Standard approaches to probabilistic reasoning require that one possesses an explicit model of the distribution in question. But, the empirical learning of models of probability distributions from partial observations is a problem for which efficient algorithms are generally not known. In this work we consider the use of bounded-degree fragments of the "sum-of-squares" logic as a probability logic. Prior work has shown that we can decide refutability for such fragments in polynomial-time. We propose to use such fragments to answer queries about whether a given probability distribution satisfies a given system of constraints and bounds on expected values. We show that in answering such queries, such constraints and bounds can be implicitly learned from partial observations in polynomial-time as well. It is known that this logic is capable of deriving many bounds that are useful in probabilistic analysis. We show here that it furthermore captures useful polynomial-time fragments of resolution. Thus, these fragments are also quite expressive.

This, the 50th-anniversary summer of Arthur C. Clarke's and Stanley Kubrick's "2001: A Space Odyssey," is arguably the beginning of big-budget Hollywood science fiction as we now know it. Without "2001," the "Star Wars," "Alien," and the "Star Trek" film franchises might not be the force they are today. For this decidedly serious and sometimes ponderous 1968 film opus proved that science fiction could be both profitable and profound. "Space Odyssey: Stanley Kubrick, Arthur C. Clarke and the Making of a Masterpiece," is author Michael Benson's recently-published, fascinating and extraordinarily detailed take on the pair's four-year collaboration. The film's principal photography began on a U.K. sound stage at Shepperton Studios outside London on December 30, 1965. Benson writes "2001: A Space Odyssey" encompassed four million years of human evolution, from pre-human Australopithecine man-apes struggling to survive in southern Africa, through to twenty-first-century space-faring Homo sapiens, then on to the death and rebirth of their Odysseus astronaut, Dave Bowman, as an eerily posthuman "star child."

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'.