NHS Vale of York CCG has rolled out predictive intervention technology to identify patients at risk of unplanned hospital care. Health Navigator uses analytics and machine learning techniques to identify patients who may benefit from health coaching, particularly those with long-term health conditions. Delivered by registered clinicians, the service is designed to support patients with complex conditions and empower them to take control of their health, thus reducing A&E admissions and unplanned emergency care. The project has been commissioned by NHS Vale of York CCG and aims to address the NHS's increasing demand for urgent and emergency care services, as highlighted in figures released by NHS Digital recently which showed that emergency admissions have peaked nationally. Evidence from a local randomised control trial (RCT) at York Teaching Hospital showed a 36% reduction in A&E attendances for patients supported by health coaching.

We study the problem of binary classification in the setting where the learner does not have access to a conventional training data set with correctly labeled instances. In stead, the learner has access to several data sets for which the true labels have been randomly corrupted, with each data set having possibly different sample size and degree of corruption. Previous work has considere d learning from a single corrupted data set, but the problem considered here raises the natural question of how best to aggregate and weight the information from these multiple corrupted data sets according to t he sample size and degree of corruption. We extend the method of corruption corrected losses (Nataraja n et al., 2018) to this setting and establish a generalization error bound for kernel-based predictors. By optim izing this bound, we obtain a precise and interpretable scheme for aggregating the various corrupted sou rces according to the degree of corruption. We then apply our framework to the problem of learning from label pr oportions (LLP), which is another weak supervision setting for binary classification. In this problem, t raining data come in the form of bags. Each bag contains unlabeled feature vectors (patterns) and is an notated with the proportion of patterns arising from class 1. We argue that this problem can be reduced to th e first problem studied, and apply our results to obtain the most general theoretical analysis of this pro blem to date.

Precise estimation of uncertainty in predictions for AI systems is a critical factor in ensuring trust and safety. Conventional neural networks tend to be overconfident as they do not account for uncertainty during training. In contrast to Bayesian neural networks that learn approximate distributions on weights to infer prediction confidence, we propose a novel method, Information Robust Dirichlet networks, that learns the Dirichlet distribution on prediction probabilities by minimizing the expected $L_p$ norm of the prediction error and an information divergence loss that penalizes information flow towards incorrect classes, while simultaneously maximizing differential entropy of small adversarial perturbations to provide accurate uncertainty estimates. Properties of the new cost function are derived to indicate how improved uncertainty estimation is achieved. Experiments using real datasets show that our technique outperforms state-of-the-art neural networks, by a large margin, for estimating in-distribution and out-of-distribution uncertainty, and detecting adversarial examples.

In this work, we argue that the implications of Pseudo and Quantum Random Number Generators (PRNG and QRNG) inexplicably affect the performances and behaviours of various machine learning models that require a random input. These implications are yet to be explored in Soft Computing until this work. We use a CPU and a QPU to generate random numbers for multiple Machine Learning techniques. Random numbers are employed in the random initial weight distributions of Dense and Convolutional Neural Networks, in which results show a profound difference in learning patterns for the two. In 50 Dense Neural Networks (25 PRNG/25 QRNG), QRNG increases over PRNG for accent classification at +0.1%, and QRNG exceeded PRNG for mental state EEG classification by +2.82%. In 50 Convolutional Neural Networks (25 PRNG/25 QRNG), the MNIST and CIFAR-10 problems are benchmarked, in MNIST the QRNG experiences a higher starting accuracy than the PRNG but ultimately only exceeds it by 0.02%. In CIFAR-10, the QRNG outperforms PRNG by +0.92%. The n-random split of a Random Tree is enhanced towards and new Quantum Random Tree (QRT) model, which has differing classification abilities to its classical counterpart, 200 trees are trained and compared (100 PRNG/100 QRNG). Using the accent and EEG classification datasets, a QRT seemed inferior to a RT as it performed on average worse by -0.12%. This pattern is also seen in the EEG classification problem, where a QRT performs worse than a RT by -0.28%. Finally, the QRT is ensembled into a Quantum Random Forest (QRF), which also has a noticeable effect when compared to the standard Random Forest (RF)... ABSTRACT SHORTENED DUE TO ARXIV LIMIT

A new Senate bill would require tech companies to label internet-connected devices equipped with either a camera or microphone. Introduced by Cory Gardner, a Republican senator from Colorado, the Protecting Privacy in our Homes Act is intended to enhance consumer privacy as more and more tech devices come equipped with surveillance tools that aren't always obvious. The Federal Trade Commission would be responsible for creating the specific language for the label and for determining and enforcing penalties for non-compliance. Amazon's Alexa (pictured above) comes with a microphone that records users even when they're not using the device. The bill would exclude devices marketed specifically as cameras or microphones.

