Billionaire and Virgin Group founder Richard Branson is the latest businessman to say that universal basic income (UBI) can help people as jobs are lost to automation. In a post, Branson detailed his experience is Finland, where the country is already experimenting with UBI. Since January, Finland has been paying those who are unemployed €560, part of its two-year experiment on 2,000 Finns aged 25-58. The money, which replaces previous benefits, is paid even if the individual finds a job, in an effort to reduce unemployment and loss of income from taking low-paid jobs to get by. "The hope is that policies like these can help people struggling just to survive and allow them to get on their feet, be entrepreneurial and be more creative," said Branson is the post.

Lab work is extending machine learning to serve new applications and define new hardware architectures, said a Qualcomm researcher. He spoke on the occasion of the company acquiring Scyfer B.V., a small AI research team affiliated with University of Amsterdam that it had been working with previously. Scyfer acted as a consulting firm, applying machine learning to industrial, IoT, banking, and mobile sectors. The group is now part of Qualcomm Research, seeking to expand machine learning in areas such as computer vision and natural language processing and exploring how emerging algorithms will impact the design of hardware accelerators. "As the algorithms change, we think there is a space here for co-designing the neural networks and the hardware," said Jeff Gehlhaar, a vice president of technology for corporate R&D who is responsible for AI at Qualcomm.

At the moment, only about 12–15% of the engineers who are building the internet and its software are women. We don't want a repeat of these kinds of situations. And we've been working to address this at Women 2.0 for over a decade. We think a lot about how diversity -- or lack thereof. We think about it has affected -- and is going to affect -- the technology outputs that enter our lives.

"You are worse than a fool; you have no care for your species. For thousands of years men dreamed of pacts with demons. Only now are such things possible." When William Gibson wrote those words in his groundbreaking 1984, novel Neuromancer, artificial intelligence remained almost entirely within the realm of science fiction. Today, however, the convergence of complex algorithms, big data, and exponential increases in computational power has resulted in a world where AI raises significant ethical and human rights dilemmas, involving rights ranging from the right to privacy to due process.

Natural language processing (NLP) has recently gained much attention for representing and analysing human language computationally. It has spread its applications in various fields such as machine translation, email spam detection, information extraction, summarization, medical, and question answering etc. The paper distinguishes four phases by discussing different levels of NLP and components of Natural Language Generation (NLG) followed by presenting the history and evolution of NLP, state of the art presenting the various applications of NLP and current trends and challenges.

We investigate statistical properties of a clustering algorithm that receives level set estimates from a kernel density estimator and then estimates the first split in the density level cluster tree if such a split is present or detects the absence of such a split. Key aspects of our analysis include finite sample guarantees, consistency, rates of convergence, and an adaptive data-driven strategy for chosing the kernel bandwidth. For the rates and the adaptivity we do not need continuity assumptions on the density such as H\"older continuity, but only require intuitive geometric assumptions of non-parametric nature.

A Dirichlet $k$-partition of a domain $U \subseteq \mathbb{R}^d$ is a collection of $k$ pairwise disjoint open subsets such that the sum of their first Laplace-Dirichlet eigenvalues is minimal. A discrete version of Dirichlet partitions has been posed on graphs with applications in data analysis. Both versions admit variational formulations: solutions are characterized by minimizers of the Dirichlet energy of mappings from $U$ into a singular space $\Sigma_k \subseteq \mathbb{R}^k$. In this paper, we extend results of N.\ Garc\'ia Trillos and D.\ Slep\v{c}ev to show that there exist solutions of the continuum problem arising as limits to solutions of a sequence of discrete problems. Specifically, a sequence of points $\{x_i\}_{i \in \mathbb{N}}$ from $U$ is sampled i.i.d.\ with respect to a given probability measure $\nu$ on $U$ and for all $n \in \mathbb{N}$, a geometric graph $G_n$ is constructed from the first $n$ points $x_1, x_2, \ldots, x_n$ and the pairwise distances between the points. With probability one with respect to the choice of points $\{x_i\}_{i \in \mathbb{N}}$, we show that as $n \to \infty$ the discrete Dirichlet energies for functions $G_n \to \Sigma_k$ $\Gamma$-converge to (a scalar multiple of) the continuum Dirichlet energy for functions $U \to \Sigma_k$ with respect to a metric coming from the theory of optimal transport. This, along with a compactness property for the aforementioned energies that we prove, implies the convergence of minimizers. When $\nu$ is the uniform distribution, our results also imply the statistical consistency statement that Dirichlet partitions of geometric graphs converge to partitions of the sampled space in the Hausdorff sense.

