These magnificent rays are at risk of disappearing due to targeted fishing, being caught as bycatch, and climate change. Scientists at the research collaboration Mobula Conservation are teaming up with artisanal and industrial fishermen to protect them. Also known as "Devil Rays," mobulas are elasmobranchs: a subclass of fish--including sharks, skates, and sawfish--that are distinguished by having skeletons primarily made from cartilage. More than a third of the species in this group are threatened with extinction. Of the nine species of mobulas, seven are endangered and two are vulnerable according to the International Union for Conservation of Nature.

In a small room tucked into a US navy facility outside Memphis, Tennessee, uniformed personnel sit hunched over monitors, their eyes focused on screens as they speak into headsets with clipped efficiency. Computer towers and glowing red keyboards crowd their desks. This is top-of-the-line gear, used for executing combat missions and coordinating strategy – but not with fleets stationed across the world. These sailors are playing video games. On the other end of their headsets and screens are young gamers they hope to inspire. "In 2019, we did a big look at where we were spending our money, looking at where the next generation is," says Lt Aaron Jones, captain of the navy's esports team, as we sit in his office after touring the facility. "This is where they are," Jones continues. "Whether it's Twitch or YouTube or Facebook Gaming, this is what they love."

Is a human-shaped robot like Optimus, the Tesla Bot, the right path to travel if we want to achieve useful robots and automated help in all aspects of our working and personal lives? That's basically the promise of Optimus, which Tesla CEO Elon Musk says will usher in "a fundamental transformation for civilization as we know it." One thing is undeniable: there's an abiding appeal to human shaped and human sized robots, as Irena Cronin, CEO of Infinite Retina, recently told me. Part of the value of copying the human form: the world is built for humans. Cars, homes, machines, factories, warehouses ... all of our built environment is designed by and for human beings as inhabitants, operators, drivers, or workers. And Tesla's not the only one attempting to create humaniform robots.

A "chemputer" is a robotic method of producing drug molecules that uses downloadable blueprints to synthesize organic chemicals via programming. Originated in the University of Glasgow lab of chemist Lee Cronin, the method has produced several blueprints available on the GitHub software repository, including blueprints for Remdesivir, the FDA-approved drug for antiviral treatment of COVID-19. Cronin, who designed the "bird's nest" of tubing, pumps, and flasks that make up the chemputer, spent years thinking of a way researchers could distribute and produce molecules as easily as they email and print PDFs, according to a recent account from CNBC. "If we have a standard way of discovering molecules, making molecules, and then manufacturing them, suddenly nothing goes out of print," Cronin stated. Beyond creating the chemputer, Cronin's team recently took a second major step towards digitizing chemistry with an accessible way to program the machine.

New Zealand scientists have invented a new volcano alert system that they say could have provided warning ahead of last year's White Island disaster. Twenty-one people died when the country's most active volcano, also called Whakaari, suddenly erupted last December with tourists on it. The new system uses machine learning algorithms to analyse real-time data to predict future eruptions. The research was publish in the journal Nature last week. One of the scientists involved in the project, Shane Cronin from the University of Auckland, told the BBC the current system had been "too slow to provide warnings for people [on] the island." "The current [alert system] collects data in real-time but what tends to happen is that this information gets assessed by a panel and they have an expert process... this all takes a while," he said.

Researchers have developed what they say is a breakthrough robotic lab assistant, able to move around a laboratory and conduct scientific experiments just like a human. The machine, designed by scientists from the UK's University of Liverpool, is far from fully autonomous: it needs to be programmed with the location of lab equipment and can't design its own experiments. But by working seven days a week, 22 hours a day (with two hours to recharge every night), it allows scientists to automate time-consuming and tedious research they wouldn't otherwise tackle. In a trial reported in Nature today, the robot's creators, led by PhD student Benjamin Burger, say it was able to perform experiments 1,000 times faster than a human lab assistant, with that speed-up mostly due to the robot's ability to work around the clock without breaks. But Professor Andy Cooper, whose lab developed the robot, tells The Verge that speed is not necessarily the point.

"If I ask an organic chemist to make me a random new molecule, they can't do it," says Lee Cronin, a professor of chemistry at the University of Glasgow. "It's not because they are stupid. They will ask me what type of molecule and what specification. It could take them one week or ten years. Cronin realised that even though that's a difficult ask for a human being, it probably wasn't such a difficult project for a machine learning robot to undertake. "Could we get a robot to start mixing together random chemicals, without any previous knowledge and see what happens?

An AI equipped robotic system that enables synthetic protocells to evolve could help unravel how complex life formed on Earth. The fully automated system, which make use of machine learning, could aid understanding of other complex chemical systems too. Oil-in-water droplets are often used as protocell models because, while chemically simple, they can exhibit complex and cell-like behaviours, such as movement and division. However, in order to work out how specific droplet properties – including viscosity, density and surface tension – result in different behaviours, droplet formulations must be varied. The problem is that the permutations are vast, even for droplets with just four ingredients.

Activate satellite view in Google Maps and head to the Las Vegas strip, and you'll see it: a strange smattering of Y-shaped buildings. Their blueprints put gambling at the center of everything, funneling visitors past slot machines and card tables whether they're en route to a show, their room, a restaurant, or a retail shop. For years, the casino floor was where Vegas resorts made most of their money, and the Y was devilishly good at monetizing it. The Westgate Las Vegas Resort and Casino was the first megacasino to feature the design--a bit of trivia that Mark Waltrip, Westgate Resort's chief operating officer, relays with a mixture of pride and irony. Y-shaped buildings have their issues, after all.

It turns out that a vital missing ingredient in the long-sought after goal of getting machines to think like humans--artificial intelligence--has been lots and lots of data. Last week, at the O'Reilly Strata Hadoop World Conference in New York, Salesforce.com's head of artificial intelligence, Beau Cronin, asserted that AI has gotten a shot in the arm from the big data movement. "Deep learning on its own, done in academia, doesn't have the [same] impact as when it is brought into Google, scaled and built into a new product," Cronin said. In the week since Cronin's talk, we saw a whole slew of companies--startups mostly--come out of stealth mode to offer new ways of analyzing big data, using machine learning, natural language recognition and other AI techniques that those researchers have been developing for decades. One such startup, Cognitive Scale, applies IBM Watson-like learning capabilities to draw insights from vast amount of what it calls "dark data," buried either in the Web--Yelp reviews, online photos, discussion forums--or on the company network, such as employee and payroll files, noted KM World.