The Curiosity Mars Rover is now smart enough to pick its own targets for exploration, according to a new study. The secret to Curiosity's better brain was a software update sent from the ground in October 2015, called the Autonomous Exploration for Gathering Increased Science (AEGIS). This was the first time artificial intelligence had been tried on a remote probe, and the results have shown that similar AI techniques could be applied to future missions, according to the NASA scientists working on the project. AEGIS allows the rover to be "trained" to identify rocks with certain characteristics that scientists on the ground want to investigate. This is valuable because Curiosity's human controllers can't be in direct contact with the rover all the time.

Global GDP will be 14% higher in 2030 as a result of AI – the equivalent of an additional $15.7 trillion. This makes it the biggest commercial opportunity in today's fast changing economy according to new research by PwC. Drawing on a detailed analysis of the business impact of AI Sizing the prize outlines the economies that are set to gain the most from AI. AI will contribute $15.7 trillion to the global economy in 2030, more than the current output of China and India combined. Labour productivity improvements are expected to account for over half of all economic gains from AI over the period 2016-2030. Increased consumer demand resulting from AI-enabled product enhancements will account for the rest.

As so often is the case, science fiction has become science fact. A report published by the Royal Society and the British Academy suggests that there should not be three but just one overarching principle to govern the intelligent machines that we will soon be living alongside: "Humans should flourish." According to Prof Dame Ottoline Leyser, who co-chairs the Royal Society's science policy advisory group, human flourishing should be the key to how intelligent systems governed. "This was the term that really encapsulated what we wanted to say," she told BBC News. "The thriving of people and communities needs to be put first, and we think Asimov's principles can be subsumed into that."

Naver will acquire France-based Xerox Research Centre Europe (XRCE), the company announced, in a bid to strengthen its AI business and expand in Europe. South Korea's largest search giant this year created a lab to focus on AI research. Earlier this month, it announced a partnership with US chip giant Qualcomm to expand its AI platform, named Clova, which is a joint project with subsidiary and chat giant Line. It partnered with compatriot Mirae Asset Daewoo, Korea's largest brokerage company, to apply AI in financial services. It is also applying AI and machine learning technology to its search and apps. The 80 employees at the research centre will join Naver, with the deal expected to close in the third quarter.

Robots could soon be able to help children with autism in ways that doctors are unable to, researchers claim. It is hoped a special machine will work as a therapist and improve the ability of young sufferers to hold conversations. The device, designed by a team of international experts, will also be able to teach them how to take turns - a skill that many patients often lack. It works by scanning children with an autism spectrum disorder (ASD) for their facial expressions and body movements in certain scenarios. These images are then assessed by the robot, called Nao, to determine the most effective way of getting a child's attention.

For all the excitement around artificial intelligence and other automation technologies, we've only seen a fraction of the opportunities automation will create. With all the change still to come, it's worth considering now the role these technologies will play in your company's future. The practical benefits of automation are many: fewer data-entry errors, faster customer service response times, workload automation, better resource management, and the ability to turn legacy data into powerful insights. These functions enable your company to operate more efficiently. They also empower your employees to excel in new and exciting ways.

The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In this work, we present a density compiler for a probabilistic language with failure and both discrete and continuous distributions, and provide a proof of its soundness. The compiler greatly reduces the development effort of domain experts, which we demonstrate by solving inference problems from various scientific applications, such as modelling the global carbon cycle, using a standard Markov chain Monte Carlo framework.

We consider the goodness-of-fit testing problem of distinguishing whether the data are drawn from a specified distribution, versus a composite alternative separated from the null in the total variation metric. In the discrete case, we consider goodness-of-fit testing when the null distribution has a possibly growing or unbounded number of categories. In the continuous case, we consider testing a Lipschitz density, with possibly unbounded support, in the low-smoothness regime where the Lipschitz parameter is not assumed to be constant. In contrast to existing results, we show that the minimax rate and critical testing radius in these settings depend strongly, and in a precise way, on the null distribution being tested and this motivates the study of the (local) minimax rate as a function of the null distribution. For multinomials the local minimax rate was recently studied in the work of Valiant and Valiant. We re-visit and extend their results and develop two modifications to the chi-squared test whose performance we characterize. For testing Lipschitz densities, we show that the usual binning tests are inadequate in the low-smoothness regime and we design a spatially adaptive partitioning scheme that forms the basis for our locally minimax optimal tests. Furthermore, we provide the first local minimax lower bounds for this problem which yield a sharp characterization of the dependence of the critical radius on the null hypothesis being tested. In the low-smoothness regime we also provide adaptive tests, that adapt to the unknown smoothness parameter. We illustrate our results with a variety of simulations that demonstrate the practical utility of our proposed tests.

A rising topic in computational journalism is how to enhance the diversity in news served to subscribers to foster exploration behavior in news reading. Despite the success of preference learning in personalized news recommendation, their over-exploitation causes filter bubble that isolates readers from opposing viewpoints and hurts long-term user experiences with lack of serendipity. Since news providers can recommend neither opposite nor diversified opinions if unpopularity of these articles is surely predicted, they can only bet on the articles whose forecasts of click-through rate involve high variability (risks) or high estimation errors (uncertainties). We propose a novel Bayesian model of uncertainty-aware scoring and ranking for news articles. The Bayesian binary classifier models probability of success (defined as a news click) as a Beta-distributed random variable conditional on a vector of the context (user features, article features, and other contextual features). The posterior of the contextual coefficients can be computed efficiently using a low-rank version of Laplace's method via thin Singular Value Decomposition. Efficiencies in personalized targeting of exceptional articles, which are chosen by each subscriber in test period, are evaluated on real-world news datasets. The proposed estimator slightly outperformed existing training and scoring algorithms, in terms of efficiency in identifying successful outliers.

We consider the binary classification problem when data are large and subject to unknown but bounded uncertainties. We address the problem by formulating the nonlinear support vector machine training problem with robust optimization. To do so, we analyze and propose two bounding schemes for uncertainties associated to random approximate features in low dimensional spaces. The proposed techniques are based on Random Fourier Features and the Nystr\"om methods. The resulting formulations can be solved with efficient stochastic approximation techniques such as stochastic (sub)-gradient, stochastic proximal gradient techniques or their variants.