OSAKA - With six months to go until the Group of 20 summit in Osaka, the basic agenda has been set for discussion on issues of global importance. But with the G20 process increasingly seen as failing and many leaders likely to arrive in Osaka with severe domestic political problems at home, what will come out of the summit in terms of new agreements to cooperate on issues ranging from climate change to sustainable development to strengthening multilateral free trade and investment is increasingly unclear. On Dec. 1, as the G20 leaders' summit in Buenos Aires closed, Prime Minster Shinzo Abe outlined the agenda of what will be discussed when the leaders meet on June 28 and 29 in Osaka. "At the G20 Osaka Summit, I will set our goal to materialize a free, open, and inclusive and sustainable future society and promote efforts to this end, through our development efforts centered on the sustainable development goals and contributions to addressing global issues, along with driving the world economy through the promotion of free trade and innovation as well as simultaneously achieving economic growth and inequality correction," Abe told the other leaders. Technological innovation, especially in artificial intelligence and robotics, is another area that Japan will ask world leaders to discuss, along with infrastructure development and global health care.

Concern about gentrification has grown in the past decade as the affluent and educated have surged back into cities. But can the pace and pattern of future gentrification be predicted? New research by a team of data scientists and geographers says so. The research, conducted by Jonathan Reades, Jordan De Souza, and Phil Hubbard of Kings College London and published in the Urban Studies journal, uses an artificial intelligence technique called machine learning that essentially trains computer models to learn from past data to predict future patterns. In this case, the research team used data on past gentrification in London to predict where it will next occur.

Towards the end of the 18th century, mechanisation through water and steam power kickstarted the first industrial revolution, changing how goods were manufactured. Following this, electricity and assembly lines allowed mass production to become possible, while, in the 1970s, the adoption of computers and automation facilitated the programming of machines and networks and precipitated the third industrial revolution. We now stand at the brink of the fourth industrial revolution, in which physical and digital technologies are combining through analytics, artificial intelligence, cognitive technologies, and the Internet of Things (IoT) to form intelligent networks along the entire value chain that can control each other autonomously and carry out more informed decision-making than ever before. Digital enterprises can communicate, analyse, and use data to drive intelligent action in the physical world. The sea change is already beginning, with artificial intelligence all around us, from self-driving cars and drones to'smart' chatbots being utilised in customer service systems.

Dimensionality reduction is a first step of many machine learning pipelines. Two popular approaches are principal component analysis, which projects onto a small number of well chosen but non-interpretable directions, and feature selection, which selects a small number of the original features. Feature selection can be abstracted as a numerical linear algebra problem called the column subset selection problem (CSSP). CSSP corresponds to selecting the best subset of columns of a matrix $X \in \mathbb{R}^{N \times d}$, where \emph{best} is often meant in the sense of minimizing the approximation error, i.e., the norm of the residual after projection of $X$ onto the space spanned by the selected columns. Such an optimization over subsets of $\{1,\dots,d\}$ is usually impractical. One workaround that has been vastly explored is to resort to polynomial-cost, random subset selection algorithms that favor small values of this approximation error. We propose such a randomized algorithm, based on sampling from a projection determinantal point process (DPP), a repulsive distribution over a fixed number $k$ of indices $\{1,\dots,d\}$ that favors diversity among the selected columns. We give bounds on the ratio of the expected approximation error for this DPP over the optimal error of PCA. These bounds improve over the state-of-the-art bounds of \emph{volume sampling} when some realistic structural assumptions are satisfied for $X$. Numerical experiments suggest that our bounds are tight, and that our algorithms have comparable performance with the \emph{double phase} algorithm, often considered to be the practical state-of-the-art. Column subset selection with DPPs thus inherits the best of both worlds: good empirical performance and tight error bounds.

