The dispute highlights growing concern among governments about potential risks associated with the flood of data generated by smartphones, social media and other technology. China has ordered companies to store data about its citizens within this country, which prompted Apple Inc. to announce plans in July to set up a data center in southern China.

Last week, Chancellor Philip Hammond announced in the Autumn Budget a £500m package of investment into tech initiatives, including the development of artificial intelligence. Which must have had the Channel 4 executives ordering trebles all round, because with perfect timing they've designated this week the "Rise of the Robots season", with a schedule that includes documentaries on the take-off of artificial intelligences (AIs) as consulting doctors, a David Tennant-narrated piece on the challenge of making robots as human as possible, and the one that's had the tabloids hot under the collar, today'sThe Sex Robots Are Coming – which needs little further explanation. Doctor Who and the Invasion of the Sex-Bots aside, though, is it actually possible that the dream of science fiction writers going back a century or more is on the verge of reality? Are we really about to live in the long-promised future of robots and AIs? As they used to say on the old Six Million Dollar Man TV show (point of order – Steve Austin was a cyborg, not a robot), we have the technology… or we're about to.

Src: github There are dependencies like openCV and Apache Spark but they are optional. I used openCV to perform feature extraction by HOG which speeds up the learning process, apache spark to compare results. Utils classes support computing additional data like confusion matrix and add methods to play with some well known datasets like iris or mnist. As it doesn't require any external libraries maybe someone will find it helpful when studying basics of machine learning.

Scientists have unveiled a new software that could pave the way for real-time DNA-authentication, with the ability to identify people from their DNA in just minutes. While it might sound like something out of a science fiction movie, researchers involved in the work say it could be used for everything from crime scene analysis to experimental cancer research. The software works with a credit-card sized DNA sequencer, and can confirm the identity of a person or cells with near-perfect accuracy. Scientists have unveiled a new software that could pave the way for real-time DNA-authentication, with the ability to identify people from their DNA in just minutes. The researchers use MinION to sequence random strings of DNA.

China wants to become the world leader in artificial intelligence (AI) by 2030, and the U.S. should make sure to keep pace, according to a former …

Estimating the joint probability mass function (PMF) of a set of random variables lies at the heart of statistical learning and signal processing. Without structural assumptions, such as modeling the variables as a Markov chain, tree, or other graphical model, joint PMF estimation is often considered mission impossible - the number of unknowns grows exponentially with the number of variables. But who gives us the structural model? Is there a generic, 'non-parametric' way to control joint PMF complexity without relying on a priori structural assumptions regarding the underlying probability model? Is it possible to discover the operational structure without biasing the analysis up front? What if we only observe random subsets of the variables, can we still reliably estimate the joint PMF of all? This paper shows, perhaps surprisingly, that if the joint PMF of any three variables can be estimated, then the joint PMF of all the variables can be provably recovered under relatively mild conditions. The result is reminiscent of Kolmogorov's extension theorem - consistent specification of lower-order distributions induces a unique probability measure for the entire process. The difference is that for processes of limited complexity (rank of the high-order PMF) it is possible to obtain complete characterization from only third-order distributions. In fact not all third order PMFs are needed; and under more stringent conditions even second-order will do. Exploiting multilinear (tensor) algebra, this paper proves that such higher-order PMF completion can be guaranteed - several pertinent identifiability results are derived. It also provides a practical and efficient algorithm to carry out the recovery task. Judiciously designed simulations and real-data experiments on movie recommendation and data classification are presented to showcase the effectiveness of the approach.

Sony today announced PlayStation Vue subscribers can now control their TVs, hands-free, with Amazon's smart assistant, Alexa. The addition is a welcome one. As good as Sony's TV remote is, there's nothing quite as simple as barking orders at a digital device instead. Besides, if we're being honest, the menu system in Vue isn't all that great to begin with. Now, instead of fussing with it, you can just say things like "Alexa, go to NBC" or use any number of playback commands, like: Alexa can be used both from Echo devices -- like Dot or Echo 2 (the original Echo works too) -- or the Fire TV remote.

It may look like a mild-mannered home security camera, but Amazon's AWS DeepLens is anything but. Announced today at the AWS re:Invent 2017 conference, the $249 (£185/AU$330 converted) DeepLens video camera is designed to help train developers in deep learning programming techniques. April 14, 2018, is the projected date of availability on Amazon.com, Deep learning has become a catch-all term for the AI smarts that dominate today's smart home. It's what fuels Amazon's Alexa-enabled speakers, what makes them able to differentiate among various voices, and what makes facial recognition cameras able to distinguish you from your neighbor. And they're only getting smarter -- at least, that's Amazon's hope.

Hollywood storylines notwithstanding, machine learning and artificial intelligence dominate discussions in the world of enterprise technology these days. Nary a data-focused vendor doesn't have some story to tell, either of AI and ML already baked into their solutions, or at least some ambitious roadmap. The potential benefits are vast, and cover the spectrum of data-oriented needs: quality, access, relevance, context, richness. But these algorithms are not for the weak willed, and they must be applied with tremendous attention to details. How can your organization make the most of these new tools?

Artificial intelligence and machine learning are two phrases that are thrown around a lot in the tech world these days. It has gotten the point where every company has to say they're an AI company, even if they really don't have AI capabilities, just to be taken seriously. The problem is that the best data scientists all want to work for the same few companies: Google, Facebook or Apple. So what are the smaller companies to do? That's the problem that H2O.ai is solving.