Over the years, we have experienced dramatic changes in how we interact with computers. We went from flipping switches, to stacking punch cards, typing away on hard copy and now, manipulating objects on a desktop using touch screens. Each transition has provided us with a greater ease of interaction and a new metaphor (switches, direct commands, desktops) for thinking about that interaction. But, advances in speech recognition are completely changing the game. Siri, Cortana, Alexa and Google Now have given us a taste of what it means to directly communicate our goals and needs to the machine.

Is there a next phase for cloud computing? During the past few years, cloud computing has become a mainstream element of modern software solutions just as common as websites or databases. The cloud computing market is a race vastly dominated by four companies: Amazon, Microsoft, Google and IBM with a few other platforms with traction in specific regional markets such as AliCloud in China. In such a consolidated market, it's hard to imagine a technology being disruptive enough to alter the existing dynamics. Artificial intelligence (AI) is the type of technology with the potential to not only improve the existing cloud platform incumbents but also power a new generation of cloud computing technologies. The thesis of a new generation of cloud computing platforms might seem ludicrous at first but it also presents a very intriguing argument.

This AI Can Tell if You're Depressed by Looking at Your Instagram Photos AI, Bots and Canvases Part III: Gates and Ballmer paved the path for Nadella's AI and bots Can big data and AI fix our criminal-justice crisis?

Intel practically owns the business of selling chips for data center servers. IDC pegs its share of the market at 99%. But Intel doesn't have such a strong grip on the latest, and hottest, slice of the market: artificial intelligence. It faces stiff competition from graphics chip expert Nvidia, whose graphics cards are currently the most popular for powering deep learning neural networks that perform mainstay artificial intelligence tasks like image recognition, voice recognition and natural language processing. Hoping to push back against Nvidia's inroads, Intel announced on Wednesday a new server processor tailored for artificial intelligence, the third-generation Xeon Phi, code-named "Knights Mill."

The second-oldest confirmed shipwreck in the Great Lakes, an American-built, Canadian-owned sloop that sank in Lake Ontario more than 200 years ago, has been found, a team of underwater explorers said Wednesday. The three-member western New York-based team said it discovered the shipwreck this summer in deep water off Oswego, in central New York. Images captured by a remotely operated vehicle confirmed it is the Washington, which sank during a storm in 1803, team member Jim Kennard said. "This one is very special. We don't get too many like this," said Kennard, who along with Roger Pawlowski and Roland "Chip" Stevens has found numerous wrecks in Lake Ontario and other waterways.

Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of CGPMs into BayesDB, a probabilistic programming platform that can express data analysis tasks using a modeling language and a structured query language. The practical value is illustrated in two ways. First, CGPMs are used in an analysis that identifies satellite data records which probably violate Kepler's Third Law, by composing causal probabilistic programs with non-parametric Bayes in under 50 lines of probabilistic code. Second, for several representative data analysis tasks, we report on lines of code and accuracy measurements of various CGPMs, plus comparisons with standard baseline solutions from Python and MATLAB libraries.

The likelihood function of a finite mixture model is a non-convex function with multiple local maxima and commonly used iterative algorithms such as EM will converge to different solutions depending on initial conditions. In this paper we ask: is it possible to assess how far we are from the global maximum of the likelihood? Since the likelihood of a finite mixture model can grow unboundedly by centering a Gaussian on a single datapoint and shrinking the covariance, we constrain the problem by assuming that the parameters of the individual models are members of a large discrete set (e.g. estimating a mixture of two Gaussians where the means and variances of both Gaussians are members of a set of a million possible means and variances). For this setting we show that a simple upper bound on the likelihood can be computed using convex optimization and we analyze conditions under which the bound is guaranteed to be tight. This bound can then be used to assess the quality of solutions found by EM (where the final result is projected on the discrete set) or any other mixture estimation algorithm. For any dataset our method allows us to find a finite mixture model together with a dataset-specific bound on how far the likelihood of this mixture is from the global optimum of the likelihood

Finland is one of the first countries to try out the minibuses on city roads thanks to its laws allowing cars to roam without a driver. Dubai had signed a deal with the company back in April to test the EasyMile vehicles, while a Japanese mall began using them to shuttle shoppers around this month. But neither of those will likely rival the live-traffic demands of the Helsini experiment. The buses won't be doing extensive hauls: the EZ10 model is built for short-range travel, say for ferrying folks between a metro station and bus stop, at a max speed of a little over six miles per hour. If all goes well, the vehicles will supplement but not replace existing mass transit networks.

Apple, Google, Facebook, and Microsoft are worldwide technology powerhouses, but when it comes to the adoption of virtual digital assistants (VDAs) like Siri, Google Assistant, and Cortana, scale only takes you so far. In this particular business, players who successfully cater to the nuances of individual countries will conquer the global VDA market. The same principle will apply to enterprises looking to automate customer interactions like customer service and e-commerce with enterprise VDAs. The challenges facing VDA providers were brought to light recently by the plight of Jibo, the crowdfunded smart home VDA robot that received pre-orders from consumers in 47 countries. On August 9, the company announced that product rollouts would be limited to the United States and Canada only, and that all orders for Jibo outside those markets will be refunded.

The era of big data has witnessed a paradigm shift into analytics. Today, it's no longer sufficient to simply gather data from social media, IoT, and wearable devices, and be unable to manage or filter it. It is more about delivering the right data to the right person, at the right time. This trend is growing crucial as data is multiplying every day and pouring in from various devices and smart machines including wearables, electronic gadgets, and other devices. Such factors call for the treatment of vast pools of structured and unstructured data with care and precision. This is precisely where invisible analytics come in.