One year ago, we wrote about the world's first robot lawyer. It is a website with a chatbot that started off with a single and free legal service: helping to appeal unfair parking tickets. When the article was published, the services was available in the UK, and in New York and Seattle. At the time, it had helped overturn traffic tickets to the value of 4 million dollars. Apart from appealing parking tickets, the website could already assist you, too, in claiming compensation if your flight was delayed.

Give us your feedback Thank you for your feedback. Germany's Max Planck Society creates Nobel Prize winners. Most recently, in 2014, physicist Stefan Hell, one of its scholars, was recognised for a breakthrough in microscope technology, allowing much smaller structures -- less than 200 nanometres -- to be seen. Commercialising this kind of highbrow abstract research, however, has been a different matter. While the alumni of California's Stanford University have filled Silicon Valley with start-ups, Germany's research institutes have not created clusters on the same scale.

As climate change continues to cause a reduction in Arctic sea ice and overall ice cover in the polar region, the already threatened polar bears are beginning to display highly unusual behavior. Largely solitary animals in their adult life, dozens of them were seen together recently on an island in northeast Russia. A tourist boat passing by Wrangel Island, off the coast of Chukotka in Russia's Far East, saw over 200 polar bears on a mountain slope on the island. Dozens of the animals were seen at the bottom of the slope, eating the carcass of a bowhead whale that had washed ashore. The incident took place in September, but wasn't widely reported at the time.

The government's latest attempt to tackle Britain's poor productivity record focused on extra funding for artificial intelligence, skills and technology, as the chancellor introduced measures to boost economic growth. Philip Hammond said there would be an expansion in the national productivity investment fund which he launched last year. The fund would rise from £23bn to £31bn to help kickstart improvements in efficiency levels across the UK. Productivity is an economic measure of the efficiency of a workforce. It typically measures the level of output per hour of work, or per worker.

Shortly after SoftBank acquired his company last October, Marc Raibert of Boston Dynamics confessed, "I happen to believe that robotics will be bigger than the Internet." Many sociologists regard the Internet as the single biggest societal invention since the dawn of the printing press in 1440. To fully understand Raibert's point of view, one needs to analyze his zoo of robots which are best know for their awe-striking gait, balance and agility. The newest creation to walk out of Boston Dynamic's lab is SpotMini, the latest evolution of mechanical canines. Big Dog, Spot's unnerving ancestor, first came to public view in 2009 and has racked up quite a YouTube following with more than six and one half million views.

This paper presents a self-supervised method for detecting the active speaker in a multi-person spoken interaction scenario. We argue that this capability is a fundamental prerequisite for any artificial cognitive system attempting to acquire language in social settings. Our methods are able to detect an arbitrary number of possibly overlapping active speakers based exclusively on visual information about their face. Our methods do not rely on external annotations, thus complying with cognitive development. Instead, they use information from the auditory modality to support learning in the visual domain. The methods have been extensively evaluated on a large multi-person face-to-face interaction dataset. The results reach an accuracy of 80% on a multi-speaker setting. We believe this system represents an essential component of any artificial cognitive system or robotic platform engaging in social interaction.

LTHOUGH the Algorithm Selection Problem (ASP, [1]) has been introduced more than four decades ago, there only exist few works (e.g., [2], [3]), which perform algorithm selection in the field of continuous optimization. Independent of the underlying domain, the goal of the ASP can be described as follows: given a set of optimization algorithms A, often denoted algorithm portfolio, and a set of problem instances I, one wants to find a model m: I A that selects the best algorithm A A from the portfolio for an unseen problem instance I I. Albeit there already exists a plethora of optimization algorithms - even when only considering singleobjective, continuous optimization problems - none of them can be considered to be superior to all the other ones across all optimization problems. Hence, it is very desirable to find a sophisticated selection mechanism, which automatically picks the portfolio's best solver for a given problem. Within other optimization domains, such as the well-known Travelling Salesperson Problem, feature-based algorithm selectors have already shown their capability of outperforming the respective state-of-the-art optimization algorithm(s) by combining machine learning techniques and problem dependent features [4], [5].

We study platforms in the sharing economy and discuss the need for incentivizing users to explore options that otherwise would not be chosen. For instance, rental platforms such as Airbnb typically rely on customer reviews to provide users with relevant information about different options. Yet, often a large fraction of options does not have any reviews available. Such options are frequently neglected as viable choices, and in turn are unlikely to be evaluated, creating a vicious cycle. Platforms can engage users to deviate from their preferred choice by offering monetary incentives for choosing a different option instead. To efficiently learn the optimal incentives to offer, we consider structural information in user preferences and introduce a novel algorithm - Coordinated Online Learning (CoOL) - for learning with structural information modeled as convex constraints. We provide formal guarantees on the performance of our algorithm and test the viability of our approach in a user study with data of apartments on Airbnb. Our findings suggest that our approach is well-suited to learn appropriate incentives and increase exploration on the investigated platform.

In recent years, significant progress has been made in solving challenging problems across various domains using deep reinforcement learning (RL). Reproducing existing work and accurately judging the improvements offered by novel methods is vital to sustaining this progress. Unfortunately, reproducing results for state-of-the-art deep RL methods is seldom straightforward. In particular, non-determinism in standard benchmark environments, combined with variance intrinsic to the methods, can make reported results tough to interpret. Without significance metrics and tighter standardization of experimental reporting, it is difficult to determine whether improvements over the prior state-of-the-art are meaningful. In this paper, we investigate challenges posed by reproducibility, proper experimental techniques, and reporting procedures. We illustrate the variability in reported metrics and results when comparing against common baselines and suggest guidelines to make future results in deep RL more reproducible. We aim to spur discussion about how to ensure continued progress in the field by minimizing wasted effort stemming from results that are non-reproducible and easily misinterpreted.

In his 1990 book The Age of Intelligent Machines, the American computer scientist and futurist Ray Kurzweil made an astonishing prediction. Working at the Massachusetts Institute of Technology (MIT) throughout the 1970s and 1980s and having seen firsthand the remarkable advances in artificial intelligence pioneered there by Marvin Minsky and others, he forecast that a computer would pass the Turing test – the test of a machine's ability to match or be indistinguishable from human intelligence – between 2020 and 2050. Kurzweil, now Google's head of artificial intelligence, or AI (an acronym with which we've all now become familiar), has subsequently refined his claim. He now says this event will happen by 2029. What's more, in 2045 we will witness what he calls "the singularity" – the point at which human and artificial intelligences merge, leading to exponential advances in technology and human capabilities.