Maybe the machines won't take over, but Eric Schmidt, chairman of Google parent company Alphabet, thinks machine learning might. The combination of cloud, crowdsourced information, and machine learning "will be the basis for every fundamental and hugely successful IPO win in the next five years," he said during this morning's keynote at GCP NEXT, the developer conference for Google Cloud Platform. Schmidt is prone to sweeping statements. But having watched computing transform many times in 45 years -- he was a Sun Microsystems bigwig when the company launched Java -- he said he felt qualified to predict that machine learning could lead to truly new innovations, the kind that can't yet be envisioned. Google is now offering the technology to cloud customers in the form of Cloud Machine Learning, an alpha application launched today.

As artificial intelligence (AI) technologies such as the Amazon Echo and self-driving cars are hitting the market, they are poised to become an essential part of society. For people around the world, especially college students, this could mean some big changes. Once created, an AI can be used either internally or externally. Internal interfaces are located on a cloud and can access other devices and software that is used in the home such as a TV or smartphone. Technology like the Amazon Echo, which can access apps that are downloaded on a smart phone, is an example of this, but in comparison this technology is rudimentary compared to what others have developed.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

New York – When Toyota aired a Super Bowl television ad featuring a surprisingly quick Prius gas-electric hybrid eluding police, it marked a turning point for the auto industry. For years, automakers pushed fuel efficiency to sell hybrid and electric vehicles. Now, in an era of cheap gasoline, the message is: These cars are faster and quieter than their gas-powered counterparts. And, yes, you still save on fuel. "They've graduated out of the class of something that's a bit of an oddity to drive," says Mike O'Brien, vice president of product planning for Hyundai.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

If you think deep, you'd realize the whole process of predictive modeling is a war. Consider the data set as your opponent. Your win will depend on the intuitive usage of your knowledge & strategy to get highest accuracy. So, how many times have you won this battle? I realized it last week.

Twitter started the trend of'People to Follow'. This later got replicated by other platforms such as Facebook, Linkedin, Quora and GitHub. This cool feature lets you connect with the rockstars of various domains and get an access to what is going on their end without bothering them much. For the influencers, this has become an effective way to communicate with their followers. The lives of people on GitHub doesn't appear to as tempting as you would observe on other platforms, but if you love coding, programming and data science, you'll surely enjoy the company of 9 million users on this platform!

Over a year ago, following an original presentation at MLConf, I wrote a blog post entitled "10 Lessons Learned from building ML systems". At that point, I was leading the Algorithms Engineering team at Netflix and those lessons reflected lessons we had learned there over the last few years. When you do a post/presentation like that, you don't really know how it is going to be received. Some things might be obvious to many while others might be controversial and some will not agree. It turns out though that it was very well received and referenced elsewhere (e.g.

DARPA has announced plans to hold a competition pitting electromagnetic spectrum receivers (think: TVs, wifi-enabled computers, radios). As the amount of mobile data traffic is growing at an exponentially rapid rate, DARPA believes we need to start refining the way we handle the crowded spectrum. The Spectrum Collaboration Challenge (SC2) will see research teams collaborating to create smart systems to share wavelengths using algorithms and artificial intelligence. Global mobile data traffic grew by 74 percent in 2015, and more than half a billion devices and connections were added to the overwhelmingly clogged spectrum, according to recent Cisco reports. Analysts expect that the monthly global mobile traffic rates will reach 30.6 exabytes by 2020 (rates are at 3.7 exabytes per month as of 2015).

IBM's Watson AI product is mostly rolled out live with machine learning halted to avoid "losing control" of its behaviour, Europe CTO Duncan Anderson has confirmed. Anderson said the idea of AI always learning and adjusting its behaviour is still something people are "a bit nervous about". "At the moment, you stop the learning before it goes live, so you don't get any surprises," Anderson told Computing at our Big Data & Analytics Summit 2016 in London. The aim, explained Anderson, is to "get a sensible kind of answer" from an AI in line with the business's expectations. He added that Watson's modular-based learning updates are now so advanced in specific areas of industry that it's now possible to sell "off the shelf" versions of the AI that can immediately get to grips with traditional tasks in a given area.