The world needs more energy. Governments and companies are investing billions of dollars in technologies to harvest, convert and store power1. And as silicon solar cells approach the limit of their performance, researchers are looking to alternatives based on perovskites and quantum dots2. The batteries that store the energy must get cheaper, more efficient and longer-lasting3. And devices need to be manufactured from safe and abundant materials such as copper, nickel and carbon rather than from lead, platinum or gold. Life-cycle analyses of the materials need to show improved carbon footprints, as well as the ability to match the scale of the global energy challenge.

Artificial Intelligence will disrupt every industry; that seems to be pretty much accepted now. But rather than asking how, many seem to be waiting for some innovative company to invent the AI product that will solve their problem.

For the second consecutive year, Spain's agricultural ministry has launched a pilot experiment using artificial intelligence to predict the evolution of the olive fly.

The idea of man-made artificial intelligence is something that has fascinated and puzzled scientists, engineers and philosophers since the middle of the 20th century. Today, amid news that China and the United States are engaged in a race for AI superiority, and major progress by companies such as Google in making AI a reality, philosophical debates continue. This week, researchers gathered in Long Beach, California for the Neural Information Processing Systems Conference to discuss the possible dangers relating to the growing power of AI, and how to imbue it with an'ethical conscience.' Those subscribing to the first view fear the unknown, and what thoughts may appear in the AI's virtual mind. Scientists, meanwhile, for the most part, are more prone to supporting the latter.

Quick quiz: what do you visualize when you hear the word robot? Or perhaps one of the massive, spiderlike machines that assemble complex products ranging from automobiles to computers?

The multi-armed bandit (MAB) problem is a classic example of the exploration-exploitation dilemma. It is concerned with maximising the total rewards for a gambler by sequentially pulling an arm from a multi-armed slot machine where each arm is associated with a reward distribution. In static MABs, the reward distributions do not change over time, while in dynamic MABs, each arm's reward distribution can change, and the optimal arm can switch over time. Motivated by many real applications where rewards are binary counts, we focus on dynamic Bernoulli bandits. Standard methods like $\epsilon$-Greedy and Upper Confidence Bound (UCB), which rely on the sample mean estimator, often fail to track the changes in underlying reward for dynamic problems. In this paper, we overcome the shortcoming of slow response to change by deploying adaptive estimation in the standard methods and propose a new family of algorithms, which are adaptive versions of $\epsilon$-Greedy, UCB, and Thompson sampling. These new methods are simple and easy to implement. Moreover, they do not require any prior knowledge about the data, which is important for real applications. We examine the new algorithms numerically in different scenarios and find out that the results show solid improvements of our algorithms in dynamic environments.

Dan Burstein, reporter, novelist and successful venture capitalist, declared Wednesday night at RobotLab's winter forum on Autonomous Transportation & SmartCities that within one hundred years the majority of jobs in the USA (and the world) could disappear, transferring the mantle of work from humans to machines. Burstein cautioned the audience that unless governments address the threat of millions of unemployable humans with a wider safety net, democracy could fail. The wisdom of one of the world's most successful venture investors did not fall on deaf ears. In their book, Only Humans Need Apply, Thomas Davenport and Julia Kirby also warn that that humans are too easily ceding their future to machines. "Many knowledge workers are fearful. We should be concerned, given the potential for these unprecedented tools to make us redundant. But we should not feel helpless in the midst of the large-scale change unfolding around us," states Davenport and Kirby.

The nature, promise and risks of new technologies enter into our shared thinking through narrative – explicit or implicit stories about the technologies and their place in our lives. These narratives can determine what is salient about the technologies, influencing how they are represented in media, culture and everyday discussion. The narratives can influence the dynamics of concern and aspiration across society; the ways and the contexts in which different groups and individuals become aware of and respond to mainstream, new and emerging technologies. The narratives available at a particular point in time, and who tells them, can affect the course of technology development and uptake in subtle ways. Whilst stories about artificial intelligence have been around for centuries, the way we think about AI is evolving.

Dolphins communicate through a range of unique clicks and whistles, and now scientists have created an algorithm that can decipher these calls.

Artificial intelligence will dramatically change the accounting profession and recast the skillset required to succeed. We will see the rise of accountants as strategic advisors to small businesses as AI increasingly powers prescriptive financial decision making by sifting through vast amounts of financial data and empowering individuals to make recommendations about the best course of action. This will fundamentally alter the role of today's accountant as focus shifts from tedious data entry, to using data-driven insights uncovered by machine learning to help small business clients make better business decisions--Herman Man, VP of product and partnerships, Xero Alexa will start to use her eyes. With the release of the Echo Show and other forthcoming devices, Amazon's extensive investment in vision-based product recognition efforts through its Lab 126 is coming to fruition. It follows that Alexa will start to use the camera to disambiguate between possible purchases, particularly for grocery.