With a wide base that tapers and expands to a fat midsection, upon which is perched a tiny head, the Russian robot named Promobot IR77 is no C-3PO. It evokes nothing so much as an artificially intelligent snowman, down to the machine's white-as-fresh-powder paint job. Like another famous snowman and itinerant – Frosty – the robot seems to have acquired a taste for skipping town, too. On June 16 the robot fled its creators, as The Washington Post reported. The story goes that an engineer working at Promobot Laboratories, in the Russian city of Perm, had left a gate open.

Robots in Europe may soon be classified as "electronic persons" if the European Union adopts a recently submitted proposal. Owners of these robots would be liable to paying social security on each robot in an unprecedented step meant to address the rising presence of robotic workers in the EU. The proposal calls for "the creation of a European Agency for robotics and artificial intelligence in order to provide the technical, ethical and regulatory expertise." Robots are being used in exponentially greater numbers in factories and also taking on tasks ranging from surgery to manufacturing and even personal care. Robots are becoming so ubiquitous that there are growing fears over unemployment, wealth inequality and alienation.

The Trolley Problem is an ethical brainteaser that's been entertaining philosophers since it was posed by Philippa Foot in 1967: A runaway train will slaughter five innocents tied to its track unless you pull a lever to switch it to a siding on which one man, also innocent and unawares, is standing. Pull the lever, you save the five, but kill the one: what is the ethical course of action? The problem has run many variants over time, including ones in which you have to choose between a trolley killing five innocents or personally shoving a man who is fat enough to stop the train (but not to survive the impact) into its path; a variant in which the fat man is the villain who tied the innocents to the track in the first place, and so on. Now it's found a fresh life in the debate over autonomous vehicles. The new variant goes like this: your self-driving car realizes that it can either divert itself in a way that will kill you and save, say, a busload of children; or it can plow on and save you, but the kids all die.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

Marko Balabanovic, Chief Technology Officer at Digital Catapult, writes about the barriers facing machine learning companies, particularly when they are looking to scale. Machine learning techniques, within the field of Artificial Intelligence, are becoming increasingly effective and important for data innovators. The major challenges facing fast-growing organisations have been well documented in the Scale-Up Report, and include recruiting skilled employees, building leadership capability, accessing customer and finance, and navigating infrastructure. However, for companies whose products and services use machine learning, we see two more specific barriers: access to skilled machine learning specialists, and access to large pools of data with which to train their algorithms. Both are exacerbated by the dominant position of the "GAFA" major internet companies (Google, Apple, Facebook and Amazon), who are rapidly acquiring large machine learning teams and have many advantages in acquiring training data through the data and channels they already control.

The traditional definition of artificial intelligence is the ability of machines to execute tasks and solve problems in ways normally attributed to humans. Some tasks that we consider simple--recognizing an object in a photo, driving a car--are incredibly complex for AI. Machines can surpass us when it comes to things like playing chess, but those machines are limited by the manual nature of their programming; a 30 gadget can beat us at a board game, but it can't do--or learn to do--anything else. This is where machine learning comes in. Show millions of cat photos to a machine, and it will hone its algorithms to improve at recognizing pictures of cats.

There's a chance it could bring the mood down. Having chosen your shiny new driverless car, only one question remains on the order form: whether your spangly, futuristic vehicle be willing to kill you? To buyers more accustomed to talking models and colours, the query might sound untoward. But for manufacturers of autonomous vehicles (AVs), the dilemma it poses is real. If a driverless car is about to hit a pedestrian, should it swerve and risk killing its occupants?

Self-driving car technology is advancing at a rapid pace, thanks to work by technology and automotive companies ranging from Google to Ford. While still years from being widely adopted, driverless cars are increasingly capable of handling a wide variety of driving conditions. With the basic technical hurdles out of the way, driverless car experts now face a different set of problems -- namely, ethical issues. Among the most pressing questions: What should a driverless car do if it faces a choice between putting its passengers at risk or harming someone outside the vehicle? New research reveals that people are conflicted about such a dilemma.

Would you buy a driverless car that is programmed to kill you? Ok, how about a car programmed to kill you if it's the only way to avoid plowing into a crowd of dozens? That's one of the conundrums an international group of researchers put to 2,000 US residents through six online surveys. The questions varied the number of people that would be sacrificed or saved in each instance--if you want to try it for yourself, see if you'd make a good martyr here. The study, just published in the journal Science is the latest attempt to answer ethics' classic "trolley problem"--forcing you to choose between saving one life and saving many more.

Self-driving cars have a lot of learning to do before they can replace the roughly 250 million vehicles on U.S. roads today. They need to know how to navigate when their pre-programmed maps are out of date. They need to know how to visualize the lane dividers on a street that's covered with snow. And, if the situation arises, they'll need to know whether it's better to mow down a group of pedestrians or spare their lives by steering off the road, killing all passengers onboard. Once self-driving cars are logging serious miles, they're sure to find themselves in situations where an accident is unavoidable.