A £2.7 million project aimed at transforming life for people living alone with dementia, is to be trialled in Cambridgeshire by Anglia Ruskin University music therapists. They will use artificial intelligence to adapt and personalise live radio to try and address the key causes of hospital admission for those suffering from dementia. Radio Me will tackle issues such as agitation and failing to take medication correctly and as a result, it is hoped quality of life will improve with people able to remain living independently at home for longer. Jörg Fachner, professor of music, Health and the brain at ARU, said: "Our role is to investigate precisely how people with dementia can benefit from this interactive radio experience. "Music therapists at ARU and partner organisations will use biomarker responses to fine-tune playlists in order to deliver emotional and cognitive stimulation, and evaluate exactly how interactive music interventions, using AI, can benefit people with dementia in their own homes and in assisted living environments." Professor Eduardo Miranda, from the University of Plymouth, added: "Radio Me builds on research carried out as part of our previous EPSRC-funded project into a brain computer music interface, as well as our work on artificial intelligence, music influencing emotion, and the University's long-running involvement in shaping national policy on dementia.

The rollout of 5G, the fifth generation of wireless technology networks, promises major advancements not only to our personal lives with tech like wearables, but also to many business sectors, including autonomous shipping and transportation, smart cities and the entire workforce. The 5G conversation often leads us to think about new applications for the Internet of Things (IoT), whether it's consumer gadgets, smart refrigerators, enterprise businesses, AI-powered analytics or personal digital assistants. However, there is another side to this ultra-fast and powerful network that can open up new economic opportunities for cities, organizations and the individual worker. Not all cities are created equal, at least from a communications infrastructure and connectivity standpoint. Major tech and innovation hubs like Silicon Valley, New York and Boston are able to accommodate a wealth of workers and companies because they were built with a modern IT and network infrastructure.

The Food and Drug Administration announced Tuesday that it is developing a framework for regulating artificial intelligence products used in medicine that continually adapt based on new data. The agency's outgoing commissioner, Scott Gottlieb, released a white paper that sets forth the broad outlines of the FDA's proposed approach to establishing greater oversight over this rapidly evolving segment of AI products. It is the most forceful step the FDA has taken to assert the need to regulate a category of artificial intelligence systems whose performance constantly changes based on exposure to new patients and data in clinical settings. These machine-learning systems present a particularly thorny problem for the FDA, because the agency is essentially trying to hit a moving target in regulating them. The white paper describes criteria the agency proposes to use to determine when medical products that rely on artificial intelligence will require FDA review before being commercialized.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses--and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI--data.

London's Metropolitan Police force has said it will "carefully consider the contents" of an ethics panel's report before deciding on any continued use of Live Facial Recognition (LFR) technology in public places. The controversial technology was trialled across London for several years, and the London Policing Ethics Panel -- a group commissioned by the London Mayor's office, independent of the police -- issued its report on those trials on Wednesday. The Ethics Panel's conclusions, based on consultations with members of the public, were that most people don't have a big problem with their faces being scanned in public places and cross-checked against lists of suspected criminals -- but there are caveats. According to the report, "57% of respondents thought that in general terms, police use of LFR was acceptable. We will see, however, that the purposes for which it is used makes a significant difference to people's support for its deployment."

For most people, balloons may connote birthday parties, weddings, parades, or on a less celebratory note, meteorology. But, if one new startup has its way, sweeping surveillance may soon make that list too. World View Enterprises Inc., based in Arizona, is working to build what it's calling Stratollites -- balloon mounted-surveillance systems that the company claims can be remotely controlled and adjusted using its own proprietary technology. In an test of unprecedented length, a World View balloon safely completed a 16-day mission, navigating above states in the Western U.S. The feat, says the company, is a major mile marker in the goal of keeping the devices afloat for months at a time. Balloons could be the new method of surveillance according to one Arizona startup, World View.

My 4-year-old daughter and I were deep into a game of checkers one day about three years ago when her eye drifted to a nearby table. There, a black and white board bristled with far more interesting figures, like horses and castles. There was just one problem: I didn't know how. I dimly remembered having learned the basic moves in elementary school, but it never stuck. This fact vaguely haunted me through my life; idle chessboards in hotel lobbies or puzzles in weekend newspaper supplements teased me like reproachful riddles. And so I decided I would learn, if only so I could teach my daughter. The basic moves were easy enough to pick up--a few hours hunched over my smartphone at kids' birthday parties or waiting in line at the grocery store.

The 19th-century physicist Hermann von Helmholtz compared his progress in solving a problem to that of a mountain climber "compelled to retrace his steps because his progress stopped." A mountain climber, von Helmholtz said, "hits upon traces of a fresh path, which again leads him a little further." The physicist's introspection provokes the question: How do creative minds overcome valleys to get to the next higher peak? Because thinking minds are different from evolving organisms and self-assembling molecules, we cannot expect them to use the same means--mechanisms like genetic drift and thermal vibrations--to overcome deep valleys in the landscapes they explore. But they must have some way to achieve the same purpose.

Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks by making a positivity assumption of the Gram-matrix of the neural tangent kernel. However, the performance of gradient descent on classification problems has not been well studied, and further investigation of the problem structure is possible. In this work, we present a partially stronger but reasonable assumption for binary classification problems compared to the positivity assumption of the Gram-matrix, where a data distribution can be perfectly classifiable by a tangent model, and we provide a refined generalization analysis of the gradient descent method for two-layer networks with smooth activations. A remarkable point of this study is that our generalization bound has much better dependence on the network width compared to existing results. As a result, our theory significantly enlarges a class of over-parameterized networks having provable generalization ability, with respect to network width, while most studies require much higher over-parameterization.

Multi-hop Reading Comprehension (RC) requires reasoning and aggregation across several paragraphs. We propose a system for multi-hop RC that decomposes a compositional question into simpler sub-questions that can be answered by off-the-shelf single-hop RC models. Since annotations for such decomposition are expensive, we recast sub-question generation as a span prediction problem and show that our method, trained using only 400 labeled examples, generates sub-questions that are as effective as human-authored sub-questions. We also introduce a new global rescoring approach that considers each decomposition (i.e. the sub-questions and their answers) to select the best final answer, greatly improving overall performance. Our experiments on HotpotQA show that this approach achieves the state-of-the-art results, while providing explainable evidence for its decision making in the form of sub-questions.