles each day.

Researchers at NASA have been hard at work on a pilot AI system intended to help future exploration missions find evidence of life on other planets in our solar system. Machine learning algorithms will help exploration devices analyze soil samples on Mars and return the most relevant data to NASA. The pilot program is currently slated for a test run during the ExoMars mission that will see its launch in mid-2022. As IEEE Spectrum reports, the decision to use machine learning and artificial intelligence to aid the search for life on other planets was driven largely by Erice Lyness, the head of the Goddard Planetary Environments Lab at NASA. Lyness needed to come up with ways of automating aspects of geochemical analyses of samples taken in other parts of our solar system.

In 2019, 53% of global data and analysis established by decision-makers announced that artificial intelligence is set up, or entirely development inside their company. Here are the artificial intelligence forecasts for 2020. It is important to see that these findings are obtained from statistics revealing percentages calculated from the observation of Fortune 500 companies. The Fortune 500 companies are recognized as the absolute most profitable in the United States. The study shows finding that 29% of developers have worked on AI and machine learning in recent years. The findings came from a Forrester study.

A team of scientists has developed a technique that automatically makes written sentences more polite. Why it matters: As the authors themselves note in the paper, it is "imperative to use the appropriate level of politeness for smooth communication in conversations." And what better to determine the appropriate level of politeness than an unfeeling machine-learning algorithm? What's new: In a paper presented this week at the annual meeting of the Association for Computational Linguistics, researchers from Carnegie Mellon University analyzed a dataset of 1.39 million sentences, each of which was labeled with a politeness score. Of note: The researchers used the "Enron Corpus" as a dataset -- hundreds of thousands of emails exchanged by Enron employees and preserved by the federal government during its investigation of the now-defunct energy firm.

Fintechs lead as Verdict lists the top five terms tweeted on disruptive tech in Q2 2020, based on data from GlobalData's Influencer Platform. The top tweeted terms are the trending industry discussions happening on Twitter by key individuals (influencers) as tracked by the platform. New technologies and increased collaboration with fintechs shaping payments and the role of fintechs startups in transforming financial services, and innovation, were popularly discussed in Q2 2020. According to an article shared by Antonio Grasso, a digital transformation advisor, new technologies and collaborations with fintehcs were defining the future of payments. For instance, payment companies were acquiring or collaborating with SaaS companies focused on serving such as students and restaurants, the article noted.

The high-profile case of a Black man wrongly arrested earlier this year wasn't the first misidentification linked to controversial facial recognition technology used by Detroit police, the Free Press has learned. Last year, a 25-year-old Detroit man was wrongly accused of a felony for supposedly reaching into a teacher's vehicle, grabbing a cell phone and throwing it, cracking the screen and breaking the case. Detroit police used facial recognition technology in that investigation, too. It identified Michael Oliver as an investigative lead. After that hit, the teacher who had his phone snatched from his hands identified Oliver in a photo lineup as the person responsible.

A Japanese chain of sushi restaurants is using an AI-powered app to assess the quality of tuna -- a key step in the preparation of sushi that traditionally requires years of training from experienced human buyers. But can it really replace a human's fish sense? The app, named Tuna Scope, was developed by Japanese advertising firm Dentsu Inc. It uses machine learning algorithms trained on thousands of images of the cross-sections of tuna tails, a cut of the meat that can reveal much about a fish's constitution. From a single picture, the app grades the tuna on a five-point scale based on visual characteristics like the sheen of the flesh and the layering of fat.

A country's AI prowess has major implications for how its citizens live and work -- and its economic and military strength moving into the future. With so much at stake, the narrative of an AI "arms race" between the US and China has been brewing for years. Dramatic headlines suggest that China is poised to take the lead in AI research and use, due to its national plan for AI domination and the billions of dollars the government has invested in the field, compared with the US' focus on private-sector development. But the reality is that at least until the past year or so, the two nations have been largely interdependent when it comes to this technology. It's an area that has drawn attention and investment from major tech heavy hitters on both sides of the Pacific, including Apple, Google and Facebook in the US and SenseTime, Megvii and YITU Technology in China. Generation China is a CNET series that looks at the areas of technology where the country is looking to take a leadership position.