So it's a no-brainer for a company like Acuilae to try and find a solution to the dilemma we now face: who can and should bestow ethics on artificial intelligence? It's an important question, considering global finance, health systems, the justice system and much more will soon be managed by artificial intelligence -- if we're going to leave such crucial decisions in the hands of machines, we have to ensure they make the right decisions and, if possible, even fair and good ones. That's where ETHYKA comes into the picture. "Born from the desire to research and learn," according to Acuilae's CEO, Cristina Sánchez, ETHYKA is a project that looks to give AI morals so actions they perform will be based on ethical data. An expert in statistics, computer science and data science, thus far she's had a team of just four to help move the project forward and is now looking to attract investors through a funding round.

An artificial intelligence system can better detect skin cancer than experienced dermatologists, a study has found. Researchers trained a form of artificial intelligence or machine learning known as a deep learning convolutional neural network (CNN) to identify skin cancer by showing it more than 100,000 images of malignant melanomas (the most lethal form of skin cancer), as well as benign moles (or nevi). They compared its performance with that of 58 international dermatologists and found that the CNN missed fewer melanomas and misdiagnosed benign moles less often as malignant than the group of dermatologists. "The CNN works like the brain of a child. To train it, we showed the CNN more than 100,000 images of malignant and benign skin cancers and moles and indicated the diagnosis for each image," said Holger Haenssle, from the University of Heidelberg in Germany.

A team of researchers from the National Research Nuclear University MEPhI, the National Research Center Kurchatov Institute and the Voronezh State University has developed a new learning algorithm that allows a neural network to identify a writer's gender by the written text on a computer with up to 80 percent accuracy. This is a new development in the field of computational linguistics. The research was funded by a Russian Science Foundation grant. The findings were published in the Procedia Computer Science journal. Many scientific studies show that writing style can reflect certain characteristics of a writer – gender, physiological personality traits, and level of education.

A growing number of people are worried that robots -- and other machines with artificial intelligence -- will imminently steal so many jobs that it will lead to a future of pervasive unemployment. But even a cursory reading of history will show that we've been here before. Consider a series of headlines pulled from just one newspaper, the New York Times, as an illustration: In 1928, the Times ran an article titled "March of the Machine Makes Idle Hands." In 1956, it announced "Workers See'Robot Revolution' Depriving Them of Jobs" (for an article about labor unrest in London). In 1980, the newspaper declared "A Robot Is After Your Job."

The construction of a meaningful graph topology plays a crucial role in the effective representation, processing, analysis and visualization of structured data. When a natural choice of the graph is not readily available from the datasets, it is thus desirable to infer or learn a graph topology from the data. In this tutorial overview, we survey solutions to the problem of graph learning, including classical viewpoints from statistics and physics, and more recent approaches that adopt a graph signal processing (GSP) perspective. We further emphasize the conceptual similarities and differences between classical and GSP graph inference methods and highlight the potential advantage of the latter in a number of theoretical and practical scenarios. We conclude with several open issues and challenges that are keys to the design of future signal processing and machine learning algorithms for learning graphs from data.

Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce the bias caused by measurement noise using a large number of noisy measurements of the underlying confounders. We propose the use of matrix factorization to infer the confounders from noisy covariates, a flexible and principled framework that adapts to missing values, accommodates a wide variety of data types, and can augment many causal inference methods. We bound the error for the induced average treatment effect estimator and show it is consistent in a linear regression setting, using Exponential Family Matrix Completion preprocessing. We demonstrate the effectiveness of the proposed procedure in numerical experiments with both synthetic data and real clinical data.

Many recently introduced machine learning techniques in the context of dynamical problems have much in common with system identification procedures developed in the last decades for applications in signal treatment, circuit theory and, in general, systems theory. In these problems, system knowledge is only available in the form of input-output observations and the task consists in finding or learning a model that approximates it for mainly forecasting or classification purposes. An important goal in that context is to find a family of transformations that is both computationally feasible and versatile enough to reproduce a rich number of patterns just by modifying a limited number of procedural parameters. This feature is usually referred to as universality. A first solution to this problem was pioneered in the works of Fréchet [Frec 10] and Volterra [Volt 30] one century ago when they proved that finite Volterra series can be used to uniformly approximate continuous functionals defined on compact sets of continuous functions. These results were further extended in the 1950s by the MIT school lead by N. Wiener [Wien 58, Bril 58, Geor 59] but always under compactness assumptions on the input space and the time interval in which inputs are defined. A major breakthrough was the generalization to infinite time intervals carried out by Boyd and Chua in [Boyd 85] using the so called fading memory property. In this paper we address that problem for transformations or filters of discrete time signals of infinite length that have the fading memory property. The approximating set that we use is generated by nonlinear state-space transformations and that is referred to as reservoir computers (RC) [Jaeg 10, Jaeg 04, Maas 02, Maas 11, Croo 07, Vers 07, Luko 09] or reservoir systems.

This paper studies a Nystr\"om type subsampling approach to large kernel learning methods in the misspecified case, where the target function is not assumed to belong to the reproducing kernel Hilbert space generated by the underlying kernel. This case is less understood, in spite of its practical importance. To model such a case, the smoothness of target functions is described in terms of general source conditions. It is surprising that almost for the whole range of the source conditions, describing the misspecified case, the corresponding learning rate bounds can be achieved with just one value of the regularization parameter. This observation allows a formulation of mild conditions under which the plain Nystr\"om subsampling can be realized with subquadratic cost maintaining the guaranteed learning rates.

In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Graph CNN, a graph convolutional architecture that alternates convolution-like operations on the graph and its dual. Our approach allows to learn both vertex- and edge features and generalizes the previous graph attention (GAT) model. We provide extensive experimental validation showing state-of-the-art results on a variety of tasks tested on established graph benchmarks, including CORA and Citeseer citation networks as well as MovieLens, Flixter, Douban and Yahoo Music graph-guided recommender systems.

A blockchain-driven platform wants to reduce the influence that biased shop assistants and product marketers have on consumers in the skincare industry, and instead create an ecosystem where shoppers can receive expert, impartial advice from qualified dermatologists. Opu Labs says consumers are currently overwhelmed with an avalanche of information online and believes they are not getting the service they deserve. For the millions of people out there with skin conditions, it's often difficult to find the right treatments to cosmetically and medically deal with their complaints. The company believes that artificial intelligence (AI) and big data holds the key to making shoppers better informed – helping them to save money and dramatically reduce the amount of time they spend researching which products to buy. One of the centerpieces of the start-up's offering is Opu AI. Here, users can upload an image of their face which will be analyzed for redness, wrinkles, hyperpigmentation or acne across four specific regions.