Choosing the best-performing optimizer(s) out of a portfolio of optimization algorithms is usually a difficult and complex task. It gets even worse, if the underlying functions are unknown, i.e., so-called Black-Box problems, and function evaluations are considered to be expensive. In the case of continuous single-objective optimization problems, Exploratory Landscape Analysis (ELA) - a sophisticated and effective approach for characterizing the landscapes of such problems by means of numerical values before actually performing the optimization task itself - is advantageous. Unfortunately, until now it has been quite complicated to compute multiple ELA features simultaneously, as the corresponding code has been - if at all - spread across multiple platforms or at least across several packages within these platforms. This article presents a broad summary of existing ELA approaches and introduces flacco, an R-package for feature-based landscape analysis of continuous and constrained optimization problems. Although its functions neither solve the optimization problem itself nor the related "Algorithm Selection Problem (ASP)", it offers easy access to an essential ingredient of the ASP by providing a wide collection of ELA features on a single platform - even within a single package. In addition, flacco provides multiple visualization techniques, which enhance the understanding of some of these numerical features, and thereby make certain landscape properties more comprehensible. On top of that, we will introduce the package's build-in, as well as web-hosted and hence platform-independent, graphical user interface (GUI), which facilitates the usage of the package - especially for people who are not familiar with R - making it a very convenient toolbox when working towards algorithm selection of continuous single-objective optimization problems.

Sampling from the posterior distribution using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations to fully explore the correct posterior. This is often the case when the posterior of interest is multi-modal, as the MCMC sampler can become trapped in a local mode for a large number of iterations. In this paper, we introduce the pseudo-extended MCMC method as an approach for improving the mixing of the MCMC sampler in complex posterior distributions. The pseudo-extended method augments the state-space of the posterior using pseudo-samples as auxiliary variables, where on the extended space, the MCMC sampler is able to easily move between the well-separated modes of the posterior. We apply the pseudo-extended method within an Hamiltonian Monte Carlo sampler and show that by using the No U-turn algorithm (Hoffman and Gelman, 2014), our proposed sampler is completely tuning free. We compare the pseudo-extended method against well-known tempered MCMC algorithms and show the advantages of the new sampler on a number of challenging examples from the statistics literature.

Malaria is a serious infectious disease that is responsible for over half million deaths yearly worldwide. The major cause of these mortalities is late or inaccurate diagnosis. Manual microscopy is currently considered as the dominant diagnostic method for malaria. However, it is time consuming and prone to human errors. The aim of this paper is to automate the diagnosis process and minimize the human intervention. We have developed the hardware and software for a cost-efficient malaria diagnostic system. This paper describes the manufactured hardware and also proposes novel software to handle parasite detection and life-stage identification. A motorized microscope is developed to take images from Giemsa-stained blood smears. A patch-based unsupervised statistical clustering algorithm is proposed which offers a novel method for classification of different regions within blood images. The proposed method provides better robustness against different imaging settings. The core of the proposed algorithm is a model called Mixture of Independent Component Analysis. A manifold based optimization method is proposed that facilitates the application of the model for high dimensional data usually acquired in medical microscopy. The method was tested on 600 blood slides with various imaging conditions. The speed of the method is higher than current supervised systems while its accuracy is comparable to or better than them.