As a technology pioneer, Elon Musk is hardly anyone's idea of a Luddite. So when Musk tweeted that artificial intelligence competition with Russia and China would be the "most likely cause" of World War III, it got people pretty worked up. We were worried about the perils of creating an artificial being long before AI became a science. The term "robot" comes from a play written in 1920 by Karel Čapek called R.U.R -- Rossum's Universal Robots (Rossumovi Univerzální Roboti in the original Czech). The play starts in a factory that manufactures synthetic people to serve as workers.

Capgemini, a global leader in consulting, technology and outsourcing services, has today announced the findings of "Turning AI into concrete value: the successful implementers' toolkit", a study of nearly 1,000 organizations with revenues of more than $500m that are implementing artificial intelligence (AI), either as a pilot or at scale[1]. The research both counters fears that AI will cause massive job losses in the short term, as 83% of firms surveyed say AI has generated new roles in their organizations, and highlights the growth opportunity presented by AI: three-quarters of firms have seen a 10% uplift in sales, directly tied to AI implementation. The report, which surveyed executives from nine countries and across seven sectors, found that four out of five companies (83%) have created new jobs as a result of AI technology. Specifically, organizations are producing jobs at a senior level, with two in three jobs being created at the grade of a manager or above. Furthermore, among organizations that have implemented AI at scale, more than 3 in 5 (63%) said that AI has not destroyed any jobs in their organization. Alongside the trend towards job creation at management level, the report provides further evidence that organizations see AI as a means of reducing the time employees spend on routine and administrative tasks to enable them to deliver more value.

Christina is audience development editor. After graduating from the University of Nottingham reading philosophy and theology in 2013, Christina joined a tech start-up specialising in mobile apps. She has a keen interest in the mobile platform and innovative tech..

We consider the problem of clustering noisy finite-length observations of stationary ergodic random processes according to their generative models without prior knowledge of the model statistics and the number of generative models. Two algorithms, both using the $L^1$-distance between estimated power spectral densities (PSDs) as a measure of dissimilarity, are analyzed. The first one, termed nearest neighbor process clustering (NNPC), relies on partitioning the nearest neighbor graph of the observations via spectral clustering. The second algorithm, simply referred to as $k$-means (KM), consists of a single $k$-means iteration with farthest point initialization and was considered before in the literature, albeit with a different dissimilarity measure. We prove that both algorithms succeed with high probability in the presence of noise and missing entries, and even when the generative process PSDs overlap significantly, all provided that the observation length is sufficiently large. Our results quantify the tradeoff between the overlap of the generative process PSDs, the observation length, the fraction of missing entries, and the noise variance. Finally, we provide extensive numerical results for synthetic and real data and find that NNPC outperforms state-of-the-art algorithms in human motion sequence clustering.

We present a novel method for exact hierarchical sparse polynomial regression. Our regressor is that degree $r$ polynomial which depends on at most $k$ inputs, counting at most $\ell$ monomial terms, which minimizes the sum of the squares of its prediction errors. The previous hierarchical sparse specification aligns well with modern big data settings where many inputs are not relevant for prediction purposes and the functional complexity of the regressor needs to be controlled as to avoid overfitting. We present a two-step approach to this hierarchical sparse regression problem. First, we discard irrelevant inputs using an extremely fast input ranking heuristic. Secondly, we take advantage of modern cutting plane methods for integer optimization to solve our resulting reduced hierarchical $(k, \ell)$-sparse problem exactly. The ability of our method to identify all $k$ relevant inputs and all $\ell$ monomial terms is shown empirically to experience a phase transition. Crucially, the same transition also presents itself in our ability to reject all irrelevant features and monomials as well. In the regime where our method is statistically powerful, its computational complexity is interestingly on par with Lasso based heuristics. The presented work fills a void in terms of a lack of powerful disciplined nonlinear sparse regression methods in high-dimensional settings. Our method is shown empirically to scale to regression problems with $n\approx 10,000$ observations for input dimension $p\approx 1,000$.

We propose a theoretical framework for thinking about score normalization, which confirms that normalization is not needed under (admittedly fragile) ideal conditions. If, however, these conditions are not met, e.g. under data-set shift between training and runtime, our theory reveals dependencies between scores that could be exploited by strategies such as score normalization. Indeed, it has been demonstrated over and over experimentally, that various ad-hoc score normalization recipes do work. We present a first attempt at using probability theory to design a generative score-space normalization model which gives similar improvements to ZT-norm on the text-dependent RSR 2015 database.

In phase retrieval we want to recover an unknown signal $\boldsymbol x\in\mathbb C^d$ from $n$ quadratic measurements of the form $y_i = |\langle{\boldsymbol a}_i,{\boldsymbol x}\rangle|^2+w_i$ where $\boldsymbol a_i\in \mathbb C^d$ are known sensing vectors and $w_i$ is measurement noise. We ask the following weak recovery question: what is the minimum number of measurements $n$ needed to produce an estimator $\hat{\boldsymbol x}(\boldsymbol y)$ that is positively correlated with the signal $\boldsymbol x$? We consider the case of Gaussian vectors $\boldsymbol a_i$. We prove that - in the high-dimensional limit - a sharp phase transition takes place, and we locate the threshold in the regime of vanishingly small noise. For $n\le d-o(d)$ no estimator can do significantly better than random and achieve a strictly positive correlation. For $n\ge d+o(d)$ a simple spectral estimator achieves a positive correlation. Surprisingly, numerical simulations with the same spectral estimator demonstrate promising performance with realistic sensing matrices. Spectral methods are used to initialize non-convex optimization algorithms in phase retrieval, and our approach can boost the performance in this setting as well. Our impossibility result is based on classical information-theory arguments. The spectral algorithm computes the leading eigenvector of a weighted empirical covariance matrix. We obtain a sharp characterization of the spectral properties of this random matrix using tools from free probability and generalizing a recent result by Lu and Li. Both the upper and lower bound generalize beyond phase retrieval to measurements $y_i$ produced according to a generalized linear model. As a byproduct of our analysis, we compare the threshold of the proposed spectral method with that of a message passing algorithm.

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly from pixels. Deep reinforcement learning algorithms are also applied to robotics, allowing control policies for robots to be learned directly from camera inputs in the real world. In this survey, we begin with an introduction to the general field of reinforcement learning, then progress to the main streams of value-based and policy-based methods. Our survey will cover central algorithms in deep reinforcement learning, including the deep $Q$-network, trust region policy optimisation, and asynchronous advantage actor-critic. In parallel, we highlight the unique advantages of deep neural networks, focusing on visual understanding via reinforcement learning. To conclude, we describe several current areas of research within the field.

BMW has announced that it's making Alexa available in all its 2018 model year cars (including the Mini brand) starting in mid-2018 for the US, UK and Germany. You can use the voice assistant for typical Alexa tasks like the news or smart home control, as well as driving-oriented features like navigation or finding businesses. In many instances, voice responses will include relevant "visual cards" on your car's control display. The move isn't at all shocking: this is Amazon's attempt to claim a foothold in the automotive space before it's too late. Apple and Google already have a strong presence thanks to CarPlay and Android Auto, and Amazon risked sitting on the sidelines if it didn't announce some major deals.

The cybersecurity industry has always had a fortress mentality: Firewall the perimeter! But that mindset has failed--miserably, as each new headline-generating hack reminds us. Even if you do patch all your software, the way Equifax didn't, or you randomize all your passwords, the way most of us don't, bad actors are going to get past your heavily guarded gate, into your network. And once they do, they're free to go wild. Scott Rosenberg is an editor at Backchannel.