The UK government is due to publish its long awaited Digital Strategy later today, about a year later than originally slated. Existing delays having been compounded by the shock of Brexit. Drafts of the strategy framework seen by TechCrunch suggest its scope and ambition vis-a-vis digital technologies has been pared back and repositioned vs earlier formulations of the plan, dating from December 2015 and June 2016, as the government recalibrated to factor in last summer's referendum vote for the UK to leave the European Union. Since the earlier drafts were penned there has also of course been a change of leadership (and direction) at the top of government. And Prime Minister Theresa May appointed a new cabinet, including digital minister, Matt Hancock, who replaced Ed Vaizey.

When the United Kingdom voted to leave the European Union in June, ultimately tanking the British pound, traders with access to Cambridge, Massachusetts-based artificial intelligence platform Kensho had a special advantage. With a few keystrokes on Kensho's AI-powered platform, traders quickly combed through an intelligence-grade database of information and in seconds learned that populist votes such as Brexit historically led to an extended drop in the local currency, washing out any short-term recovery. That's exactly what happened in the days and months after Brexit. The pound plunged to three-decade lows in July, sinking to $1.28 versus the dollar, before rallying slightly to $1.33. The currency has been in a slump since then and currently sits at $1.24.

Roborace, the driverless car championship that has been under development for more than a year, unveiled its vision for the future on stage Monday at Mobile World Congress in Barcelona. For the startup, that future is an electric race car that can reach a top speed of 199 miles per hour that's driven by software, not humans. The car was revealed by Roborace CEO Denis Sverdlov and the company's chief design officer Daniel Simon during a keynote address on the evolution of autonomous vehicles. Simon, who designed the car, is an automotive futurist responsible for creating vehicles for movies, including the cycles in Tron: Legacy. "Roborace opens a new dimension where motorsport as we know it meets the unstoppable rise of artificial intelligence," Simon said Monday.

Helicopter drones which could shoot down Islamic State's new remote-controlled weapons are under development, the Defence Secretary announced yesterday. Sir Michael Fallon said the terror group's latest drone tactics in Iraq – in which they drop grenades from the skies – had been a'wake-up call' for Britain. Unmanned craft would enable the UK to stay ahead of enemies as they would be faster, fly for longer, and spare pilots from any risk, he said. In a speech at Oxford University to engineers and academics, he said drone wars were the future, adding: 'Science fiction will soon be science fact. Unmanned warfare is coming absolutely.'

In this paper we propose a modified version of the simulated annealing algorithm for solving a stochastic global optimization problem. More precisely, we address the problem of finding a global minimizer of a function with noisy evaluations. We provide a rate of convergence and its optimized parametrization to ensure a minimal number of evaluations for a given accuracy and a confidence level close to 1. This work is completed with a set of numerical experimentations and assesses the practical performance both on benchmark test cases and on real world examples.

Multivariate clustering in astrophysics is a recent development justified by the bigger and bigger surveys of the sky. The phylogenetic approach is probably the most unexpected technique that has appeared for the unsupervised classification of galaxies, stellar populations or globular clusters. On one side, this is a somewhat natural way of classifying astrophysical entities which are all evolving objects. On the other side, several conceptual and practical difficulties arize, such as the hierarchical representation of the astrophysical diversity, the continuous nature of the parameters, and the adequation of the result to the usual practice for the physical interpretation. Most of these have now been solved through the studies of limited samples of stellar clusters and galaxies. Up to now, only the Maximum Parsimony (cladistics) has been used since it is the simplest and most general phylogenetic technique. Probabilistic and network approaches are obvious extensions that should be explored in the future.

The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work, we introduce a novel formulation of DGPs based on random feature expansions that we train using stochastic variational inference. This yields a practical learning framework which significantly advances the state-of-the-art in inference for DGPs, and enables accurate quantification of uncertainty. We extensively showcase the scalability and performance of our proposal on several datasets with up to 8 million observations, and various DGP architectures with up to 30 hidden layers.

To guide their behavior, humans and animals rely on previously learned knowledge about the world. Since the world is complex and models of the world are never perfect, the question arises whether we should trust our internal world model that we have built from past data or whether we should readjust it when we receive a new data sample. In noisy environments, a single data sample may not be reliable and in general we need to average over several data samples. However, when a structural change occurs in the environment, the most recent data samples are the most informative ones and we should put more weight on recent data samples than on earlier ones. Indeed, both humans and animals adaptively adjust the relative contribution of old and newly acquired data during learning (Behrens et al., 2007; Nassar et al., 2012; Krugel et al., 2009; Pearce and Hall, 1980) and rapidly adapt to changing environments (Pearce and Hall, 1980; Wilson et al., 1992; Holland, 1997).

Explosive growth in data and availability of cheap computing resources have sparked increasing interest in Big learning, an emerging subfield that studies scalable machine learning algorithms, systems, and applications with Big Data. Bayesian methods represent one important class of statistic methods for machine learning, with substantial recent developments on adaptive, flexible and scalable Bayesian learning. This article provides a survey of the recent advances in Big learning with Bayesian methods, termed Big Bayesian Learning, including nonparametric Bayesian methods for adaptively inferring model complexity, regularized Bayesian inference for improving the flexibility via posterior regularization, and scalable algorithms and systems based on stochastic subsampling and distributed computing for dealing with large-scale applications.

Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations when it comes to 1) flexibility in terms of quality measures and constraints that can be used, and/or 2) guarantees with respect to sampling accuracy. We therefore present Flexics, the first flexible pattern sampler that supports a broad class of quality measures and constraints, while providing strong guarantees regarding sampling accuracy. To achieve this, we leverage the perspective on pattern mining as a constraint satisfaction problem and build upon the latest advances in sampling solutions in SAT as well as existing pattern mining algorithms. Furthermore, the proposed algorithm is applicable to a variety of pattern languages, which allows us to introduce and tackle the novel task of sampling sets of patterns. We introduce and empirically evaluate two variants of Flexics: 1) a generic variant that addresses the well-known itemset sampling task and the novel pattern set sampling task as well as a wide range of expressive constraints within these tasks, and 2) a specialized variant that exploits existing frequent itemset techniques to achieve substantial speed-ups. Experiments show that Flexics is both accurate and efficient, making it a useful tool for pattern-based data exploration.