Achieving production-level governance with machine-learning projects currently presents unique challenges. A new space of tools and practices is emerging under the name MLOps. The space is analogous to DevOps but tailored to the practices and workflows of machine learning. Machine learning models make predictions for new data based on the data they have been trained on. Managing this data in a way that can be safely used in live environments is challenging, and one of the key reasons why 80 per cent of data science projects never make it to production – an estimate from Gartner.

A new hospital ward run entirely by robots has opened in Wuhan, China, in a bid to protect medical staff from contracting the coronavirus. On 7 March, about 200 patients exhibiting early symptoms of covid-19 were ushered into the new ward, which is in a converted sports centre in Wuhan, the city where the coronavirus outbreak started. The robots deliver food, drinks and drugs to the patients, and keep the ward clean.

Many plants naturally bend towards bright light. Now a robot has been built that copies a technique plants use to do the same thing. Creating a robot that can sense and adjust automatically to its environment without any need for programming or maintenance is one of the major goals of robotics. A machine that could control and regulate itself in this way can then behave like a living organism, says Bilge Baytekin at Bilkent University in Turkey.

A self-driving car hurtling along the highway and weaving through traffic has less understanding of what might cause an accident than a child who's just learning to walk. A new experiment shows how difficult it is for even the best artificial intelligence systems to grasp rudimentary physics and cause and effect. It also offers a path for building AI systems that can learn why things happen. The experiment was designed "to push beyond just pattern recognition," says Josh Tenenbaum, a professor at MIT's Center for Brains Minds & Machines, who led the work. "Big tech companies would love to have systems that can do this kind of thing."

Scientists who designed an artificially intelligent robot that helped children with autism boost their learning and social skills hope such technology could one day aid others with the developmental disorder. The study saw seven children with mild to moderate autism take home what is known as a socially assistive robot, named Kiwi, for a month. According to a statement by the University of Southern California where the team is based, the participants from the Los Angeles area were aged between three and seven years old, and played space-themed games with the robot almost daily. As Kiwi was fitted with machine-learning technology, it was able to provide unique feedback and instructions to the children based on their abilities. For instance, if the child got a question wrong Kiwi would give prompts to help them solve it, and tweak the difficulty levels to challenge the child appropriately.

As night falls in London, Georgina Rowlands and Anna Hart start applying makeup. Rowlands has long narrow blue triangles and thin white rectangles criss-crossing her face. Hart has a collection of red, orange and white angular shapes on hers. They're two of the four founders of the Dazzle Club, a group of artists set up last year to provoke discussion about the growing using of facial recognition technology. The group holds monthly silent walks through different parts of London to raise awareness about the technology, which they say is being used for "rampant surveillance."

Researchers have discovered a new means to target voice-controlled devices by propagating ultrasonic waves through solid materials in order to interact with and compromise them using inaudible voice commands without the victims' knowledge. Called "SurfingAttack," the attack leverages the unique properties of acoustic transmission in solid materials -- such as tables -- to "enable multiple rounds of interactions between the voice-controlled device and the attacker over a longer distance and without the need to be in line-of-sight." In doing so, it's possible for an attacker to interact with the devices using the voice assistants, hijack SMS two-factor authentication codes, and even place fraudulent calls, the researchers outlined in the paper, thus controlling the victim device inconspicuously. The research was published by a group of academics from Michigan State University, Washington University in St. Louis, Chinese Academy of Sciences, and the University of Nebraska-Lincoin. The results were presented at the Network Distributed System Security Symposium (NDSS) on February 24 in San Diego.

Until January few had heard of Clearview AI, a company that has scraped billions of publicly available images from millions of websites in order to build a facial image search engine app. Clearview claims that more than six hundred law enforcement agencies have used its technology in the last year. News that police officers can search against a plethora of images uploaded to the most popular social media platforms has prompted outcry from officials, activists, and civil libertarians. Clearview's technology should concern everyone who values privacy and security. Clearview CEO Hoan Ton-That has been on the defensive since a New York Times report raised the company's profile from relative obscurity to the topic of a nationwide privacy discussion.

The idea came to Zheng around the turn of the Lunar New Year. Wuhan had just been put on lockdown and the number of cases and deaths was rising rapidly every day. As an engineer, Zheng wanted to do something to contribute to the relief effort. On the first day of the Lunar New Year, he heard from his friend, Dong Jiahong, executive president at Beijing's Tsinghua Changgung Hospital, that the biggest problem was that of frontline workers getting infected. Gathering a team, Zheng set to work converting two mechanized robotic arms with the same technology used on space stations and lunar explorers.

A recent article in the Financial Times argued -- fairly -- that despite the billions of dollars poured into "AI" companies, investors have, on the whole, not seen returns consistent with the hype. There are exceptions of course, but, by and large, the promise(s) appear to have not been met, as of yet. The argument was not simply a lamentation, however, with the author suggesting that the next wave of focused AI solutions might indeed generate better results and returns. Such a sentiment is not uncommon in technology. In order to garner investment, entrepreneurs employ hyperbolic language to excite potential investors and the business press follows this lead in order to ensure that they don't miss out on the appearance of prescience. So, out of the gates, there is much promise and little delivered and when this gap is revealed, negativity enters the scene.