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.

Despite apparently being up for sale, Alphabet's Boston Dynamics has revealed its latest robot creation. The robotics firm is best known for Atlas, its 5 foot 9 humanoid robot, and spot, a four legged'dog robot'. Now, a new mini version of spot with a strange extendable neck has been shown off helping around the house. The robor has an extenable neck and is shown helping around the house in a new video from Alphabet's secretive firm. 'SpotMini is a new smaller version of the Spot robot, weighing 55 lbs dripping wet (65 lbs if you include its arm.)' the secretive firm said.

It brings a whole new meaning to the phrase'saving face'. Living bone grown from stem cells found in fat could soon be used to help reconstruct the faces of people injured in accidents or who have undergone cancer surgery. Scientists have for the first time grown large sections of bone in the laboratory before implanting them into pigs to repair damage to their jaws. The bone was grown on a scaffold inside a bioreactor (pictured) that kept it under mechanical strain to ensure it grew in a way that would be similar to bone grown in the body. The researchers say the implants can precisely replicate the original anatomical structure of the facial bone they are replacing, meaning facial features can be restored.

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 most important subpackages provide implementations of boilerplate code that is relevant to machine-learning problems. These include computer vision, natural language processing, and speech processing. Other subpackages may be smaller and focus on more specific problems or even specific data sets.

Over the past year, questions about how emerging technologies will impact employment have taken on a new tenor. Will robots take over our jobs? One thing is indisputable: automation and artificial intelligence (AI) will displace workers in the IT and business process outsourcing services industry. Such tectonic shifts have occurred every few decades over the last two centuries. With each wave of new technology and each accompanying paradigm shift, jobs have disappeared.

In a recent interview at the Code Conference in California, technology entrepreneur Elon Musk suggested we are living inside a computer simulation. On first hearing, this claim seems far-fetched. But could there be some substance to Musk's thinking? As founder of a number of high-profile companies, such as Tesla and Space X, Musk's business interests lie firmly in leading technologies. Key to his claim is that computer games have evolved rapidly over the past 40 years to the point that, inside the next few years, they will be fully immersive, with a computer-generated and controlled world seamlessly merged with the physical world.

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.