If you're not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms' accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build. But breakthroughs in deep learning aren't the only reason this is a big moment for machine learning. Just as important is that over the last five years, machine learning has become far more accessible to nonexperts, opening up access to a vast group of people. For most software developers, there have historically been many barriers to entry in machine learning, most notably software libraries designed more for academic researchers than for software engineers as well as a lack of sufficient data.

I've been trying to think of a way to describe how big Machine Learning is, and I think I finally have a decent one: Because Statistics is the primary mechanism we've had for decades for learning about the world. Machine Learning is similar, except its method of doing it is far more powerful. Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed. Most importantly, Machine Learning can…well, learn. With traditional Statistics you can potentially extract additional insights with more (and better) data, but the model for doing the analysis itself doesn't improve.

Facebook recently announced a rather scenically named system, Big Sur, designed around Nvidia's Tesla compute cards aimed at helping their neural networks, and obviously machine learning, become faster and more versatile. They are, of course, not alone. IBM Watson has similar visions as does Microsoft. Big Data and analytics have long staked claim to the holiday shopping season, but I sense that this 2015 holiday shopping season the real big winner will be Machine Learning. The big gun in the Machine Learning camp is the aforementioned IBM Watson.

Recent headlines about Artificial Intelligence (AI) flooding legal press in recent months with accompanying images of human-like robotic associates have many lawyers asking: will AI really take our jobs? And the follow up: can Siri or Alexa help me quickly research basic legal principles? My short answers are currently "no," and "not quite yet." The answer to the former is not likely to change; the answer to the latter is subject to change at any moment. In the meantime, my discussions around AI, or cognitive computing, and its place in the law, as well as my recent transfer from litigation partner to innovation partner, have allowed me to reach some (preliminary) observations about it all, and preview where I think the technology can and should be going in the practice of law – particularly, for the purposes of this article, litigation.

With the advent of Network Function Virtualization (NFV), Network Functions (NF) will no longer be tightly coupled with the hardware they are running on, which poses new challenges in network management. Noisy neighbor is a term commonly used to describe situations in NFV infrastructure where an application experiences degradation in performance due to the fact that some of the resources it needs are occupied by other applications in the same cloud node. These situations cannot be easily identified using straightforward approaches, which calls for the use of sophisticated methods for NFV infrastructure management. In this paper we demonstrate how Machine Learning (ML) techniques can be used to identify such events. Through experiments using data collected at real NFV infrastructure, we show that standard models for automated classification can detect the noisy neighbor phenomenon with an accuracy of more than 90% in a simple scenario.

While the number of headlines about machine learning might lead one to think that we just discovered something profoundly new, the reality is that the technology is nearly as old as computing. It's no coincidence that Alan Turing, one of the most influential computer scientists of all time, started his 1950 treatise on computing with the question "Can machines think?" From our science fiction to our research labs, we have long questioned whether the creation of artificial versions of ourselves will somehow help us uncover the origin of our own consciousness, and more broadly, our role on earth. Unfortunately, the learning curve on AI is really damn steep. By tracing a bit of history, we should hopefully be able to get to the bottom of wtf machine learning really is.

Watson, can you grow into a multibillion-dollar business and become the engine of IBM's resurgence? IBM is betting its future that the answer is yes. Its campaign to commercialize Watson, the company's version of artificial intelligence technology, stands out, even during the current A.I. frenzy in the tech industry. IBM has invested billions of dollars in its Watson business unit, created at the start of 2014, which now employs an estimated 10,000 workers. Its big-ticket marketing push includes clever television ads that feature Watson trading quips with famous people like Serena Williams and Bob Dylan.

In AI-Ready or Not, Weber Shandwick surveyed global consumers and senior ranking marketers on their attitudes toward and expectations for artificial intelligence (AI). The following provides the results of the consumer perspectives and what those implications mean for marketers. In its most basic definition, AI is intelligence exhibited by machines. It is frequently thought of as robotics, but encompasses a broader range of technologies, some of which are in wide use among the general population today.

Jason Tan is the co-founder and CEO of Sift Science. He's also held leadership and engineering roles at BuzzLabs, Optify and Zillow. Facebook recently fired the entire Trending Topics team of human editors amid accusations they were promoting specific agendas and biasing what news was deemed "important." Now the company is relying on machine learning algorithms to manage Trending Topics -- and finding that keeping the results free of hoaxes and fake news isn't always easy. The social media giant recently assured an audience at TechCrunch Disrupt that it was working on new technology that would help prevent untrue or satirical stories from being labeled as legitimate news we should follow.

The text is supported by a strong outline. The authors preserve much of the introductory material, but add the latest techniques and developments in data mining, thus making this a comprehensive resource for both beginners and practitioners. The focus is data-all aspects. The presentation is broad, encyclopedic, and comprehensive, with ample references for interested readers to pursue in-depth research on any technique. "This interesting and comprehensive introduction to data mining emphasizes the interest in multidimensional data mining--the integration of online analytical processing (OLAP) and data mining. Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers."