With the rapid rise of artificial intelligence, cognitive computing and machine learning, insurance carriers have the opportunity to join the travel and retail industry, and leading technology players such as Google, IBM and Microsoft who are already taking advantage of these smart technologies. This form of "conversational intelligence" or "thinking machine" technology learns from every human interaction to be predictive towards a users questions or proactive with a suggestion. One of the barriers carriers face is with the Data. "Collecting it, storing it, normalizing it, tracing its lineage and the critical – if not particularly sexy – matter of governance" The key is then harnessing modern software and technologies to translate these insights into action. "There's little arguing that the degree of complexity around big data in healthcare is exactly why clinicians, physicians and, indeed, the industry at large need these emerging technologies, which have felt so far away for so long."

A new USB stick computer uses Google's machine-learning software to give drones and robots the equivalent of a human eye, and add new smarts to cameras. It is instead designed to analyze pixels and provide the right context for images. Fathom provides the much-needed horsepower for devices like drones, robots and cameras to run computer vision applications like image recognition. These devices alone typically don't have the ability to run computer vision applications. Fathom uses an embedded version of Google's TensorFlow machine learning software for vision processing.

How does the image-recognition technology in a self-driving car respond to a blurred shape suddenly appearing on the road? Researchers from KU Leuven, Belgium, have shown that machines can learn to respond to unfamiliar objects like human beings would. Imagine heading home in your self-driving car. The rain is falling in torrents and visibility is poor. All of a sudden, a blurred shape appears on the road.

SAN FRANCISCO -- Google CEO Sundar Pichai says the next big evolution for technology is artificial intelligence. "Looking to the future, the next big step will be for the very concept of the'device' to fade away," Pichai wrote in the technology giant's annual founders' letter to shareholders. His vision: Over time, computers, whatever shape they take, a mobile device in your hand or a mini computer on your wrist, "will be an intelligent assistant helping you through your day." Google is far from alone. Artificial intelligence in which Google was a pioneer, is an increasingly crowded field.

Microsoft is the world's most valuable company, with a 261 billion market cap. And an IBM computer named Deep Blue defeats Garry Kasparov, reigning world chess champion and, at the time, the highest-ranked chess player to have ever lived. Though it was not the first time man has lost to machine, it is perhaps the most prominent, highly publicized by IBM and widely covered by the global media. It was viewed as a milestone for AI, the true arrival of computer intelligence. The world celebrated the achievement of technology -- or offered doomsday predictions of a robot revolution.

AI technologies such as machine learning will play a key role in shaping the future, Google CEO Sundar Pichai said in the company's annual Founders' Letter to stockholders on Thursday. "It's what has allowed us to build products that get better over time, making them increasingly useful and helpful," wrote Pichai, who cited examples such as voice search, translation tools, image recognition and spam filters. The recent victory of DeepMind's AlphaGo software over legendary master Lee Sedol at the ancient game of Go is "game-changing," Pichai added. Far from portending humanity's downfall, however, the victory is ultimately one for the human race, he said. "This is another important step toward creating artificial intelligence that can help us in everything from accomplishing our daily tasks and travels to eventually tackling even bigger challenges like climate change and cancer diagnosis," Pichai said.

A computer scientist who helped bring Apple's Siri to the world has invented a method for unlocking an iPhone by talking to it. The technology, revealed in an Apple patent application, would eliminate the need for a separate security step and allow users to be identified by voice at the same time they're telling the device to perform a task. "There are few security measures I can think of that aren't an additional step," said Bryant Walker Smith, an affiliate scholar at Stanford University's Center for Internet and Society. "Conceivably, you might unlock simply by saying, 'Apple, give me directions to Redmond, Washington,' and there would be no additional steps. Cutting away that one step makes it that much easier to interact with your device as a conversational partner or assistant, where you just ask a question rather than unlock and ask it a question."

Do you remember what it was like the first time you got your hands on an iPhone? When you realized that all the things that you used to have to do on separate devices now could be accomplished on one single device? Well, the minds behind LogicBlox would like you to feel the same way about its foundational technology that collapses multiple technology stacks into a unified smart database environment that aims to enable enterprises to create sophisticated and easily iterated applications in one place. Transaction and analytics co-exist in the platform, with the system utilizing a single declarative language with extensions – such as Machine Learning capabilities and statistical relational models – to support prescriptive and predictive analytics. Users can leverage LogicBlox' full-blown database functionality to train Machine Learning models, for use in solving forecasting or optimization problems, for instance.

This exercise represents an intended worst-case scenario for machine learning, as my aim was to test the results on a scarce dataset (n 52). In the Catalan linguistic area in Spain there is no census data about native speakers. Indeed, there is no real useful data about Catalan language from a data science perspective. I focus my attention in this case because until 1950, virtually 100% of the population of the Catalan linguistic area was Catalan native speaker, but from 1950 until now, population has doubled by immigration, that in addition to demographic and political reasons, resulted in Catalan as a minority language in most of its former linguistic area. Currently, we don't have extensive quantitative data to know the real situation or evolution of Catalan language in its territory.

It's no secret that machine learning - more specifically, deep learning - has been playing an increasingly noticeable role in the world of art, as of late. From Deep Dream, to Deep Forger, to Beyond the Fence, and further, all varieties of art have been touched by the creativity of neural networks, and it seems that this has not gone unnoticed by those outside of the direct sphere of machine learning. Gene Kogan, of the Tisch School of the Arts at NYU, has recently started up his inaugural offering of Machine Learning for Artists, an elective course in the school's Interactive Telecommunications Program (ITP). The ITP has the mission of exploring "the imaginative use of communications technologies," and how they may be leveraged for bringing art and delight into the lives of individuals. They self-identify as "a Center for the Recently Possible," a term I think is fantastic.