Read the posts here, then write your own. The idea that we're shifting to the "next economy," to borrow the title of an O'Reilly Media conference I recently co-hosted with Tim O'Reilly in San Francisco, presupposes that our current one is ending. And that of course can be an unsettling prospect. People are wondering how they'll pay the bills. What life will be like in a world where AI, augmented reality, and the Internet of Things proliferate so rapidly that even the most diehard technophiles begin to wonder how long they can keep up with the treadmill of progress.

CAMBRIDGE, UK: Professor Stephen Hawking on Wednesday opened a new artificial intelligence research centre at Britain's Cambridge University. Funded by a  10 million (11.2 million-euro, 12.3-million) grant from the Leverhulme Trust, the centre's express aim is to ensure AI is used to benefit humanity. Opening the new centre, Hawking said it was not possible to predict what might be achieved with AI. "Perhaps with the tools of this new technological revolution, we will be able to undo some of the damage done to the natural world by the last one - industrialisation. "And surely we will aim to finally eradicate disease and poverty. Every aspect of our lives will be transformed.

Since the final episode of hit sitcom Friends first aired in 2004, many fans have clung to the hope of a reunion. Earlier this year, the show's co-creator Marta Kauffman quashed that idea emphatically: "There will never be a Friends reunion movie," she told E! News. Could she be any clearer? But for those still mourning the gang, there is some sort of hope. A team of researchers at the University of Leeds seeks to immortalize popular characters as digital avatars that you might eventually chat with in the way you talk to Siri, Alexa or a similar virtual assistant. As spotted by Prosthetic Knowledge, the team is building a series of algorithms that can recognize and track individual characters and capture their body language, facial expressions and voice.

For some reason, the word "robot" does not sit right with the most of us. We have this set idea that one day, the robots will take over the planet and destroy the human race. I am trying to guess where this fear comes from. Could it be Stephen Hawking's repeated predictions? And still, we are afraid that if the robots become too smart, the chances of a bad ending for us, the humans, increase.

LONDON Jaguar Land Rover, Ford and Tata Motors are testing connected cars which can communicate with each other using technology designed to speed up journeys and cut accidents, the first such trials in Britain.

Tesla has begun equipping all its new cars with self-driving hardware. Elon Musk, Tesla's CEO, tweeted Wednesday night that the new Tesla drives itself with no human input, using has eight cameras, 12 ultrasonars, and radar. All this hardware is mounted so the technology is not visible to drivers. The company's current slate of cars being built now, the Model S sedan and the Model X SUV, have the hardware that will eventually make full autonomy possible. Cars previously built don't have the new hardware and -- this is the moment, in the article where early adopters groan -- Musk said in a tweet that retrofitting vehicles won't be possible.

Many real-world control applications, from economics to robotics, are characterized by the presence of multiple conflicting objectives. In these problems, the standard concept of optimality is replaced by Pareto-optimality and the goal is to find the Pareto frontier, a set of solutions representing different compromises among the objectives. Despite recent advances in multi-objective optimization, achieving an accurate representation of the Pareto frontier is still an important challenge. In this paper, we propose a reinforcement learning policy gradient approach to learn a continuous approximation of the Pareto frontier in multi-objective Markov Decision Problems (MOMDPs). Differently from previous policy gradient algorithms, where n optimization routines are executed to have n solutions, our approach performs a single gradient ascent run, generating at each step an improved continuous approximation of the Pareto frontier. The idea is to optimize the parameters of a function defining a manifold in the policy parameters space, so that the corresponding image in the objectives space gets as close as possible to the true Pareto frontier. Besides deriving how to compute and estimate such gradient, we will also discuss the non-trivial issue of defining a metric to assess the quality of the candidate Pareto frontiers. Finally, the properties of the proposed approach are empirically evaluated on two problems, a linear-quadratic Gaussian regulator and a water reservoir control task.

We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures. Many important machine learning and statistical tasks fit into this framework, including multi-instance learning and point estimation problems without analytical solution (such as hyperparameter or entropy estimation). Despite the large number of available heuristics in the literature, the inherent two-stage sampled nature of the problem makes the theoretical analysis quite challenging, since in practice only samples from sampled distributions are observable, and the estimates have to rely on similarities computed between sets of points. To the best of our knowledge, the only existing technique with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which often performs poorly in practice), and the domain of the distributions to be compact Euclidean. In this paper, we study a simple, analytically computable, ridge regression-based alternative to distribution regression, where we embed the distributions to a reproducing kernel Hilbert space, and learn the regressor from the embeddings to the outputs. Our main contribution is to prove that this scheme is consistent in the two-stage sampled setup under mild conditions (on separable topological domains enriched with kernels): we present an exact computational-statistical efficiency trade-off analysis showing that our estimator is able to match the one-stage sampled minimax optimal rate [Caponnetto and De Vito, 2007; Steinwart et al., 2009]. This result answers a 17-year-old open question, establishing the consistency of the classical set kernel [Haussler, 1999; Gaertner et. al, 2002] in regression. We also cover consistency for more recent kernels on distributions, including those due to [Christmann and Steinwart, 2010].

We present a sparse estimation and dictionary learning framework for compressed fiber sensing based on a probabilistic hierarchical sparse model. To handle severe dictionary coherence, selective shrinkage is achieved using a Weibull prior, which can be related to non-convex optimization with $p$-norm constraints for $0 < p < 1$. In addition, we leverage the specific dictionary structure to promote collective shrinkage based on a local similarity model. This is incorporated in form of a kernel function in the joint prior density of the sparse coefficients, thereby establishing a Markov random field-relation. Approximate inference is accomplished using a hybrid technique that combines Hamilton Monte Carlo and Gibbs sampling. To estimate the dictionary parameter, we pursue two strategies, relying on either a deterministic or a probabilistic model for the dictionary parameter. In the first strategy, the parameter is estimated based on alternating estimation. In the second strategy, it is jointly estimated along with the sparse coefficients. The performance is evaluated in comparison to an existing method in various scenarios using simulations and experimental data.

This paper deals with a natural stochastic optimization procedure derived from the so-called Heavy-ball method differential equation, which was introduced by Polyak in the 1960s with his seminal contribution [Pol64]. The Heavy-ball method is a second-order dynamics that was investigated to minimize convex functions f . The family of second-order methods recently received a large amount of attention, until the famous contribution of Nesterov [Nes83], leading to the explosion of large-scale optimization problems. This work provides an in-depth description of the stochastic heavy-ball method, which is an adaptation of the deterministic one when only unbiased evalutions of the gradient are available and used throughout the iterations of the algorithm. We first describe some almost sure convergence results in the case of general non-convex coercive functions f . We then examine the situation of convex and strongly convex potentials and derive some non-asymptotic results about the stochastic heavy-ball method. We end our study with limit theorems on several rescaled algorithms.