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BakerHostetler became one of the first BigLaw players to publicly license AI technology for use in its firm, when it teamed up with the legal tech company ROSS this summer. The BigLaw firm is putting ROSS's AI technology to work in its Bankruptcy, Restructuring, and Creditor Rights practice, helping attorneys research the law a bit faster. But while BakerHostetler might have been first, it wasn't the last; other big name firms soon followed, bringing AI in to their practices. Plenty of firms are taking a cautious, wait-and-see approach to tech adaptation. It is, even if it's just through client demands.

Conservatives are girding for an extended clash on two fronts in the months ahead: one with a possible Clinton administration and one with Republicans who rejected Donald J. Trump. Megyn Kelly's divergent approach at Fox News took a different turn in her exchange with Newt Gingrich and again raised the question of the channel's future. A lot of healthy people are defying predictions by the Affordable Care Act architects and refusing to enroll, throwing off the calculations behind the system. The startling double-digital declines in TV viewership raise questions about whether the football and soccer leagues have reached their peak. Mr. Beatty's "Rules Don't Apply" is the first film he has written, directed and starred in since "Bulworth" in 1998.

Finding the right piece of data can be a big challenge. With the latest release of Collibra's data governance and data catalog solution, machine learning algorithms help the product learn what types of data you use with the goal of surfacing and recommend new data sources that are appropriate for your job. This use of machine learning is one of the features in Collibra 5.0, the latest release of the company's flagship data governance solution that was formally announced yesterday. The Collibra Catalog is one of applications enabled atop this core platform that hundreds of companies use to keep track of big data sitting in Hadoop, Hive, and other locations. "We have a technology platform that has the capability to keep track of processes around data, the metadata and organizations and roles and who has responsibility for data," says Daniel Sholler, director of product marketing for Collibra.

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.