Rep. Will Hurd (R-TX) discusses how President Trump calling on China to investigate 2020 presidential candidate Joe Biden could have an impact on U.S.-China trade talks. The Trump administration has put Hunter Biden's business dealings in its crosshairs. One of the 27 Chinese companies the Commerce Department added to its so-called entity list of firms barred from doing business with the U.S. is Megvii Technology, a business in which BHR, the cross-border investment arm of China's Bohai Industrial Investment Fund, owns a stake in. Biden and his father, Vice President Joe Biden -- who's seeking the Democratic Party's nomination to run against Trump -- have come under scrutiny over the younger man's business dealings in Ukraine. House Democrats are conducting an impeachment inquiry into a whistleblower's claim that the president asked the Ukrainian government to investigate Hunter Biden's work there in the hopes of generating ammunition for his 2020 reelection campaign.

The U.S. National Science Foundation has announced the creation of a new program that will significantly advance research in AI and accelerate the development of transformational, AI-powered innovation by allowing researchers to focus on larger-scale, longer-term research. The National Artificial Intelligence Research Institutes program anticipates approximately $120 million in grants next year to fund planning grants and up to six research institutes in order to advance AI research and create national nexus points for U.S. universities, federal agencies, industries, and nonprofits. "Advances in AI are progressing rapidly and demonstrating the potential to transform our lives," says NSF Director France Córdova. "This landmark investment will further AI research and workforce development, allowing us to accelerate the development of transformational technologies and catalyze markets of the future." "The National Science Foundation is at the cutting edge when it comes to this Administration's efforts to prioritize AI research and development. These institutes will advance our national strategy for U.S. leadership in AI, leverage important multisector R&D partnerships, and support groundbreaking AI innovation for the benefit of the American people," says Michael Kratsios, Chief Technology Officer of the United States, The White House.

"This is a key first step in being able to shed light on serial hijackers' behavior," says MIT Ph.D. candidate Cecilia Testart. Hijacking IP addresses is an increasingly popular form of cyber-attack. This is done for a range of reasons, from sending spam and malware to stealing Bitcoin. It's estimated that in 2017 alone, routing incidents such as IP hijacks affected more than 10 percent of all the world's routing domains. There have been major incidents at Amazon and Google and even in nation-states -- a study last year suggested that a Chinese telecom company used the approach to gather intelligence on western countries by rerouting their Internet traffic through China.

Washington this week targeted Chinese facial recognition startups SenseTime, Megvii and Yitu over national security concerns and foreign policy interests, aggravating the clash between the two economic superpowers over who will dominate the technologies of the future. SenseTime is the second-most valuable artificial intelligence startup in the world, with investments from tech giants SoftBank (SFTBF) and Alibaba (BABA) and a private market valuation of $7.5 billion, according to CB Insights. Megvii and Yitu are worth $4 billion and $2.4 billion respectively, according to CB Insights. The three tech startups, along with a handful of other Chinese firms like AI-driven surveillance camera maker Hikvision and voice recognition firm iFlyTek, are now banned from buying US products or importing American technology. The US Commerce Department added them to a trade blacklist this week, saying the companies had been implicated in human rights violations against Uyghurs and other members of Muslim minority groups in Xinjiang.

RUSI has been commissioned by GCHQ to conduct a research study into the use of artificial intelligence (AI) for national security purposes. The overall aim of the project is to establish an independent evidence base to inform future government policy development and strategic thinking regarding national security uses of AI. As part of this project, RUSI researchers are consulting widely with practitioners and policy-makers from across government, academics, legal experts, technologists and other subject matter experts. RUSI's final report for this project will be published in Spring 2020, and will include recommendations for future government policy regarding the use of AI for national security purposes. GCHQ have stated publicly that the agency "embraces AI, but not as a black box", and that "it is absolutely essential that we have the debates around AI and machine learning in the national security space that will deliver the answers and approaches that will give us public consent".