Data extracted from software repositories is used intensively in Software Engineering research, for example, to predict defects in source code. In our research in this area, with data from open source projects as well as an industrial partner, we noticed several shortcomings of conventional data mining approaches for classification problems: (1) Domain experts' acceptance is of critical importance, and domain experts can provide valuable input, but it is hard to use this feedback. (2) The evaluation of the model is not a simple matter of calculating AUC or accuracy. Instead, there are multiple objectives of varying importance, but their importance cannot be easily quantified. Furthermore, the performance of the model cannot be evaluated on a per-instance level in our case, because it shares aspects with the set cover problem. To overcome these problems, we take a holistic approach and develop a rule mining system that simplifies iterative feedback from domain experts and can easily incorporate the domain-specific evaluation needs. A central part of the system is a novel multi-objective anytime rule mining algorithm. The algorithm is based on the GRASP-PR meta-heuristic but extends it with ideas from several other approaches. We successfully applied the system in the industrial context. In the current article, we focus on the description of the algorithm and the concepts of the system. We provide an implementation of the system for reuse.

Actor-critic methods can achieve incredible performance on difficult reinforcement learning problems, but they are also prone to instability. This is partly due to the interaction between the actor and critic during learning, e.g., an inaccurate step taken by one of them might adversely affect the other and destabilize the learning. To avoid such issues, we propose to regularize the learning objective of the actor by penalizing the temporal difference (TD) error of the critic. This improves stability by avoiding large steps in the actor update whenever the critic is highly inaccurate. The resulting method, which we call the TD-regularized actor-critic method, is a simple plug-and-play approach to improve stability and overall performance of the actor-critic methods. Evaluations on standard benchmarks confirm this.

Character-based and word-based methods are two different solutions for Chinese word segmentation, the former exploiting sequence labeling models over characters and the latter using word-level features. Neural models have been exploited for character-based Chinese word segmentation, giving high accuracies by making use of external character embeddings, yet requiring less feature engineering. In this paper, we study a neural model for word-based Chinese word segmentation, by replacing the manually-designed discrete features with neural features in a transition-based word segmentation framework. Experimental results demonstrate that word features lead to comparable performance to the best systems in the literature, and a further combination of discrete and neural features obtains top accuracies on several benchmarks.

A lot has been accomplished in the last year to improve comprehension, accuracy and scalability of artificial intelligence, but 2019 will see efforts focused on eliminating bias and making decision making more transparent. Jeff Welser, vice president at IBM Research, says the organization has hit several AI milestones in the past year and is predicting three key areas of focus for 2019. Bringing cognitive solutions powered by AI to a platform businesses can easily adopt is a strategic business imperative for the company, he said, while also increasing understanding of AI and addressing issues such as bias and trust. When it comes to advancing AI, Welser said there's been progress in several areas, including comprehension of speech and analyzing images. IBM's Project Debater work has been able to extend current AI speech comprehension capabilities beyond simple question answering tasks, enabling machines to better understand when people are making arguments, he said, and taking it beyond just "search on steroids."

Why is AI important for Europe? As electricity did in the past, AI is transforming our world. AI is at our fingertips, when we translate texts online or use a mobile app to find the best way to go to our next destination. At home, a smart thermostat can reduce energy bills by up to 25% by analysing the habits of the people who live in the house and adjusting the temperature accordingly. In healthcare, algorithms can help dermatologists make better diagnoses: by detecting, for example, 95% of skin cancers by learning from large sets of medical images.

Recent successes in Reinforcement Learning have encouraged a fast-growing network of RL researchers and a number of breakthroughs in RL research. As the RL community and the body of RL work grows, so does the need for widely applicable benchmarks that can fairly and effectively evaluate a variety of RL algorithms. This need is particularly apparent in the realm of Hierarchical Reinforcement Learning (HRL). While many existing test domains may exhibit hierarchical action or state structures, modern RL algorithms still exhibit great difficulty in solving domains that necessitate hierarchical modeling and action planning, even when such domains are seemingly trivial. These difficulties highlight both the need for more focus on HRL algorithms themselves, and the need for new testbeds that will encourage and validate HRL research. Existing HRL testbeds exhibit a Goldilocks problem; they are often either too simple (e.g. Taxi) or too complex (e.g. Montezuma's Revenge from the Arcade Learning Environment). In this paper we present the Escape Room Domain (ERD), a new flexible, scalable, and fully implemented testing domain for HRL that bridges the "moderate complexity" gap left behind by existing alternatives. ERD is open-source and freely available through GitHub, and conforms to widely-used public testing interfaces for simple integration and testing with a variety of public RL agent implementations. We show that the ERD presents a suite of challenges with scalable difficulty to provide a smooth learning gradient from Taxi to the Arcade Learning Environment.