After all kind of appliances –needed and unneeded -- were made smarter thanks to the integration of Artificial Intelligence, Microsoft is finally bringing its Cortana suite to your fridge -- yuuup. The company is doing it in collaboration with Liebherr's appliance division, and we actually love what we have seen so far. The very idea of the product, and the vast possibilities it entails, actually is awesome and I am having trouble finding enough words in its praise. The fridge will literally be able to tell you if you are running out of milk! I mean what more can you ask of life?

Automation: A new frontier of Human-Machine partnership "At its very foundation, the raison d'tre of technology is to trigger and harness disruption for the benefit of people in a collaborative process.

Titled "Artificial Intelligence and Life in 2030," this year-long investigation is the first product of the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted by Stanford University to inform societal deliberation and provide guidance on the ethical development of smart software, sensors and machines. "We believe specialized AI applications will become both increasingly common and more useful by 2030, improving our economy and quality of life," said Peter Stone, a computer scientist at The University of Texas at Austin and chair of the 17-member panel of international experts. "But this technology will also create profound challenges, affecting jobs and incomes and other issues that we should begin addressing now to ensure that the benefits of AI are broadly shared." The new report traces its roots to a 2009 study that brought AI scientists together in a process of introspection that became ongoing in 2014, when Eric and Mary Horvitz created the AI100 endowment through Stanford's School of Engineering. AI100 formed a standing committee of scientists and charged it with commissioning reports on different aspects of AI over the ensuing century.

If you've watched the television series 24 on Netflix, the online streaming service will recommend that you catch up on Homeland, too. Now, a Boston-based technology firm is hoping to bring that same intuitive technology to financial services. Start-up indico Data Solutions Inc. announced Friday that it is collaborating with John Hancock Financial and the Boston insurer's Canadian parent Manulife Financial Corp. to help financial analysts and portfolio managers cull through thousands of pieces of data and find the most important information. Based on that data, managers at Manulife and John Hancock will be able to make quicker decisions on whether to invest in certain companies and industries or sell their shares. Think of it as Google on steroids.

Amazon poaches eBay A.I. chief, continues ramping up machine learning operations See The Eerie Sci-Fi Movie Trailer Made By IBM's Artificial Intelligence

It might not be obvious to a human, but an artificial intelligence programme sees distinct similarities in, say, the composition of the subjects, or colour of their outfits. Called Recognition, the AI programme compares current photojournalism provided by Reuters with works from the Tate's collection to find its best match. It pairs the images based on image recognition techniques that analyse objects, faces, composition, and context gleaned from metadata. The AI matches a photo of eunuchs putting on makeup in Mumbai (Image: REUTERS/Danish Siddiqui TPX IMAGES OF THE DAY) with Sir Peter Lely's Two Ladies of the Lake Family c.1660 (Image: Tate) "We wanted to understand how we could bring artificial intelligence into a museum, and how rational and objective thinking could be applied to a subjective thing like art," said Angelo Semeraro of Italian research centre Fabrica. He and teammates Coralie Gourguechon, Monica Lanaro, and Isaac Valentin created the project to enter (and win) the Tate's 2016 IK Prize for digital innovation, which is run in partnership with Microsoft.

Beauty is in the eye of the beholder, they say. And in a futuristic beauty contest (though some might argue judging people based on their looks isn't all that forward-thinking), the beholders are a bunch of robots. The Beauty.AI 2.0 contest is an international beauty contest judged by artificial intelligence. The robot jury is comprised of five algorithms -- RYNKL (judging your wrinkles), PIMPL (complexion), MADIS (how you compare to models within your age and racial group), Symmetry Master (symmetry of your face) and AntiAgeist (how old do you really look?). The objective of this beauty contest wasn't simply to show people what robots think are beautiful -- it was to test biogerontologists and data scientists' theory "that in the near future machines will be able to get a lot of vital medical information about people's health just by processing their photos," the Beauty.AI website says.

The optimism around modern technology lies in part in the belief that it's a democratizing force--one that isn't bound by the petty biases and prejudices that humans have learned over time. But for artificial intelligence, that's a false hope, according to new research, and the reason is boneheadedly simple: Just as we learn our biases from the world around us, AI will learn its biases from us. There was plenty of reason to think AI could be unbiased. Since it's based on mathematical algorithms, AI doesn't start off with any explicit preference for white-sounding names or a belief that women should stay at home. To guard against implicit biases--biases that programmers might build into AI without realizing it, sort of like how standardized tests are biased in favor of whites--some have recommended transparent algorithms, more diverse development teams, and so on.

DynaML is a Scala environment for conducting research and education in Machine Learning. DynaML comes packaged with a powerful library of classes for various predictive models and a Scala REPL where one can not only build custom models but also play around with data work-flows. The data/ directory contains a few data sets, which are used by the programs in the dynaml-examples/ module. Lets run a Gaussian Process (GP) regression model on the synthetic'delve' data set. In this example TestGPDelve we train a GP model based on the RBF Kernel with its bandwidth/length scale set to 2.0 and the noise level set to 1.0, we use 500 input output patterns to train and test on an independent sample of 1000 data points.

Amazon is taking on a new machine learning team, to operate out of Cambridge, UK, according to a Facebook post spotted by reporter Jack Clark. Neil Lawrence, a professor of machine learning and computational biology at the University of Sheffield, announced to his Facebook followers that he and his team of students will be joining the Seattle tech titan. Ralf Herbrich, Amazon's Director of Machine Learning Science, said that Lawrence's team will partner with his operation in Berlin, in a comment on the Facebook thread. Herbrich's team focuses on "Forecasting, Content Linkage, Scalable Machine Learning Services and Vision-Assisted Technologies," which may offer a glimpse into the kind of work that the new UK team will be doing. Both Herbrich and Lawrence previously worked for the Seattle-area's other tech titan, Microsoft.