'The head of MI6 will on Monday highlight the urgent need for a new era of spying in which artificial intelligence and robotics are deployed to combat rogue states hellbent on "perpetual confrontation" with the UK. In a rare public speech - only his second in four years in the job - Alex Younger, the Chief of MI6, will say that Britain must enter an age of "fourth generation espionage" to keep the country safe. The MI6 boss - known as "C" - will also emphasise the importance of "strengthening" Britain's security ties with European allies ahead of Brexit, pointing out that "multiple" Islamic State-inspired attacks on the Continent have been disrupted thanks to the co-operation of intelligence agencies. The speech to students at St Andrew's University, where Mr Younger studied, will also warn of the danger of "adversaries" who are "willing to take advantage" of huge leaps in cyber technology to launch attacks on Britain "in ways that fall short of traditional warfare". Mr Younger will single out Russia for its "malign behaviour".

We often think about artificial intelligence (AI) in terms of the benefits it can provide by helping us complete tasks more efficiently. It's important to remember, though, that this technology can be used just as easily for malicious ends. Could it pose more of a cybersecurity threat than we think? Because AI can learn on its own and use that knowledge to complete tasks autonomously, it can help us complete work more efficiently, more cost-effectively, more accurately and with less hands-on effort. Those benefits apply to virtually every sector.

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the 21st century. Its period of phenomenal growth likely lies ahead. Still, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. Hence, the materials and technologies that provide the needed energy represent a critical challenge towards future use-scenarios of AI and should be integrated into their design. Here we make four recommendations for stakeholders and especially decision makers to facilitate a successful trajectory for this technology. First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI. Second, that biomimetic energetic solutions be central to design consideration for future AI. Third, that a pre-competitive space be organized between stakeholder partners and fourth, that a trainee pipeline be established to ensure the human capital required for success in this area.

Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations. SplitNN does not share raw data or model details with collaborating institutions. The proposed configurations of splitNN cater to practical settings of i) entities holding different modalities of patient data, ii) centralized and local health entities collaborating on multiple tasks and iii) learning without sharing labels. We compare performance and resource efficiency trade-offs of splitNN and other distributed deep learning methods like federated learning, large batch synchronous stochastic gradient descent and show highly encouraging results for splitNN.

Interpretability has emerged as a crucial aspect of machine learning, aimed at providing insights into the working of complex neural networks. However, existing solutions vary vastly based on the nature of the interpretability task, with each use case requiring substantial time and effort. This paper introduces MARGIN, a simple yet general approach to address a large set of interpretability tasks ranging from identifying prototypes to explaining image predictions. MARGIN exploits ideas rooted in graph signal analysis to determine influential nodes in a graph, which are defined as those nodes that maximally describe a function defined on the graph. By carefully defining task-specific graphs and functions, we demonstrate that MARGIN outperforms existing approaches in a number of disparate interpretability challenges.

Hospital Readmissions within 30 days after discharge following Coronary Artery Bypass Graft (CABG) Surgery are substantial contributors to healthcare costs. Many predictive models were developed to identify risk factors for readmissions. However, majority of the existing models use statistical analysis techniques with data available at discharge. We propose an ensembled model to predict CABG readmissions using pre-discharge perioperative data and machine learning survival analysis techniques. Firstly, we applied fifty one potential readmission risk variables to Cox Proportional Hazard (CPH) survival regression univariate analysis. Fourteen of them turned out to be significant (with p value < 0.05), contributing to readmissions. Subsequently, we applied these 14 predictors to multivariate CPH model and Deep Learning Neural Network (NN) representation of the CPH model, DeepSurv. We validated this new ensembled model with 453 isolated adult CABG cases. Nine of the fourteen perioperative risk variables were identified as the most significant with Hazard Ratios (HR) of greater than 1.0. The concordance index metrics for CPH, DeepSurv, and ensembled models were then evaluated with training and validation datasets. Our ensembled model yielded promising results in terms of c-statistics, as we raised the the number of iterations and data set sizes. 30 day all-cause readmissions among isolated CABG patients can be predicted more effectively with perioperative pre-discharge data, using machine learning survival analysis techniques. Prediction accuracy levels could be improved further with deep learning algorithms.

Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined within the testing communities of education and hiring over the past half century, exploring the cultural and social context in which different fairness definitions have emerged. In some cases, earlier definitions of fairness are similar or identical to definitions of fairness in current machine learning research, and foreshadow current formal work. In other cases, insights into what fairness means and how to measure it have largely gone overlooked. We compare past and current notions of fairness along several dimensions, including the fairness criteria, the focus of the criteria (e.g., a test, a model, or its use), the relationship of fairness to individuals, groups, and subgroups, and the mathematical method for measuring fairness (e.g., classification, regression). This work points the way towards future research and measurement of (un)fairness that builds from our modern understanding of fairness while incorporating insights from the past.

The International Space Station has got a new team member and if this new shared footage is anything to go by, it's quite the character. AI bot CIMON (Crew Interactive Mobile Companion) was brought aboard earlier this year with the aim of assisting crew and boosting morale. But the floating basketball-shaped robot appeared to have its own opinions about what made for a happy crew, according to a new video shared by the station, which shows CIMON throwing a tantrum when told to stop playing music. The robot even accuses European Space Agency astronaut Alexander Gerst of being'mean' and orders him to'be nice' before demanding to know when it's time for lunch. The video of began simply enough with Gerst asking CIMON, which is brought to life by IBM Watson artificial intelligence, to perform different commands and engages it in small talk throughout the demonstration.

Sponsored "Machine learning is eating the world," writes Clarence Chio in Machine Learning and Security with David Freeman, who heads a team of ML engineers charged with detecting and preventing fraud and abuse across LinkedIn. Chio went on to write that in fact: "Cybersecurity is also eating the world," and he has a point. The UK's National Cyber Security Centre (NCSC) claimed, on its second anniversary in October 2018, it had stopped more than 10 attacks per week, primarily from hostile nation states. Perhaps, unsurprisingly, the rise in threats has led to a boom in sales of cybersecurity software: it will be a $248bn (£194bn) industry by 2023, according to Markets and Markets research. Within this, the future for machine learning is bright.

You've heard of profiling criminals, but welcome to fashion profiling -- the practice of classifying and targeting individuals based on their clothing brand preferences. Fashion profiling played a bigger role in the 2016 American presidential election than anyone realized, according to new information from Christopher Wylie, the Cambridge Analytica whistle-blower. Today at a conference in Britain organized by the fashion industry website The Business of Fashion, Mr. Wylie explained that clothing preferences were a key metric for Cambridge Analytica, whose business was constructing and selling voter profiles drawn from Facebook data. "Fashion data was used to build AI models to help Steve Bannon build his insurgency and build the alt-right," he said. Preferences in clothing and music are the leading indicators of political leaning, he said.