Artificial intelligence is no longer the stuff of the future. It is becoming an increasingly common part of our everyday lives, from the watches we wear that monitor our heart rate to the robots that vacuum our floors without any direction from us. Yet we have traditionally thought of artificial intelligence as similar to a robot that has a mind of its own and can act independently. While these robots would certainly qualify, they are not the only examples. Google - the leader in almost all things Internet-related - has introduced RankBrain, which is a machine learning algorithm. It is an example of ANI, just like the spam filters in your email or the "recommended products" feature on Amazon.

The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.

I'm really fond of Bayesian methods, so I decided to spend some time wrapping my head around modern Bayesian ideas, especially combined with Deep Learning. I'm mostly interested in Variational Inference at the moment, so my reading list is I hope to write a blogpost (or maybe a series of?) summarizing all these works and putting them in a common context. BTW, if you know interesting papers that marry Bayesian methods with Deep Learning -- I'd be interested to hear about them.

Google is hiring and there are lots of opportunities to do Machine Learning-related work here. Kevin Murphy is applying Bayesian methods to video recommendation, Andrew Ng is working on a neural network that can run on millions of cores, and that's just the tip of the iceberg that I've discovered working here for last 3 months. There is machine learning work in both "researcher" and "engineer" positions, and the focus on applied research makes the distinction somewhat blurry.

As card not present (CNP) transactions grow, online fraud is growing too, and with EMV migration in the US it is expected to grow even more. No wonder fraud is so high on merchants' list of concerns! Next to omnichannel, big data and cross-border, fraud was also one of the main keywords of the CNP EXPO 2016 edition. Amongst the top challenges in fighting fraud is the immense volumes and velocity of data generated by the big brands. This poses new, unique problems but it also creates opportunities to transform business and create competitive advantage.

Physical beauty is subjective and often difficult to define. But for the robot jury of Beauty.AI, an online competition billed as "the first international beauty contest judged by artificial intelligence," beauty is calculated by a set of complex algorithms that measure parameters like participants' facial symmetry and skin quality. The contest, launched in December, is an experiment by Youth Laboratories, an international team of data scientists and biogerontologists interested in developing anti-aging technologies. Its aim is to test and demonstrate how computers can learn to assess human attractiveness. The robot jury uses algorithms to analyze and rate participants' selfies submitted through the Beauty.AI app.

Many people think that the healthcare sector will greatly benefit from the Fourth Industrial Revolution. Professor Klaus Schwab, founder and executive Chairman of the World Economic Forum, describes this revolution as "a fusion of technologies that is blurring the lines between the physical, digital and biological spheres." Many of the discussions around technology and healthcare have, in broad terms, focused on the internet of things, telemedicine, personalized medicine, and robotics. But how exactly are these technologies going to be transformative? As the Chair of the Global Agenda Council on Artificial Intelligence and Robotics, I've been part of many discussions on the impacts of these technologies, both good and bad.

But this idea of superintelligence is fallacious because it assumes that the brain is "like a computer", i.e. a biological information processing machine. Our brain is notneither a computer nor a "machine". It does not "store memories" and does not "process information". We use such words to describe the brain metaphorically, not literarily. Musk and Hawking confuse metaphor with reality, and they do so systematically. Indeed, Hawking has repeatedly said that he believes that in the future humans will be able to "download their consciousness" in a computer and live forevermore.

The chip, announced late last month, is called Swarm. It was developed by researchers at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) with the aim of making parallel programs more efficient, faster and easier to write. Swarm speeds up processing performance, allows more operations to be run at one time and reduces the amount of code developers need to write by up to 90 percent. Programs developed for Swarm are typically three to 18 times faster than their standard versions. The researchers ran six common algorithms through Swarm and compared the outcomes with the best existing results for running each algorithm using parallel processing.

SAP, the 23.3 billion market leader in Enterprise application software has upped its game in India, by bringing into its campus in Whitefield, Bengaluru, the first batch of startups that are working on core technologies like artificial intelligence and cloud-based software. The new tenants at SAP Labs India campus will be housed at the "StartUp Studio" – a 75 seat incubation facility. Every year, the studio will take around 10 startups, provide them with technical expertise and support and give them access to the large base of SAP customers where appropriate. . The end goal of the Studio is to fine-tune the business of these startups and take them to their next stage of growth. "Startup innovations are happening in India and we recognise that we have to support the Startup Policy launched by the Prime Minister this year," says Bernd Leukert, Member of the Executive Board at SAP, Products and Innovation.