JAKARTA – The waste from discarded electronic gadgets and electrical appliances has reached severe levels in East Asia, posing a growing threat to health and the environment unless safe disposal becomes the norm. China was the biggest culprit with its electronic waste more than doubling, according to a new study by the United Nations University. But nearly every country in the region had massive increases between 2010 and 2015, including those least equipped to deal with the growing mountain of discarded smartphones, computers, TVs, air conditioners and other goods. On average, electronic waste in the 12 countries in the study had increased by nearly two thirds in the five years, totaling 12.3 million tons in 2015 alone. Rising incomes in Asia, burgeoning populations of young adults, rapid obsolescence of products due to technological innovation and changes in fashion, on top of illegal global trade in waste, are among factors driving the increases.

He is a German computer scientist and artist known for his work on machine learning, Artificial Intelligence (AI), artificial neural networks, digital physics, and low-complexity art. "We need to be super careful with artificial intelligence. It is potentially more dangerous than nukes." That was Elon Musk two years ago, on Twitter. What does it mean for a technology, when it faces serious doubts from a man who is passionate about creating a better world through innovation? Since its beginnings in the 1950s, artificial intelligence has been a favourite subject of science fiction. But now AI has entered the realm of fact: several studies predict that intelligent machines will have a big impact on how we work, how we move and even how wars are fought. Innovators and scientists around the world believe that now is the time to ensure that AI is beneficial above all for humans. And even if there are plausible reasons to be anxious about machines that could one day be more intelligent than we are, many scientists are ready to take up the challenge. Some people fret that artificial intelligence will end civilization as we know it. Others believe it can solve every problem.

This performance by Trevor Paglen explores the way machines see and interpret the experience of watching a musical performance. Kronos Quartet will perform works ranging from Baroque composer J. S. Bach to contemporary minimalist composers Terry Riley and Steve Reich, and American blues to African folk music. A live video feed of the performance will be processed in real time through surveillance AI algorithms, and the resultant machine vision data images will be projected on a screen above the performers. Sight Machine is organized by the Cantor Arts Center. We gratefully acknowledge support from the Palmer Gross Ducommon Fund and from our partners: Meyer Sound, Obscura Digital, Orton Development, and Plant Construction.

The GTCI measures how countries grow, attract and retain talent, providing a resource for decision makers to develop strategies for boosting their talent competitiveness. The theme of this fourth edition of the GTCI is Talent and Technology: Shaping the Future of Work. The 2017 report explores the effects of technological change on talent competitiveness, arguing that while jobs at all levels continue to be replaced by machines, technology is also creating new opportunities. However, people and organisations will need to adapt to a working environment in which technology know-how, people skills, flexibility and collaboration are key to success, and in which horizontal networks are replacing hierarchies as the new leadership norm. Governments and business players need to work together to build educational systems and labour market policies that are fit for purpose.

How should one aggregate ordinal preferences expressed by voters into a measurably superior social choice? A well-established approach -- which we refer to as implicit utilitarian voting -- assumes that voters have latent utility functions that induce the reported rankings, and seeks voting rules that approximately maximize utilitarian social welfare. We extend this approach to the design of rules that select a subset of alternatives. We derive analytical bounds on the performance of optimal (deterministic as well as randomized) rules in terms of two measures, distortion and regret. Empirical results show that regret-based rules are more compelling than distortion-based rules, leading us to focus on developing a scalable implementation for the optimal (deterministic) regret-based rule. Our methods underlie the design and implementation of RoboVote.org,

The classification of MRI images according to the anatomical field of view is a necessary task to solve when faced with the increasing quantity of medical images. In parallel, advances in deep learning makes it a suitable tool for computer vision problems. Using a common architecture (such as AlexNet) provides quite good results, but not sufficient for clinical use. Improving the model is not an easy task, due to the large number of hyper-parameters governing both the architecture and the training of the network, and to the limited understanding of their relevance. Since an exhaustive search is not tractable, we propose to optimize the network first by random search, and then by an adaptive search based on Gaussian Processes and Probability of Improvement. Applying this method on a large and varied MRI dataset, we show a substantial improvement between the baseline network and the final one (up to 20\% for the most difficult classes).

Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicating relevant variables and parameter values. Yet, this prior knowledge is often tacit and only available from domain experts. We present a novel approach that uses interactive visualization to elicit the tacit prior knowledge and uses it to improve the accuracy of prediction models. The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task. In particular, based on the expert's earlier input, the user model guides the selection of the features on which to elicit user's knowledge next. The results of a controlled user study show that the user model significantly improves prior knowledge elicitation and prediction accuracy, when predicting the relative citation counts of scientific documents in a specific domain.

This is the last part on building an end-end search engine. In this part we will take a look at how to go about building the front end. This will be an AngularJS application and will consist of some HTML and Javascript. All codes are readily available on Github along with the data itself. Here we will just do a walkthrough of what we are doing to make it all happen.

It's hard to believe this is my 10th annual predictions piece for VentureBeat. VentureBeat founder Matt Marshall asked me write my first prediction piece in 2007 for the year 2008. The tech landscape has changed a lot since then. In 2008, Microsoft offered $44.6 billion for Yahoo, and now Verizon could acquire Yahoo for $4.8 billlion. Cloud computing made its mark in 2008, back when no one could have imagined Amazon would be leading the space.

Those of you braving the World Economic Forum's annual jamboree in Davos this week, should you find time in between ski sessions and early-hours nightcaps to pop into a session or two, will notice a familiar theme emerging. You won't be able to move in Davos without hearing about AI: the conference's agenda includes sessions on "AI and advanced robotics", "Decision by algorithm", "Intelligent killing machines", not to mention dozens of side events on the matter. Given the subject matter, you might be forgiven for thinking this could be the last time Davos hosts the World Economic Forum before the inevitable robot uprising forces next year's event to be held in an underground cave.