Robin heads up commercial activities at Insightive.tv. He previously founded the award-winning Digital agency Red Sky Vision before building Europe's largest technology thought leadership video platform, cloud-channel.tv.

Vehicle automation has received much attention worldwide. But EU policymakers are not giving enough attention to the impact automated vehicles may have on sustainable mobility policy, therefore turning opportunities for automation into threats, writes Karen Vancluysen. Karen Vancluysen is the Secretary-General of POLIS, the European network of cities and regions on innovation in urban mobility. Policy makers at EU and national level are not giving enough attention to the impact automated vehicles may have on sustainable mobility policy. Vehicle automation has become a trending topic worldwide.

Business adoption of artificial intelligence is accelerating, fueled by an explosion of data, the rapid growth in cloud computing and the emergence of advanced algorithms. In a survey of IT decision-makers that my company, CCS Insight, conducted in July 2017, 58 percent of respondents said they are using, testing or researching the use of artificial intelligence (AI) in their organizations. Respondents also estimated that as much as 30 percent of their business applications would be enhanced with machine learning within the next 24 months -- a bullish view, considering the technology's well-documented problems with trust, cost and the lack of skills needed to train machine learning systems. Speech-based and image-based cognitive applications are emerging at an accelerating rate for use in specific markets, such as fraud detection in finance, low-level contract analysis in the legal sector and personalization in retail. AI is also beginning to appear in systems designed for corporate functions such as customer service, HR, sales and IT.

Google Lens is coming to more phones besides the Pixel. When Google unveiled Google Lens last May, it was billed as the future of search. Typing a search was outdated. Now you could just point your camera at, say, a landmark and instantly learn about it. Take a picture of a book, and there's all the info you need about where to buy it, who published it and what reviewers say about it.

It could be here in a few years' time, another sign of the ever-faster technological changes that are reshaping our world. But will the march of technology and AI come at a price? Will it cost you your job? Let us start the week in outer space, where – to misquote Ming the Merciless – the puny earthlings have hurled a car into the void. At the beginning of this month, the Falcon Heavy was launched.

Joy Buolamwini was conducting research at MIT on how computers recognized people's faces, when she started experiencing something weird. Whenever she sat before a system's front-facing camera, it wouldn't recognize her face, even after working for her lighter-skinned friends. But when she put on a simple white mask, the face-tracking animation suddenly lit up the screen. Suspecting a more widespread problem, she carried out a study on the AI-powered facial recognition systems of Microsoft, IBM and Face, a Chinese startup that has raised more than $500 million from investors. Buolamwini showed the systems 1,000 faces, and told them to identify each as male or female.

This will be followed by an additional twenty minutes where both take part in a "Fireside Chat" in which they discuss the challenges of leveraging these platforms as tools for engaging customers in healthcare, the feedback, benefits and ultimate practical successes.

Artificial intelligence (A.I.) is making the "rise of machines" -- once the stuff of science fiction -- a reality. As 60 Minutes reported on October 9, "It might not be long before machines begin thinking for themselves -- creatively, independently, and sometimes with better judgment than a human." On February 28, 2018, you're invited to "Meet the Future" at DePauw University as the Ubben Lecture Series presents the world's first artificial intelligence-fueled android, Sophia, and her creator, David Hanson. In a 7:30 p.m. program in Kresge Auditorium, Dr. Hanson -- founder, CEO and chief designer of Hong Kong-based Hanson Robotics -- will be joined by his one-of-a-kind robot character. At the free event, which is open to all, the two will deliver a speech, take questions from the audience, and offer insights into the world of tomorrow that we're already entering today.

Flood of information is produced in a daily basis through the global Internet usage arising from the on-line interactive communications among users. While this situation contributes significantly to the quality of human life, unfortunately it involves enormous dangers, since on-line texts with high toxicity can cause personal attacks, on-line harassment and bullying behaviors. This has triggered both industrial and research community in the last few years while there are several tries to identify an efficient model for on-line toxic comment prediction. However, these steps are still in their infancy and new approaches and frameworks are required. On parallel, the data explosion that appears constantly, makes the construction of new machine learning computational tools for managing this information, an imperative need. Thankfully advances in hardware, cloud computing and big data management allow the development of Deep Learning approaches appearing very promising performance so far. For text classification in particular the use of Convolutional Neural Networks (CNN) have recently been proposed approaching text analytics in a modern manner emphasizing in the structure of words in a document. In this work, we employ this approach to discover toxic comments in a large pool of documents provided by a current Kaggle's competition regarding Wikipedia's talk page edits. To justify this decision we choose to compare CNNs against the traditional bag-of-words approach for text analysis combined with a selection of algorithms proven to be very effective in text classification. The reported results provide enough evidence that CNN enhance toxic comment classification reinforcing research interest towards this direction.

The rapid growth of storage capacity and processing power has caused machine learning applications to increasingly rely on using immense amounts of labeled data. It has become more important than ever to have fast and inexpensive ways to annotate vast amounts of data. With the emergence of crowdsourcing services, the research direction has gravitated toward putting the wisdom of crowds to better use. Unfortunately, spammers and inattentive annotators pose a threat to the quality and trustworthiness of the consensus. Thus, high quality consensus estimation from crowd annotated data requires a meticulous choice of the candidate annotator and the sample in need of a new annotation. Due to time and budget limitations, it is of utmost importance that this choice is carried out while the annotation collection is in progress. We call this process active crowd-labeling. To this end, we propose an active crowd-labeling approach for actively estimating consensus from continuous-valued crowd annotations. Our method is based on annotator models with unknown parameters, and Bayesian inference is employed to reach a consensus in the form of ordinal, binary, or continuous values. We introduce ranking functions for choosing the candidate annotator and sample pair for requesting an annotation. In addition, we propose a penalizing method for preventing annotator domination, investigate the explore-exploit trade-off for incorporating new annotators into the system, and study the effects of inducing a stopping criterion based on consensus quality. We also introduce the crowd-labeled Head Pose Annotations datasets. Experimental results on the benchmark datasets used in the literature and the Head Pose Annotations datasets suggest that our method provides high-quality consensus by using as few as one fifth of the annotations (~80% cost reduction), thereby providing a budget and time-sensitive solution to the crowd-labeling problem.