The growing number of services that run machine learning algrithms under the hood will now be able to tap the popular open-source database Redis' capabilities much more easily than before. Typically used to cache applications' most frequently accessed information, Redis has received a new extension today that brings integration with Spark ML. Organizations that employ that framework to power their machine learning projects can now easily load models into Redis to improve their performance. Initial benchmark tests indicate that the system provides five to 10 times lower latency than a standard Spark implementation when performing certain tasks such as categorizing records. The assessment was carried out by Redis Labs Inc., the startup behind the new module and the database's main commercializer.

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Imagine being paralysed and having an implanted microchip that could action a message from your brain to move your prosthetic arm. Or a diagnostic system that could pick up Alzheimer's a decade before you develop any symptoms. Or a 3D printing machine that could print a pill with a combination of drugs tailored just for you. The faculty chair of exponential medicine at the Silicon Valley-based Singularity University, no one could be more serious – or ambitious – about the revolutionary impact that digital technologies will have on the future of healthcare. The internet of things, constant connectivity, ever cheaper hardware, big data, machine learning: Kraft's list of converging "meta-trends" goes on.

How Can Lean Six Sigma Help Machine Learning? Does Machine Intelligence Reflect an Engineer's Internal Bias? Can Natural Language Processing Boost Clinical Documentation? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term.

How Can Lean Six Sigma Help Machine Learning? Does Machine Intelligence Reflect an Engineer's Internal Bias? Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

Given that the reality of AI may be fast approaching, it's of the utmost importance that we work out what might a future with artificial intelligence might look like. Last year, an open letter with signatories including Stephen Hawking and Nick Bostrom called for AI to be of demonstrable benefit to humanity, or risk something that exceeds our ability to control it. AI, as conceived of in popular culture, does not yet exist, even if autonomous and expert systems do. Smartphones might not be supercomputers, but they are called "smartphones" for good reason, in terms of how their operating systems function. Equally, we are happy to talk about a computer game's "AI", but gamers quickly learn to take advantage of its limitations and inability to "think" creatively.

A well-funded Tech start up in Bristol looking for a Data Scientist specialising in machine learning to join a growing team. You will be able to implement high level machine learning techniques and big data technologies on a unique platform.

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Those new glasses make you look completely different – especially to face recognition software. A team of researchers from Carnegie Mellon University has fooled face recognition algorithms using the oldest trick in the book: a pair of fake glasses. By printing bespoke patterns onto the front of the frames, they enabled wearers not only to obscure their identity but to impersonate people who look completely different, at least in the eyes of the algorithms. A white male researcher wearing the glasses was able to pass for American actress Milla Jovovich while a South-Asian female colleague was digitally disguised as a Middle-Eastern male. The system wasn't perfect, however: a Middle-Eastern male trying to use the glasses to pass as white British actor Clive Owen only succeeded 16 per cent of the time.

This long article with a lot of source code was posted by Suraj V Vidyadaran. Suraj is pursuing a Master in Computer Science at Temple university primarily focused in Data Science specialization. His areas of interests are in sentiment analysis, data visualization, big data and machine learning. I was surprised to see the overlap with our recent article on top 10 machine learning algorithms. You can read the full article (with voluminous source code in R) here.