Earlier this month, Bill Gates took the stage at the Recode conference to talk about philanthropy with his wife, Melinda. They discussed mobile payments, contraception, and billionaires giving away their fortunes. Then the conversation turned to artificial intelligence, and Gates grinned and swiveled in his giant red leather chair. "Certainly, it's the most exciting thing going on," he said. It's the big dream that anybody who's ever been in computer science has been thinking about."

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.

Google Research cooperated with OpenAI, Stanford University, and the University of California to publish a research paper highlighting the five main problems with machine learning systems that can lead to accidents. The problems, although minor now, can escalate to concerning levels during artificial intelligence development and operation.

PRO: Chris Bishop, director of Microsoft Research in Cambridge, said earlier this year that he believes the hyperbole around the AI risks could jeopardise any future developments that may in fact assist humanity. "Any scenario in which AI is an existential threat to humanity is not just around the corner," he told the Guardian. Referring to the views of high-profile cynics like professor Stephen Hawking, Bishop said: "I think they must be talking decades away for those comments to make any sense. Right now we are in control of that technology and we can make lots of choices about the paths that we follow." Oren Etzioni, chief executive of the Allen Institute for AI and professor of computer science at the University of Washington, meanwhile says the popular dystopian vision of AI is wrong because it "equates intelligence with autonomy".

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 system consists of a mountable hardware band that scans up to 200 meters in every direction along with software that automatically avoids detected obstacles. If objects larger than a meter enter that radius, the radar system will detect it and automatically move the drone to avoid a collision. Arbe Robotics claims that its solution only uses 5 percent battery life throughout the flight. Obviously, this is for a civilian solution detecting slow-moving obstacles. DARPA has been innovating a collision avoidance system for drones to dodge speedy things like small aircraft and other drones.

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.

This guest post is part of the Festschrift of the Blogosphere celebrating HealthBlawg's Tenth Blogiversary. Festschrift posts are appearing throughout the month of June 2016. A recent article in The Commonwealth Fund blog, "Envisioning a Digital Health Advisor," raises the question of being able to use smartphone apps to get real-time, accurate and personalized guidance for health concerns. While one can envision the convenience, affordability and peace of mind that would result from their use, such services face a number of hurdles before they become reality. As a result, the "digital revolution" has not yet greatly affected most people's interactions with the health care system.

Northwestern University's Ken Forbus is closing the gap between humans and machines. Using cognitive science theories, Forbus and his collaborators have developed a model that could give computers the ability to reason more like humans and even make moral decisions. Called the structure-mapping engine (SME), the new model is capable of analogical problem solving, including capturing the way humans spontaneously use analogies between situations to solve moral dilemmas. "In terms of thinking like humans, analogies are where it's at," said Forbus, Walter P. Murphy Professor of Electrical Engineering and Computer Science in Northwestern's McCormick School of Engineering. "Humans use relational statements fluidly to describe things, solve problems, indicate causality, and weigh moral dilemmas."

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.