Inspired by the matching of supply to demand in logistical problems, the optimal transport (or Monge--Kantorovich) problem involves the matching of probability distributions defined over a geometric domain such as a surface or manifold. In its most obvious discretization, optimal transport becomes a large-scale linear program, which typically is infeasible to solve efficiently on triangle meshes, graphs, point clouds, and other domains encountered in graphics and machine learning. Recent breakthroughs in numerical optimal transport, however, enable scalability to orders-of-magnitude larger problems, solvable in a fraction of a second. Here, we discuss advances in numerical optimal transport that leverage understanding of both discrete and smooth aspects of the problem. State-of-the-art techniques in discrete optimal transport combine insight from partial differential equations (PDE) with convex analysis to reformulate, discretize, and optimize transportation problems. The end result is a set of theoretically-justified models suitable for domains with thousands or millions of vertices. Since numerical optimal transport is a relatively new discipline, special emphasis is placed on identifying and explaining open problems in need of mathematical insight and additional research.

We propose a Monte Carlo algorithm to sample from high-dimensional probability distributions that combines Markov chain Monte Carlo (MCMC) and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high-dimensionality, explicit comparison with standard MCMC and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard schemes. When applied to Bayesian Variable Selection problems, the novel algorithm is orders of magnitude more efficient than available alternative sampling schemes and allows to perform fast and reliable fully Bayesian inferences with tens of thousands regressors.

Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminated. We introduce BREX, a new bootstrapping method that protects against such contamination by highly effective confidence assessment. This is achieved by using entity and template seeds jointly (as opposed to just one as in previous work), by expanding entities and templates in parallel and in a mutually constraining fashion in each iteration and by introducing higherquality similarity measures for templates. Experimental results show that BREX achieves an F1 that is 0.13 (0.87 vs. 0.74) better than the state of the art for four relationships.

Dynamic topic modeling facilitates the identification of topical trends over time in temporal collections of unstructured documents. We introduce a novel unsupervised neural dynamic topic model named as Recurrent Neural Network-Replicated Softmax Model (RNNRSM), where the discovered topics at each time influence the topic discovery in the subsequent time steps. We account for the temporal ordering of documents by explicitly modeling a joint distribution of latent topical dependencies over time, using distributional estimators with temporal recurrent connections. Applying RNN-RSM to 19 years of articles on NLP research, we demonstrate that compared to state-of-the art topic models, RNNRSM shows better generalization, topic interpretation, evolution and trends. We also introduce a metric (named as SPAN) to quantify the capability of dynamic topic model to capture word evolution in topics over time.

In Positional-Slotted Object-Applicative (PSOA) RuleML, a predicate application (atom) can have an Object IDentifier (OID) and descriptors that may be positional arguments (tuples) or attribute-value pairs (slots). PSOA RuleML 1.0 specifies for each descriptor whether it is to be interpreted under the perspective of the predicate in whose scope it occurs. This perspectivity dimension refines the space between oidless, positional atoms (relationships) and oidful, slotted atoms (frames): While relationships use only a predicate-scope-sensitive (predicate-dependent) tuple and frames use only predicate-scope-insensitive (predicate-independent) slots, PSOA RuleML 1.0 uses a systematics of orthogonal constructs also permitting atoms with (predicate-)independent tuples and atoms with (predicate-)dependent slots. This supports data and knowledge representation where a slot attribute can have different values depending on the predicate. PSOA thus extends object-oriented multi-membership and multiple inheritance. Based on objectification, PSOA laws are given: Besides unscoping and centralization, the semantic restriction and transformation of describution permits rescoping of one atom's independent descriptors to another atom with the same OID but a different predicate. For inheritance, default descriptors are realized by rules. On top of a metamodel and a Grailog visualization, PSOA's atom systematics for facts, queries, and rules is explained. The presentation and (XML-)serialization syntaxes of PSOA RuleML 1.0 are introduced. Its model-theoretic semantics is formalized by extending the interpretation functions for dependent descriptors. The open PSOATransRun system since Version 1.3 realizes PSOA RuleML 1.0 by a translator to runtime predicates, including for dependent tuples (prdtupterm) and slots (prdsloterm). Our tests show efficiency advantages of dependent and tupled modeling.

Predicting the future is risky business. You never really know if you are going to get it right. While experts may not agree on exactly how work will change in the next decades, there is growing consensus that "we find ourselves at the edge of another industrial revolution," according to Professor Sabine Kunst, president of the Humboldt University, Berlin. "Advances in artificial intelligence, the Internet of Things, and Big Data are already profoundly shifting all aspects of society -- how we work, connect, organize politically, and learn as human beings," she continues. What to anticipate, how to manage these changes, and how to ensure humans do not get left behind is what business leaders, researchers, academics, policy makers, and innovators met to discuss at the recent SAP research round table on the Future of Work at the SAP Innovation Center in Potsdam, Germany.

While the job losses generate the most interest and headlines, the losses only tell part of the story. Dig a little bit deeper into the hype cycle and you'll see Gartner also predicts AI will create 2.3 million jobs by 2020, driving a net gain of 500,000 new jobs. The question is no longer whether AI will fundamentally change the workplace. The true question is how companies can successfully use AI in ways that enables, not replaces, the human workforce, helping to make humans faster, more efficient and more productive. Andy Peart is chief strategy and marketing officer at Barcelona, Spain-based Artificial Solutions, a global specialist in natural language interaction.

But a new project could change all that. Known as In Codice Ratio, it uses a combination of artificial intelligence and optical-character-recognition (OCR) software to scour these neglected texts and make their transcripts available for the very first time. If successful, the technology could also open up untold numbers of other documents at historical archives around the world. OCR has been used to scan books and other printed documents for years, but it's not well suited for the material in the Secret Archives. Traditional OCR breaks words down into a series of letter-images by looking for the spaces between letters.

Daedalus Pavilion is a 3D printed architectural installation premiered in GPU Technology Conference, Amsterdam in October 2016.

It has been an incredible few years watching artificial intelligence (AI) emerge from the world of academia to become a mainstream business practice, and even being mentioned in Teen Vogue. AI is arguably the biggest technology opportunity for the UK economy today, so it's good to see the government taking this seriously by making it a core part of its modern industrial strategy. Last Thursday, the government agreed its AI sector deal with international tech firms, which will see £1bn of investment put into the industry. The step-changes in the capacity to collect and process massive amounts of data, combined with unprecedented accessibility of scalable cloud computing power and a wave of global entrepreneurship, has unlocked the AI technology developed in the 1950s, leading to massive innovation. AI already enables me to plan traffic-free journeys, play the perfect next music track, unlock my iPhone using my face, set a timer using my voice – and soon, hopefully, drive better. It is being used to reduce energy consumption, to improve teaching in schools, and to help detect disease.