He will be discussing the topic at this year's edition of South by South West on March 14th in Austin, Texas. For EPFL theoretical biologist Marcel Salathé, the answer is invariably yes. To him, a more fundamental question that needs to be addressed is who owns that artificial intelligence? "We have to hold AI accountable, and the only way to do this is to verify it for biases and make sure there is no deliberate misinformation," says Salathé. "This is not possible if the AI is privatized." So what exactly is AI? It is generally regarded as "intelligence exhibited by machines".

What kind of existential problems does AI bring about? The medium-term challenge of AI is not killer robots, it's job replacement. This dynamic is already underway and the literature suggests it's a more powerful driver of job loss than trade, though trade receives much more attention. True AI has not arrived, and automation is not AI, but robots and human-written code are a reasonable preview of what employment challenges genuine AI will bring. Computers already manage warehouses, can drive reasonably well, and are making meaningful progress into areas like basic lawyering and radiology that we long considered to be immune to change.

This paper deals with sparse feature selection and grouping for classification and regression. The classification or regression problems under consideration consists in minimizing a convex empirical risk function subject to an $\ell^1$ constraint, a pairwise $\ell^\infty$ constraint, or a pairwise $\ell^1$ constraint. Existing work, such as the Lasso formulation, has focused mainly on Lagrangian penalty approximations, which often require ad hoc or computationally expensive procedures to determine the penalization parameter. We depart from this approach and address the constrained problem directly via a splitting method. The structure of the method is that of the classical gradient-projection algorithm, which alternates a gradient step on the objective and a projection step onto the lower level set modeling the constraint. The novelty of our approach is that the projection step is implemented via an outer approximation scheme in which the constraint set is approximated by a sequence of simple convex sets consisting of the intersection of two half-spaces. Convergence of the iterates generated by the algorithm is established for a general smooth convex minimization problem with inequality constraints. Experiments on both synthetic and biological data show that our method outperforms penalty methods.

We present the first treatment of the arc length of the Gaussian Process (GP) with more than a single output dimension. GPs are commonly used for tasks such as trajectory modelling, where path length is a crucial quantity of interest. Previously, only paths in one dimension have been considered, with no theoretical consideration of higher dimensional problems. We fill the gap in the existing literature by deriving the moments of the arc length for a stationary GP with multiple output dimensions. A new method is used to derive the mean of a one-dimensional GP over a finite interval, by considering the distribution of the arc length integrand. This technique is used to derive an approximate distribution over the arc length of a vector valued GP in $\mathbb{R}^n$ by moment matching the distribution. Numerical simulations confirm our theoretical derivations.

Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of words change over time. We use dynamic embeddings to analyze three large collections of historical texts: the U.S. Senate speeches from 1858 to 2009, the history of computer science ACM abstracts from 1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We find dynamic embeddings provide better fits than classical embeddings and capture interesting patterns about how language changes.

This paper corrects the proof of the Theorem 2 from the Gower's paper \cite[page 5]{Gower:1982}. The correction is needed in order to establish the existence of the kernel function used commonly in the kernel trick e.g. for $k$-means clustering algorithm, on the grounds of distance matrix. The scope of correction is explained in section 2.

A team of researchers from the University of Bristol in the UK is working on the development of a biomimetic forebrain that can be installed in 3D printed robot hands to increase their manipulation skills and applications. The project has received £1 million in funding from the Leverhulme Trust under the Research Leadership Award scheme. Led by Dr. Nathan Lepora from the Bristol Robotics Laboratory (BRL), the research project is seeking to develop a biomimetic forebrain (the anterior part of the brain) that could help 3D printed tactile robots perform a wider range of manipulation tasks at a higher level of quality. The robotic brain will be based on computer models of a mammal neural system, capable of communicating touch in both humans and animals. The ultimate goal of the five-year research program will be to provide a 3D printed robot hand with humanistic tactile dexterity through the implementation of the complex biomimetic forebrain.

Robots have helped increase revenue for numerous firms and a new survey has revealed that people feel the machines could do the same for the wealth of a nation. Approximately one in four UK citizens believe robots would make better decisions than elected human officials when it comes to boosting the economy. The research also uncovered that 66 percent of people foresee AI powered machines working in government positions by 2037. Approximately one in four UK citizens believe robots would make better decisions than human officials when it comes to boosting the economy. A robot run world is a fear among many people across the globe, as some worry about the future of their jobs. But according to a survey conducted by OpenText, a firm that provides management solutions, people living in the UK believe it could revolutionize their country.

United States Representative Jason Chaffetz (R-UT) suggested in a Congressional hearing on the use of facial recognition technology by law enforcement officials Wednesday that facial recognition software should be used to identify "people that are here illegally," according to Gizmodo. The Representative who previously drew criticism for saying that Americans who can't afford healthcare should just give up their iPhones, is the chairman of the House Committee on Oversight and Government Reform. Chaffetz's comment on the facial recognition software was in reference to the FBI's use of databases that have more than 400 million American's faces. Representative Chaffetz argued Wednesday that the databases are a privacy concern that could spread to the limiting of certain citizen's rights. He also suggested that if the databases were smaller, or limited "to known criminals, wanted criminals, people that are here illegally, maybe those are the types of things that we should be focused on, as opposed to everybody," they would be more useful because it would be easier for the software to accurately identify people.

It's March of the Machines this week, so we had a look at some surprising companies hiring in emerging technologies such as IoT, AI and machine learning. It has never been a more exciting time to work in artificial intelligence (AI) or the internet of things (IoT). There are so many career options available to those who want to pursue these emerging technologies, and successful candidates can make a real difference in how society uses technology. But, while you might know that you want to work in AI or machine learning, you might be left wondering where exactly to start your job search. What companies are hiring in that sector?