Microsoft just showed how artificial intelligence could find its way into many software applications--by writing code on the fly. At the Microsoft Build developer conference today, the company's chief technology officer, Kevin Scott, demonstrated an AI helper for the game Minecraft. The non-player character within the game is powered by the same machine learning technology Microsoft has been testing for auto-generating software code. The feat hints at how recent advances in AI could change personal computing in years to come by replacing interfaces that you tap, type, and click to navigate into interfaces that you simply have a conversation with. The Minecraft agent responds appropriately to typed commands by converting them into working code behind the scenes using the software API for the game.

A robot capable of doing laundry sadly won't be available for the public to buy, as it is only intended to help appliance manufacturers test their washing machines. Gianluca Palli at the University of Bologna, Italy, and his colleagues have developed "a complete robotic laundry operation" in partnership with Electrolux, a Swedish firm that is one of the world's largest appliance makers. The robotic system, which uses pre-existing commercial robots, is able to detect dirty clothes, pick them up and load them into a washing machine, interpret the machine's display and choose the correct wash cycle.

Autonomous cars are predicted to improve fuel efficiency for everyone on the road – an idea that will be put to the test on routes around Nashville, Tennessee, later this year.

In 2020, a machine-learning algorithm helped researchers to develop a potent antibiotic that works against many pathogens (see Nature https://doi.org/ggm2p4; Artificial intelligence (AI) is also being used to aid vaccine development, drug design, materials discovery, space technology and ship design. Within a few years, numerous inventions could involve AI. This is creating one of the biggest threats patent systems have faced. Patent law is based on the assumption that inventors are human; it currently struggles to deal with an inventor that is a machine.

A soft robot with no motors, batteries or computers can roll over a range of surfaces and escape simple mazes by harvesting heat energy and turning it into motion. Jie Yin at North Carolina State University and his colleagues created a spiral-shaped device from a narrow rectangle of rubber-like material impregnated with liquid crystals. When placed on a surface heated to at least 55 C, the areas of the robot touching the surface warm up and expand, while others remain static. This causes a twisting motion that rolls the device along at speeds up to 3.8 millimetres per second. Although the robot has no computational ability, it can achieve relatively complex tasks such as navigating mazes.

Earlier this month, DeepMind presented a new "generalist" AI model called Gato. The model can play the video game Atari, caption images, chat, and stack blocks with a real robot arm, the Alphabet-owned AI lab announced. All in all, Gato can do hundreds of different tasks. But while Gato is undeniably fascinating, in the week since its release some researchers have got a bit carried away. One of DeepMind's top researchers and a coauthor of the Gato paper, Nando de Freitas, couldn't contain his excitement.

The UK's data watchdog has fined a facial recognition company £7.5m for collecting images of people from social media platforms and the web to add to a global database. The Information Commissioner's Office (ICO) also ordered US-based Clearview AI to delete the data of UK residents from its systems. Clearview AI has collected more than 20bn images of people's faces from Facebook, other social media companies and from scouring the web. John Edwards, the UK information commissioner, said Clearview's business model was unacceptable. "Clearview AI Inc has collected multiple images of people all over the world, including in the UK, from a variety of websites and social media platforms, creating a database with more than 20bn images," he said. "The company not only enables identification of those people, but effectively monitors their behaviour and offers it as a commercial service.

Explains Nikola Konstantinov of Switzerland's ETH Zürich, "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin." As artificial intelligence (AI) becomes more widely used to make decisions that affect our lives, making certain it is fair is a growing concern. Algorithms can incorporate bias from several sources, from the people involved in different stages of their development to modelling choices that introduce or amplify unfairness. A machine learning system used by Amazon to pre-screen job applicants was found to display bias against women, for example, while an AI system used to analyze brain scans failed to perform equally well across people of different races. "Fairness in AI is about ensuring that AI models don't discriminate when they're making decisions, particularly with respect to protected attributes like race, gender, or country of origin," says Nikola Konstantinov, a post-doctoral fellow at the ETH AI Center of ETH Zürich, in Switzerland. Researchers typically use mathematical tools to measure the fairness of machine learning systems based on a specific definition of fairness.

Although workflows are very popular, prior to the CWL standards, all workflow systems were incompatible with each other. This means that users who do not use the CWL standards are required to express their computational workflows in a different way each time they use another workflow system, leading to local success but global unportability. The success of workflows is now their biggest drawback. Users are locked into a particular vendor, project, and often a specific hardware setup, hampering sharing and reuse. Even non-academics suffer from this situation, as the lack of standards, or their adoption, hinders effective collaboration on computational methods within and between companies.

If you own a Tesla, or a loved one does, or you're thinking about buying one, or you share public roads with Tesla cars, you might want to watch the new documentary "Elon Musk's Crash Course." Premiering Friday on FX and Hulu, the 75-minute fright show spotlights the persistent dangers of Tesla's automated driving technologies, the company's lax safety culture, Musk's P.T. Barnum-style marketing hype and the weak-kneed safety regulators who seem not to care. Get Screen Gab for weekly recommendations, analysis, interviews and irreverent discussion of the TV and streaming movies everyone's talking about. You may occasionally receive promotional content from the Los Angeles Times. The central through line is the story of Joshua Brown, a rabid Tesla fan and derring-do techno-geek beheaded when his Autopilot-engaged Tesla drove itself at full speed on a Florida highway underneath the trailer of a semi-truck in 2016.