At long last, Microsoft has set a public target of bringing consumer devices--that aren't PCs--to market in 2017 with its personal digital assistant, Cortana, inside of them. On Tuesday, Microsoft announced a way to challenge Amazon's Alexa-powered Dot and Echo devices, as well as Google's Google Home. Microsoft's New Devices SDK will allow to power a new line of connected-car speakers from Harmon Kardon in 2017, executives said, with presumably more partnerships to come. This follows an earlier announcement that Cortana is coming to Windows 10 IoT, Microsoft's operating system for smart devices. Why this matters: Though Apple, Microsoft, and Google all have prominent artificial-intelligence initiatives, it's been Amazon--and, to a lesser extent, Google--that have designed devices around their technology.

In this post, we are going to introduce you to the Support Vector Machine (SVM) machine learning algorithm. We will follow a similar process to our recent post Naive Bayes for Dummies; A Simple Explanation by keeping it short and not overly-technical. The aim is to give those of you who are new to machine learning a basic understanding of the key concepts of this algorithm. Support Vector Machines – What are they? A Support Vector Machine (SVM) is a supervised machine learning algorithm that can be employed for both classification and regression purposes. SVMs are more commonly used in classification problems and as such, this is what we will focus on in this post. SVMs are based on the idea of finding a hyperplane that best divides a dataset into two classes, as shown in the image below.

It was penned by an AI developed at the University of Toronto, which builds on deep learning capabilities like those of Google's DeepDream. Instead of turning images into hallucination nightmare dogs, this project took seasonal inputs and kicked out its best approximation of a real human carol. It's called "Neural Karaoke," because good science is unnerving science. To prepare, the AI was given 100 hours of Christmas music, 50 hours of song lyrics, and video feed from the game Just Dance. So the thing can dance too.

On a nondescript street in downtown San Francisco, an unremarkable parking garage with dusty walls and painted-over windows seems like an abandoned warehouse. Row upon row of shiny self-driving cars await their moment. At first glance, they look like regular dark-gray Volvo SUVs and white Ford sedans. But they're decked out with rectangular sci-fi contraptions on their roofs. The Uber logo is emblazoned across their doors.

If you're relatively new to Machine Learning and it's applications, you'll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around. Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine Learning terms which have been broken down in simple English, making them easier to understand. So, if you're struggling to understand the difference between Supervised and Un-supervised Learning you'll enjoy this post. A subfield of computer science and artificial intelligence (AI) that focuses on the design of systems that can learn from and make decisions and predictions based on data. Machine learning enables computers to act and make data-driven decisions rather than being explicitly programmed to carry out a certain task.

Drones can do some incredible acrobatics. If you were somehow a passenger on that drone and weren't a trained fighter pilot (and maybe even if you were), you'd pass out and very likely die. Drones don't do a lot of passenger carrying at the moment, which is probably for the best, but we've seen enough crazy ideas to suggest that using autonomous drones instead of autonomous cars to transport humans is probably going to be a reality within a handful of decades.* At the École Polytechnique Fédérale de Lausanne (EPFL), in Switzerland, a group of researchers led by Dario Floreano is already worrying about how we're going to handle personal drone flights, especially in situations where a lot of drones are trying to go in a lot of different directions at the same time. They've come up with an algorithm that allows drones to avoid collisions with each other while also not turning humans into mounds of quivering goo.

Microsoft's Cortana virtual assistant is getting a lot smarter. On Tuesday, the company announced a set of developer tools aimed at bringing it into the internet of things, and adding support for developers to build new functionality for it. The makers of IoT devices like speakers and cars will be able to use a Microsoft software development kit to integrate Cortana into their products. In addition, developers will be able to build custom integrations that add capabilities to Microsoft's virtual assistant. Microsoft is also launching a new service designed to help users simplify the process of scheduling meetings.

This is a great interview with Abhik Banerjee. Abhik is a Staff Data Scientist at Kohl's Technology (at the Kohl's departmental stores) where he leads a team on machine learning and data mining projects. He also provides strategic direction around these areas to senior executives at Kohl's. Abhik has an interesting role because he has to marry online and physical store analytics to provide insights to Kohl's. Abhik received his MS in computer science from the University of Cincinnati in 2012. Welcome to another episode of Flyover Labs. Today we are lucky enough to have Abhik Banerjee with us. And Abhik is a Staff Data Scientist at Kohl's Department Stores where he works on machine learning and data mining projects.

If you're a fan of the X-Men comics series, you're familiar with Cerebro, a fictional device that taps into the brain waves of humans and has the ability to identify mutants by an individual's thoughts and experiences. Wouldn't it be terrifying if Cerebro were real? If we could indulge in mind reading at a global scale? While we haven't discovered that capability -- neural quantum entanglement anyone? The deepest insights of a quarter of humanity are available for analysis.

RETAIL FORECAST: U.S. consumers will spend $632 billion online by 2020 Nordstrom launched its first chatbot on the Facebook Messenger and Kik messaging services to help holiday shoppers find gift items, Geek Wire reports. The chatbot helps users select gift items by asking them a series of questions about who they are shopping for. It then chooses gifts from Nordstrom's online store that match their answers. Users can also type in a request for gift ideas that are forwarded to Nordstrom customer service representatives who respond with customized gift ideas based on the request. The chatbot will available to consumers up until December 24, and was developed in partnership with Snaps, a New York-based startup that helps brands develop their own chatbots.