Implanting a microchip inside the brain to augment its mental powers has long been a science fiction trope. Now, brain computer interface is suddenly the hot new thing in tech. This spring, Elon Musk started a new company, Neuralink, to do it. Facebook, at its F8 developer's conference, showed a video of an ALS patient typing with her brain. But earlier to the game was Bryan Johnson, an entrepreneur who in 2013 made a bundle by selling his company, Braintree, to Paypal for $800 million.

Artificial intelligence is already being put to use in the NHS, with Google's AI firm DeepMind providing technology to help monitor patients. And a new study suggests that Google could soon be meeting with Genomic England - a company set up by the Department of Health to sequence 100,000 genomes – to discuss whether DeepMind could get involved. In an article for The Conversation, Edward Hockings a researcher at the University of the West of Scotland, explains the risks of letting a private company gain access to sensitive genetic data. In Google's case, he says, it could allow them to target users with personalised advertising based on their preferences and health risks. It could also create profiles of people based on their DNA data, which may provide details such as their risk of becoming a criminal.

Real world Machine Learning is not just about the application of an algorithm to a dataset but a workflow, which involves a sequence of steps such as adding new features, sampling, removing anomalies, applying a few algorithms in cascade, and stacking a few others. The exact steps are often arrived at during iterative experiments performed by the practitioner. In other words, when it comes to the real life Machine Learning process, not everything is as automatic as various business media may make you believe. Usually, one starts by playing around a bit with the data to assess its quality and to get more familiar with it. Then, a significant amount of time is spent in feature engineering datasets, configuring models, evaluating them, and iterating or combining resources to improve results.

Self-driving cars will soon be able to make snap life or death judgements in the event of deciding who to save in a collision, according to new research. The increasing drive towards automated vehicles has raised questions over whether they will be capable of making ethical decisions, like motorists. Now, a study has shown for the first time that human morality can be modelled on a computer. The findings have significant implications for managing the dilemmas that driverless cars may face on the road. Self driving cars will soon be able to make snap life or death judgements.

Realistic sex robots will bring about a social and technological revolution in Britain, experts predict. While it might seem like science fiction, the machines are becoming increasingly sophisticated – and over the next ten years realistic sex androids will become more common, scientists say. But politicians and the public need to understand and deal with the ethical issues that sex robots will pose to society and relationships. This was the warning from Noel Sharkey, emeritus professor of artificial intelligence and robotics at the University of Sheffield, and Dr Aimee van Wynsberghe, assistant professor in ethics and technology at the Technical University of Delft in the Netherlands, as they launched a report on the issue yesterday. Supporters of the use of sex robots say they could be useful for lonely people or those unable to form relationships. But scientists yesterday sounded grim predictions about the'dark side' of the advancing technology, that could mean grappling with issues such as rape and paedophilia.

LONDON – Move over blow-up dolls, the sex robots are here. Artificial intelligence is making its way into the global sex market, bringing with it a revolution in robotic "sex tech" designed to offer sexual gratification with a near-human touch. In a report on the growing market in sex robots, the Foundation for Responsible Robotics said rapidly advancing technologies have already led to the creation of "android love dolls" capable of performing 50 automated sexual positions. They can be customized down to the nipple shape and pubic hair color, and can cost between $5,000 and $15,000. The increasingly life-like robots raise complex issues that should be considered by policymakers and the public, the report said -- including whether use of such devices should be encouraged in sexual therapy clinics, for sex offenders or for people with disabilities.

Can a self-driving vehicle be moral, act like humans do, or act like humans expect humans to? Contrary to previous thinking, a ground-breaking new study has found for the first time that human morality can be modelled meaning that machine based moral decisions are, in principle, possible. The research, Virtual Reality experiments investigating human behavior and moral assessments, from The Institute of Cognitive Science at the University of Osnabrück, and published in Frontiers in Behavioral Neuroscience, used immersive virtual reality to allow the authors to study human behavior in simulated road traffic scenarios. The participants were asked to drive a car in a typical suburban neighborhood on a foggy day when they experienced unexpected unavoidable dilemma situations with inanimate objects, animals, and humans and had to decide which was to be spared. The results were conceptualized by statistical models leading to rules, with an associated degree of explanatory power to explain the observed behavior. The research showed that moral decisions in the con?ned scope of unavoidable traffic collisions can be explained well, and modeled, by a single value-of-life for every human, animal, or inanimate object.

In Constraint Programming (CP) a portfolio solver combines a variety of different constraint solvers for solving a given problem. This fairly recent approach enables to significantly boost the performance of single solvers, especially when multicore architectures are exploited. In this work we give a brief overview of the portfolio solver sunny-cp, and we discuss its performance in the MiniZinc Challenge---the annual international competition for CP solvers---where it won two gold medals in 2015 and 2016. Under consideration in Theory and Practice of Logic Programming (TPLP)

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by \texttt{Google.org}. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.

Understanding the evolution of human society, as a complex adaptive system, is a task that has been looked upon from various angles. In this paper, we simulate an agent-based model with a high enough population tractably. To do this, we characterize an entity called \textit{society}, which helps us reduce the complexity of each step from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$. We propose a very realistic setting, where we design a joint alternate maximization step algorithm to maximize a certain \textit{fitness} function, which we believe simulates the way societies develop. Our key contributions include (i) proposing a novel protocol for simulating the evolution of a society with cheap, non-optimal joint alternate maximization steps (ii) providing a framework for carrying out experiments that adhere to this joint-optimization simulation framework (iii) carrying out experiments to show that it makes sense empirically (iv) providing an alternate justification for the use of \textit{society} in the simulations.