In June, Professor Stephen Hawking warned that AI, disguised as helpful digital assistants, could one day spell the end for mankind. But not everyone agrees – including Facebook founder, Mark Zuckerberg. In a recent interview, Zuckerberg made a bid to reassure the world that AI is a force for good, and will destroy humanity. Mark Zuckerberg recently spoke to The Macro, saying that we should not see AI as a technological development which will bring about out species' demise A team from the University of Texas have created a new software that integrates humanoid robots and web-based tele-operation to provide humanoid robotic learners more access to the hardware. The software, named'Cloud-based Advanced Robotics Laboratory' (CARL), detects the movements of a human controller's body, mimicking the actions in the robot. The system runs through the internet, so could even allow users to control their robots through their mobile phone.

Artificial Lawyer caught up with Canadian legal consultant and futurist Jordan Furlong of Law Twenty One and asked him about his perspective on what opportunities and challenges AI faced in the legal sector. Do you see a strategic advantage for the law firms that embrace AI? If yes, how would that advantage manifest itself? We should probably begin by creating a working definition of'AI', which is a term applied so broadly in the legal sphere that, as Ryan McClead has pointed out, it might as well just be written as'magic'. Michael Mills of Neota Logic has suggested instead the term'cognitive technologies', which encompasses a wide range of tech applications including machine learning, natural language processing, and expert systems.

SINGAPORE – Sherine Toh says her best days at work are when none of the 600 or so staff members at Singapore's Tung Lok Restaurants quits, though such days are rare. The Chinese restaurant group is one of the thousands of businesses struggling with a labor crunch caused by curbs on foreign workers that threaten the city-state's already feeble growth rates. "It has gotten much tougher compared to the old days, five years back," said Toh, who has at least 20 vacancies to fill at any one time as head of human resources. The group closed some outlets because of the shortage. The city's restaurants, hotels and retailers have become the biggest casualties of the labor crunch since Singapore accelerated restrictions on foreign workers in 2011 as political disquiet about immigration grew.

Although Chatbot UI (CUI) may seem like a new phenomenon, the technology has long been popular in countries like Japan and China. When Didi Dache integrated with WeChat's CUI for taxi service in 2014, the company quickly got over 700,000 bookings a day through WeChat alone. Fast forward to 2016: Silicon Valley has finally discovered CUI and has invested heavily in the sector, with hundreds of chatbots soon to be released into the market. One of the earliest steps in developing a chatbot is deciding how to give it a personality. Some companies take the Siri approach and choose not to give their chatbot a visual avatar, while Slack, for example, created the slackbot to make things a little more fun.

Detroit got its first toehold in Startup Nation on Tuesday, after Ford Motor Co. said it was buying SAIPS, an Israeli company that develops technologies that are key to self-driving vehicles. The acquisition was one of four deals the U.S. carmaker announced with the aim of having a high-volume, fully autonomous vehicle in commercial operation in 2021 in a ride-hailing or ride-sharing service. Ford said the investment/collaboration with the four startups is part of its strategy of enhancing its autonomous vehicle development, which includes more than doubling its staff at its Palo Alto, California, research and development center. Ford said SAIPS, a computer vision and machine-learning startup, would strengthen its expertise in artificial intelligence and enhanced computer vision. The company provided no financial details about its acquisition, but industry sources estimated it at tens of millions of dollars.

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.

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.