As Apple plays catch up in many ways, the Mountain View company is doubling down on machine learning efforts that will play an important role in future products. Earlier this week, Google launched a dedicated Machine Learning research group in Europe. Google Research, Europe is based out of the company's Zurich office -- which is already home to the company's largest engineering presence outside of the US. Googlers there were responsible for developing the engine that drives Knowledge Graph and are currently working on the conversation engine that powers the upcoming Google Assistant in Allo. Engineers will specifically focus on natural language processing & understanding, machine intelligence, and machine perception.

Dan Schutzer (photo left), a senior technology consultant at the Financial Services Roundtable's BITS technology division defined artificial intelligence as'the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.' According to Schutzer, efforts in the 1990s to build artificial intelligence-like systems for use in financial services has resulted in'disillusionment as realization set in that these systems were harder and more costly to build and maintain than first anticipated.' Fast forward to 2015 when advances in high performance computing, algorithmic theory and cloud computing are bringing us closer to true AI capabilities for commercial use by the financial industry. Patrick Tucker, author of The Naked Future: What Happens In a World That Anticipates Your Every Move?, wrote that "When the cost of collecting information on virtually every interaction falls to zero, the insights that we gain from our activity, in the context of the activity of others, will fundamentally change the way we relate to one another, to institutions, and with the future itself." "One of the first things to note about AI is its ability to process enormous amounts of data very quickly and far more data than it's ever been processed in the past by humans or computer programs. That is going to enable banks to improve the services they provide to customers, including better, more targeted advice," said Astrid Raetze (photo right), a partner at Baker & McKenzie.

If you follow me on Snapchat (msuster) you might already know that I've been looking at and investing in a number of companies in the computer vision space. My thesis is that it will become a major I/O computing metaphor or as this field is sometimes referred to HCI (human-computer interaction). Today I am so excited to announce our latest investment in the category -- Nanit -- which is a smart baby monitor. The objective behind Nanit is to help parent "sleep more and monitor less." By using computer vision Nanit is able to better help parents understand how well a child is sleeping and if they're having difficulties what the causes may be (sound, ambient light, temperature or even, gasp, too much parental interference).

Machine learning represents a paradigm shift in programming, there's no doubt. The concept has begun to weave its promising tendrils through everything from Facebook's image software to Gmail's spam filter, but a crafty engineer thought up a better use: a real-life sorting hat inspired by the character in J.K. Rowlings's beloved Harry Potter series. The impetus for project lead Ryan Anderson, a tech hobbyist by night and a solutions architect for IBM by day, was a creation that would entertainingly instill in a young audience the importance of math, technology, and science. "I was thinking of fun projects and, coincidentally, I have a couple of daughters, and they are mad keen on'Harry Potter,'" he told Tech Insider. "They've read the books, like, five times."

The method of regularization is very popular in the field of machine learning however you will see that many people are still not using it. One reason I can think of is because of the complexity behind the whole concept of the regularization so I thought to make it simple for all of us. In this article I am going to try to explain the regularization in a way that it is easy to understand and easy to use. Basically while I explain the concept I will give practical details t on how to implement regularization in R and SAS. In very simple terms Regularization refers to the method of preventing overfitting, by explicitly controlling the model complexity.

Sarcasm is difficult to detect online, whether as a user or as an algorithm. Sentiment analysis can help, but there are limits to its effectiveness. An article from cloud-based social intelligence agency Infegy examines the problems resulting from the use of sarcasm online and offers solutions for more accurately identifying and dealing with sarcasm.

It seems like almost every day a new headline warns us that artificial intelligence (AI) will soon take over the world, or at the very least steal jobs. Even when AI is not in the news, Hollywood offers up a steady stream of entertainment that depicts a very near future in which life as we know it is threatened by super-intelligent machines. These scenarios have something in common: they oversimplify and misrepresent an important and broader set of transformative technologies that hold great promise for business and society. They indulge in fantasy rather than take into account a rational and better-informed dialogue currently underway in the scientific, policy and business communities about what we consider the third age of computing – the cognitive era. What is Cognitive Computing Cognitive computing -- of which AI is but one part – refers to an entirely new class of technologies whose purpose is to deepen human engagement, scale and elevate expertise, enable new products and services, and enhance exploration and discovery.

The more discretionary and quant traders try to analyze market price action, the higher the chances of failure. This may sound counter-intuitive because it contradicts the common belief that the more one tries to achieve a goal, the higher the chances of success. But this is not how things work in the markets. Confirmation bias is a primary cause of failure of discretionary traders. Data-mining bias is a primary cause of failure of quant traders.

Google announced today that it is expanding its largest non-U.S. The new Machine Learning Research Group will be based in Zurich, Switzerland, which is already home to Google's largest research center outside the U.S. The company did not say specifically how many positions will be added. But in a blog post, Google executives said machine learning has become critical to the company's development efforts across a wide range of services. "Google's ongoing research in Machine Intelligence is what powers many of the products being used by hundreds of millions of people a day -- from Translate to Photo Search to SmartReply for Inbox," wrote Emmanuel Mogenet, head of Google Research in Europe. Indeed, the Zurich research center has already had a sizable impact on Google.

Join our next free training with Kenji Lefèvre, Product Manager at Dataiku, to understand how Dataiku streamlines the deployment and production of predictive applications with Dataiku 3.0. If the timing is not convenient, register to receive the recording of the session afterwards. Kenji Lefevre is Dataiku's Product Manager, a collaborative platform to design, build, and run predictive applications from start to finish. Before joining Dataiku, Kenji worked as a freelance data scientist. He holds a PhD in mathematics on homotopical algebra and is particularly interested in the popularizing of science.