Computers and other machines have and will continue to change the way people do business and how we live. Many researchers use the term artificial intelligence (AI) to describe the thinking and intelligent behavior demonstrated by machines. While AI can be helpful to human beings, scientists warn, it can also be a threat. We live with artificial intelligence all around us. A few examples are iPhone's personal assistant Siri, searches on the Internet, and autopilot programs on airplanes.

Abstract--A central problem in analyzing networks is partitioning them into modules or communities. One of the best tools for this is the stochastic block model, which clusters vertices into blocks with statistically homogeneous pattern of links. Despite its flexibility and popularity, there has been a lack of principled statistical model selection criteria for the stochastic block model. Here we propose a Bayesian framework for choosing the number of blocks as well as comparing it to the more elaborate degree-corrected block models, ultimately leading to a universal model selection framework capable of comparing multiple modeling combinations. We will also investigate its connection to the minimum description length principle. I NTRODUCTION An important task in network analysis is community detection, or finding groups of similar vertices which can then be analyzed separately [1]. Community structures offer clues to the processes which generated the graph, on scales ranging from face-to-face social interaction [2] through social-media communications [3] to the organization of food webs [4]. However, previous work often defines a "community" as a group of vertices with high density of connections within the group and a low density of connections to the rest of the network. While this type of assortative community structure is generally the case in social networks, we are interested in a more general definition of functional community--a group of vertices that connect to the rest of the network in similar ways. A set of similar predators form a functional group in a food web, not because they eat each other, but because they feed on similar prey.

There is resurging interest, in statistics and machine learning, in solvers for ordinary differential equations (ODEs) that return probability measures instead of point estimates. Recently, Conrad et al. introduced a sampling-based class of methods that are 'well-calibrated' in a specific sense. But the computational cost of these methods is significantly above that of classic methods. On the other hand, Schober et al. pointed out a precise connection between classic Runge-Kutta ODE solvers and Gaussian filters, which gives only a rough probabilistic calibration, but at negligible cost overhead. By formulating the solution of ODEs as approximate inference in linear Gaussian SDEs, we investigate a range of probabilistic ODE solvers, that bridge the trade-off between computational cost and probabilistic calibration, and identify the inaccurate gradient measurement as the crucial source of uncertainty. We propose the novel filtering-based method Bayesian Quadrature filtering (BQF) which uses Bayesian quadrature to actively learn the imprecision in the gradient measurement by collecting multiple gradient evaluations.

Spectral clustering has become a popular technique due to its high performance in many contexts. It comprises three main steps: create a similarity graph between N objects to cluster, compute the first k eigenvectors of its Laplacian matrix to define a feature vector for each object, and run k-means on these features to separate objects into k classes. Each of these three steps becomes computationally intensive for large N and/or k. We propose to speed up the last two steps based on recent results in the emerging field of graph signal processing: graph filtering of random signals, and random sampling of bandlimited graph signals. We prove that our method, with a gain in computation time that can reach several orders of magnitude, is in fact an approximation of spectral clustering, for which we are able to control the error. We test the performance of our method on artificial and real-world network data.

The future predictive performance of a Bayesian model can be estimated using Bayesian cross-validation. In this article, we consider Gaussian latent variable models where the integration over the latent values is approximated using the Laplace method or expectation propagation (EP). We study the properties of several Bayesian leave-one-out (LOO) cross-validation approximations that in most cases can be computed with a small additional cost after forming the posterior approximation given the full data. Our main objective is to assess the accuracy of the approximative LOO cross-validation estimators. That is, for each method (Laplace and EP) we compare the approximate fast computation with the exact brute force LOO computation. Secondarily, we evaluate the accuracy of the Laplace and EP approximations themselves against a ground truth established through extensive Markov chain Monte Carlo simulation. Our empirical results show that the approach based upon a Gaussian approximation to the LOO marginal distribution (the so-called cavity distribution) gives the most accurate and reliable results among the fast methods.

How would you perform accurate classification on a very large dataset, by just looking at a sample of it? One of his recent papers is about big data and similarity metrics. In this work Rocco proposes a deterministic method to obtain subsets from Big Data which are a good representative of the inherent structure in the data itself. This allows one to consider only a subset of the entire dataset, still performing at high accuracy if not better than traditional (eg. As you can see, there is always a solution in Big Data.

Four sister robots built by NASA could be pioneers in the colonization of Mars, part of an advance construction team that sets up a habitat for more fragile human explorers. But first they're finding new homes on Earth and engineers to hone their skills. The space agency has kept one Valkyrie robot at its birthplace, the Johnson Space Center in Houston. It has loaned three others to universities in Massachusetts and Scotland so professors and students can tinker with the 6-foot-tall, 300-pound humanoids and make them more autonomous. One of the robots, nicknamed Val, still hasn't quite harmonized its 28 torque-controlled joints and nearly 200 sensors after arriving at a robotics center at the University of Massachusetts-Lowell. Engineering students let the electricity-powered robot down from a harness and tried to let it walk, only to watch as Val's legs awkwardly lurched and locked into a ballet pose.

Depends where your interests lie. Bristol has excellent applied research in ML/AI but my experience is that there isn't a huge amount of foundational stuff going on in the Engineering dpt AFAIK. I don't really know what's going on at Manchester. Were I in your shoes, I would definitely go to Edinburgh.

"I don't know what you mean - how about a web search for it?" If you want the latest football scores, to add meetings to your calendar or launch an app, today's virtual assistants are relatively good at understanding your voice and doing what's asked. But try to have the type of natural conversation seen in sci-fi movies featuring artificial intelligence systems - from HAL in 2001 to the sultry-voiced operating system Samantha in Spike Jonze's Her - and you'll find your device about as smart as a waterproof teabag. "Google and Apple are painfully aware that their systems are not getting better fast enough because right now Siri and Google Now and the other personal assistant type applications are all programmed by hand," says Steve Young, professor of information engineering at the University of Cambridge. "If you speak to Siri about baseball it seems relatively intelligent, but if you ask it something much less common it doesn't really do anything except for a web search. "That's an indication that the programmers have been busy trying to anticipate what people want to ask about baseball but haven't thought about people who ask about, for example, GPU chips because you don't get many queries about that." Microsoft doesn't yet have a virtual assistant on its Windows Phone platform, but the company is experimenting with AI in lifts and reception desks at its headquarters. Eric Horvitz, managing director of Microsoft's research unit, believes part of the solution involves allowing computers to look beyond questions posed. "The ability of a system to understand more broadly what the overall context of a communication is turns out to be very important," he told the BBC. "There are some critical signals in context.

China is emerging as the biggest market for video games. It is, therefore, great news to investors of Microsoft (NASDAQ:MSFT) that its subsidiary Mojang has stuck a deal with NetEase (NASDAQ:NTES). Mojang granted a 5-year exclusive license to NetEase to distribute the mobile and PC versions of its cult hit game, Minecraft. Mojang will create a China-specific version of Minecraft for NetEase. Microsoft paid 2.5 billion to acquire Mojang in 2014.