Markov logic networks (MLNs) reconcile two opposing schools in machine learning and artificial intelligence: causal networks, which account for uncertainty extremely well, and first-order logic, which allows for formal deduction. An MLN is essentially a first-order logic template to generate Markov networks. Inference in MLNs is probabilistic and it is often performed by approximate methods such as Markov chain Monte Carlo (MCMC) Gibbs sampling. An MLN has many regular, symmetric structures that can be exploited at both first-order level and in the generated Markov network. We analyze the graph structures that are produced by various lifting methods and investigate the extent to which quantum protocols can be used to speed up Gibbs sampling with state preparation and measurement schemes. We review different such approaches, discuss their advantages, theoretical limitations, and their appeal to implementations. We find that a straightforward application of a recent result yields exponential speedup compared to classical heuristics in approximate probabilistic inference, thereby demonstrating another example where advanced quantum resources can potentially prove useful in machine learning.

Information on these groups can help optimizing the structure and contents of the site. In this paper we use an approach based on formal concepts for constructing taxonomies of user groups. For decreasing the huge amount of concepts that arise in applications, we employ stability index of a concept, which describes how a group given by a concept extent differs from other such groups. We analyze resulting taxonomies of user groups for three target websites.

Can artificial intelligence win a championship or that much coveted Gold medal for an individual athlete? Well, one company has not one but two solutions that incorporates artificial intelligence (AI) technology into sports wearables. PIQ is a start-up based in France that has been working for two years to develop this AI interface. The company has poured more than $18 million into this project and has currently a team of 50 engineers working on AI interface that informs the athlete's own "winning factors", while also pinpointing key strengths and strategy for success. According to PIQ, connected sports are limited as they just capture basic data.

Despite what you might think, evolution rarely happens because something is good for a species. Instead, natural selection favours genetic variants that are good for the individuals that possess them. This leads to a much more complicated and messy world, with different selective forces pushing in many directions, even within a single species. A team of Swedish and Australian researchers led by Séverine Buechel from Stockholm Universitywondered if, like predator-prey conflict, sexual conflict might also affect the evolution of brain size. The team bred mosquitofish in a lab - both male and female.

Europe's Schiaparelli Mars lander crashed last month after a sensor failure caused it to cast away its parachute and turn off braking thrusters more than two miles (3.7 km) above the surface of the planet, as if it had already landed, a new report has revealed. The error stemmed from a momentary glitch in a device that measured how fast the spacecraft was spinning, the report by the European Space Agency said. The spacecraft activated its ground systems, even though it was still about 2.3 miles off the surface, the ESA said. The new image of Schiaparelli and its hardware components was taken by NASA's Mars Reconnaissance Orbiter, or MRO, on 1 November. A number of the bright white spots around the dark region interpreted as the impact site are now confirmed as real objects – they are not likely to be imaging'noise' – and therefore are most likely fragments of Schiaparelli.

He studied English Literature and History at Sussex University before gaining a Masters in Newspaper Journalism from City University. Businesses are often data-rich but information-poor. Machine learning (ML) is changing that. The use of artificial intelligence (AI) to let computers learn independently through algorithms without being explicitly programmed can help companies process vast quantities of complex data to improve analytics, predictive accuracy and decision-making. Machine learning is already being used in everything from fraud detection to self-driving cars, and in sectors from marketing to government.

Robots have become commonplace in many aspects of life including health care, military and security work. Yet until recently little thought has been given outside of academic circles to the ethics of robots. Silicon Valley Robotics recently launched a Good Robot Design Council -- which has launched "5 Laws of Robotics" guidelines for roboticists and academics -- on the ethical creation, marketing and use of robots in everyday life. The laws have been adapted from the EPSRC 2010 "Principles of Robotics". In Britain a few months ago we saw a similar document, "BS8611 Robots and robotic devices" by the British Standards Institute (BSI) presented at the Social Robotics and AI conference in Oxford as an approach to embedding ethical risk assessment in robots.

Dr. Damian Borth is the Director of the Deep Learning Competence Center at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern and founding co director of Sociovestix Labs, a social enterprise in the area of financial data science. Damian's research focuses on large scale multimedia opinion mining applying machine learning and in particular deep learning to mine insights (trends, sentiment) from online media streams. His work has been awarded by the Best Paper Award at ACM ICMR 2012, the McKinsey Business Technology Award 2011, and a Google Research Award in 2010. Damian currently serves as a member of the assessment committee for the Investment Innovation Benchmark (IIB) and several other steering and program committees of international conferences and workshops. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.

The new, hippest technology product comes in innovative packaging, is very expensive and comes with an advertising campaign that boasts of how its obsessive engineering makes it the best in the world. Casper is the company at the forefront of a technology (and marketing) revolution that's seeing perhaps one of the most domestic and boring of products – bedding – become this year's must-have tech product. And that's entirely on purpose: the company is being advertised on tech podcasts, and promotes its mattresses with the kind of fun marketing that would usually be reserved for a phone or a computer. It hasn't come easily, and it hasn't been as much of a trick as it might seem. Instead, the company says that it really is a tech company – and that it has the product to prove it.

Sebastian Raschka is the author of the bestselling book "Python Machine Learning." As a Ph.D. candidate at Michigan State University, he is developing new computational methods in the field of computational biology. Sebastian has many years of experience with coding in Python and has given several seminars on the practical applications of data science and machine learning. Sebastian loves to write and talk about data science, machine learning, and Python, and he is really motivated to help people developing data-driven solutions without necessarily requiring a machine learning background. Why did I bother writing this? Well, here is one of the most trivial yet life-changing insights and worldly wisdoms from my former professor that has become my mantra ever since: "If you have to do this task more than 3 times just write a script and automate it."