It's almost four years since Edward Snowden leaked U.S. National Security Agency documents revealing the extent of the organization's surveillance of global internet traffic, but he's still making the headlines in Germany. At the Cebit trade show in Hannover, Germany, he'll be looking back at that period in live video interview from Moscow on Tuesday evening. There have been a lot of changes on the internet in those four years, but one of the biggest is the growth in the use of encryption. In 2013, the NSA had free rein and could listen in on almost any communication it wanted. And you don't have to be an enemy of the state to use an end-to-end encrypted messaging system such as WhatsApp simply to chat with friends.

The problem with a lot of touch-sensitive controls is that the communication is one-way: They can feel you, but you can't feel them. With touch-screen displays it's easy enough, as the button does what it says on the screen. Not all buttons are designed to be looked at as they are pushed, though. Take video-game controllers or car entertainment systems, for example, or some industrial controls. The user's attention is typically elsewhere when these are operated.

Declining birth rate, aging population, natural disasters, pollution: Do these sound like issues the IT industry can deal with? Japanese businesses say yes, and a number of them are at the Cebit trade show in Hanover, Germany, to explain why. Industry 4.0 -- the building of "smart factories" in which machines monitor one another and make decentralized decisions about production and maintenance -- has been a theme of recent Cebit shows. Now, under the banner Society 5.0, the show's partner country for 2017, Japan, wants to take the transformation beyond industry, making "smart society" one of the show's talking points. Behind the drive are some very real societal problems.

We will still need programmers no matter what form the discrete to abstract transition may take. That is not the point. It is not to imply that coding is not important or that there are no improvements that better coding can realise. Instead the point is that we need to start thinking about algorithms. They are not just for mathematicians or academics.

We demonstrate that, for a range of state-of-the-art machine learning algorithms, the differences in generalisation performance obtained using default parameter settings and using parameters tuned via cross-validation can be similar in magnitude to the differences in performance observed between state-of-the-art and uncompetitive learning systems. This means that fair and rigorous evaluation of new learning algorithms requires performance comparison against benchmark methods with best-practice model selection procedures, rather than using default parameter settings. We investigate the sensitivity of three key machine learning algorithms (support vector machine, random forest and rotation forest) to their default parameter settings, and provide guidance on determining sensible default parameter values for implementations of these algorithms. We also conduct an experimental comparison of these three algorithms on 121 classification problems and find that, perhaps surprisingly, rotation forest is significantly more accurate on average than both random forest and a support vector machine.

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective.

The launch of the Samsung Galaxy S8 March 29 will represent a comeback for the company's mobile unit, which is recovering by the Galaxy Note 7 disaster. What's unclear is how Samsung Galaxy S8 flagship phone will fare given the multiple unknowns the company is facing. Samsung's Galaxy S8 lands amid a backdrop of the company's January report on what went wrong with the Note 7 and how it is fixing its processes to prevent battery problems in the future. That report went a long way to allaying fears, but brand recoveries take time. The device will have new artificial intelligence tools via a service called Bixby.

This list was KDnuggets most popular tweet recently, but the tweet only had the image and no company names or details. Here we list companies by category and in the order of high to low equity funding in each group. Company overview (e.g., headquarters location and total equity funding raised) is based on information as it have appeared on crunchbase.com The top countries for startups are: US 39, China 3, Israel 3, UK 3, France 1, Taiwan 1. Here is the total funding by areas, which shows that the areas with most funding are AD Sales & CRM, Core AI, and BI & Analytics.

The company, which is on a mission to "solve intelligence," has hired some of the brightest minds in the world, including academics from Oxbridge and research scientists from firms like Facebook and Microsoft. Exactly how DeepMind's researchers work together has been something of a mystery but the FT story sheds new light on the matter. Researchers at DeepMind are divided into four main groups, including a "neuroscience" group and a "frontiers" group, according to the report. The frontiers group is said to be full of physicists and mathematicians who are tasked with testing some of the most futuristic AI theories. "We've hired 250 of the world's best scientists, so obviously they're here to let their creativity run riot, and we try and create an environment that's perfect for that," DeepMind CEO Demis Hassabis told the FT.

A majority (84 per cent) of companies polled for a study see the use of artificial intelligence (AI) as "essential" to competitiveness, with a further 50 per cent seeing the technology as "transformative," according to Tata Consultancy Services' Global Trend Study titled, "Getting Smarter by the Day: How AI is Elevating the Performance of Global Companies." Exploring the views and actions of decision makers from global companies with average revenues of $20 billion, the study revealed AI is spreading across almost all areas of a company. Asia Pacific companies reported an average 19 per cent increase in revenue stemming from AI. AI spend in the region is expected to hit US$57 million this year. The biggest adopters of AI today are, not surprisingly, IT departments, with two-thirds (67 per cent) of survey respondents using AI to detect security intrusions, user issues and deliver automation. However, by 2020, almost a third (32 per cent) of companies believe AI's greatest impact will be in sales, marketing or customer service, while one in five (20 per cent) see AI's impact being largest in non-customer facing corporate functions, including finance, strategic planning, corporate development, and human resources.