Claims that a chatbot can diagnose medical conditions as accurately as a GP have sparked a row between the software's creators and UK doctors. Babylon, the company behind the NHS GP at Hand app, says its follow-up software achieves medical exam scores that are on-par with human doctors. It revealed the artificial intelligence bot at an event held at the Royal College of Physicians. But another medical professional body said it doubted the AI's abilities. "No app or algorithm will be able to do what a GP does," said the Royal College of General Practitioners.

Microsoft has updated it's facial recognition technology in an attempt to make it less'racist'. It follows a study published in March that criticised the technology for being able to more accurately recognise the gender of people with lighter skin tones. The system was found to perform best on males with lighter skin and worst on females with darker skin. The problem largely comes down to the data being used to train the AI system not containing enough images of people with darker skin tones. Experts from the computing firm say their tweaks have significantly reduced these errors, by up to 20 times for people with darker faces.

Building so-called Smarter Cities has long been touted as a major goal for municipalities around the globe. But building systems capable of responding to events in real time is a major challenge for any government operating on a limited IT budget. The hope is that processes will become integrated enough to create a massive pool of data that will then be employed to drive any number of artificial intelligence (AI) applications. The problem is that while city governments typically have access to massive amounts of data, most of it resides in isolated systems run by departments that are often at odds with one another. In fact, Daniel Newman, principal analyst with Futurum, says smart cities are mostly a figment of vendor marketing imagination. "The trouble with smart cities is they don't exist yet," says Newman.

While a decade ago China was known to be the "world's factory," manufacturing everyday household goods for companies across the globe, in recent years tech and internet companies have redefined the face of Chinese industry. Both Tencent and Alibaba are now among the world's top 10 most valuable companies. Indeed, Chinese companies are now leading the way in the most disruptive global tech trends, including autonomous vehicles, machine learning and blockchain. According to Deloitte, global CFOs' optimism about the Chinese market has never been higher. But all this innovation has come with a side effect: Chinese consumers' expectations of brands and businesses have risen to match the market's optimism.

Studying the invertibility of deep neural networks (DNNs) provides a principled approach to better understand the behavior of these powerful models. Despite being a promising diagnostic tool, a consistent theory on their invertibility is still lacking. We derive a theoretically motivated approach to explore the preimages of ReLU-layers and mechanisms affecting the stability of the inverse. Using the developed theory, we numerically show how this approach uncovers characteristic properties of the network.

Empirical risk minimization is the principal tool for prediction problems, but its extension to relational data remains unsolved. We solve this problem using recent advances in graph sampling theory. We (i) define an empirical risk for relational data and (ii) obtain stochastic gradients for this risk that are automatically unbiased. The key ingredient is to consider the method by which data is sampled from a graph as an explicit component of model design. Theoretical results establish that the choice of sampling scheme is critical. By integrating fast implementations of graph sampling schemes with standard automatic differentiation tools, we are able to solve the risk minimization in a plug-and-play fashion even on large datasets. We demonstrate empirically that relational ERM models achieve state-of-the-art results on semi-supervised node classification tasks. The experiments also confirm the importance of the choice of sampling scheme.

IN THE MODERN FIELD OF ARTIFICIAL INTELLIGENCE, all roads seem to lead to three researchers with ties to Canadian universities. The first, Geoffrey Hinton, a 70-year-old Brit who teaches at the University of Toronto, pioneered the subfield called deep learning that has become synonymous with A.I. The second, a 57-year-old Frenchman named Yann LeCun, worked in Hinton's lab in the 1980s and now teaches at New York University. The third, 54-year-old Yoshua Bengio, was born in Paris, raised in Montreal, and now teaches at the University of Montreal. The three men are close friends and collaborators, so much so that people in the A.I. community call them the Canadian Mafia. In 2013, though, Google recruited Hinton, and Facebook hired LeCun. Both men kept their academic positions and continued teaching, but Bengio, who had built one of the world's best A.I. programs at the University of Montreal, came to be seen as the last academic purist standing. Bengio is not a natural industrialist. He has a humble, almost apologetic, manner, with the slightly stooped bearing of a man who spends a great deal of time in front of computer screens.

If Elon Musk doesn't like something, he'll create his own version. That's exactly what he's done for his children's education by starting a radical ultra-exclusive school at his SpaceX headquarters in Hawthorne, California. For the past four years, the non-profit'experimental' school has been educating the billionaire's five sons, children of some SpaceX employees and a number of gifted students from Los Angeles. The school has some unconventional teaching methods. Reports suggest it allows students to skip subjects they don't like, build flamethrowers and'defeat evil AIs'.

Explosions from a decades-old conflict have left a pockmarked and unstable territory, though many more improvised bombs lie concealed in its vast reaches. Sunlight splays off the beaten edges of Optimus, the smaller robot. If Optimus were programmed to hope, it would hope the object was just a rock and not another bomb. It couldn't afford to take many more hits, and its algorithms have grown wary of the risk. A hulking shape shimmers in the heat as it approaches Optimus, lolling like a huge, headless cowboy.

When the machine learning renaissance started in 2013 – thanks to the development of new graphic processing units accelerating data processing by over 100 times – Frans Cronje and Daniel Schwartzkopff started positioning themselves to fill the gap in the South African market. "Daniel and I met while doing our undergrad studies at the University of Cape Town and collectively came up with the idea to start an artificial intelligence (AI) company," says Frans Cronje, the managing director of DataProphet. "At the time there was hardly any competition in the field and skills were scarce, as you could not even study machine learning here." They initially offered machine learning consultancy services to retail, manufacturing and finance companies and from there catapulted into the products market when they started DataProphet in 2015. "We were relatively new to the game. Working as consultants helped us gain valuable experience and insights into the common challenges faced by these industries, which in turn allowed us to create service solutions for these challenges," says Cronje.