This talk presents unsupervised analysis techniques that can be applied to collections of unstructured text documents for the purpose of discovering hidden topical trends, correlations or anomalies in their data. The techniques presented are applicable to a wide range of document types including news stories, technical blogs, customer feedback forms, congressional records, and legal documents, among many, many others. The talk will include introductory descriptions of the processing techniques needed to pre-process text data, discover salient multi-word phrases, and learn latent topic models describing the topical content of a collection of text data. The primary focus of the talk will be on analytic techniques that can be applied to the output of a latent topic model to extract trending topics over time, uncover topical correlations with other document features or meta-data, and discover anomalies in a text corpus. To illustrate these techniques, examples using news wire and congressional record data will demonstrate how important events in news wire data and anomalous congressional actions and interesting correlations can be discovered automatically using the presented unsupervised techniques.

Cisco Live 2016 kicks off next week in Las Vegas, and Constellation Research VP and principal analyst Alan Lepofsky will be in attendance. Lepofsky heads up Constellations' research into the Future of Work, focusing on collaboration software and emerging technologies that are changing the way people do their jobs. I spoke with him in advance of Cisco Live for a preview of what he's expecting at the conference, as well as an overview of current and future trends in the collaboration space. What follows is an edited transcript of that conversation. CRInsights: What are your thoughts on Cisco's current big bet in collaboration software, Spark?

I first heard the term "machine learning" a few years ago, and to be honest, I basically ignored it that time. I knew that it was a powerful technique, and I knew that it was in vogue, but I didn't know what it really was-- what problems it was designed to solve, how it solved them and how it related to the other sorts of issues I was working on in my professional (consulting) life and in my graduate-school research. But in the past few years, machine learning has become a topic that most will avoid at their professional peril. Despite the scary-sounding name, the ideas behind machine learning aren't that difficult to understand. Moreover, a great deal of open-source software makes it possible for anyone to use machine learning in their own work or research.

We are constantly bombarded by numerous travel sites that claim that they hold the key to planning a wonderful trip. Needless to say, it can be stressful figuring out exactly which service provides the best outcome. Amidst the noise, Hipmunk has come out as one of the leaders in the travel industry. It constantly researches and implements new technology that makes it easy for anyone to utilize its services. What's more, the company successfully implements artificial intelligence to provide you with the best travel information your itinerary can find.

In an effort to apply data-driven insights to one of the most fundamental aspects of running a health care system, UPMC announced today that it has formed Pensiamo, an independent company that aims to help hospitals improve supply chain performance through a comprehensive source-to-pay offering, including cognitive analytics with IBM Watson Health technologies. IBM is a minority owner of Pensiamo. Supply chain costs are the second-largest and fastest-growing expense behind labor costs for health care providers. The Institute of Medicine estimates that nearly one-third of health care spending is waste. In today's dynamic environment, providers face mounting pressure to improve the effectiveness of patient care while controlling costs.

Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

One of the consistent characteristics of the tech industry is an endless labelling of technology and approaches. Some of it is foundational resulting from some entirely new. Much of it is re-categorizing something, either because it is suddenly trendy again or because a set of ideas have been organized in a new way. When I was in my 20s, I found this exciting. Now that I'm in my 50s and am used to this, I find it relaxing, as it makes me feel at home.

Machines that "learn" and make decisions on their own are proliferating in our daily lives via social networks and smartphones, and experts are already thinking about how we can engineer them so that they don't go rogue. So far, suggestions have ranged from "training" self-learning machines to ignore certain kinds of information that might teach them racism or sexism, to coding them with values like empathy and respect. But according to some new work from researchers at the Universidad Autónoma de Madrid,as well as other schools in Spain, the US, and Australia, once an AI becomes "superintelligent"--think Ex Machina--it will be impossible to contain it. Well, the researchers use the word "incomputable" in their paper, posted on the ArXiv preprint server, which in the world of theoretical computer science is perhaps even more damning. The crux of the matter is the "halting problem" devised by Alan Turing, which holds that no algorithm is able to correctly predict whether another algorithm will run forever or whether it will eventually halt--that is, stop running.

Daniel Dennett (1942 –) is an American philosopher, writer and cognitive scientist whose research is in the philosophy of mind, philosophy of science and philosophy of biology, particularly as those fields relate to evolutionary biology and cognitive science. He is currently the Co-director of the Center for Cognitive Studies, the Austin B. Fletcher Professor of Philosophy, and a University Professor at Tufts University. He received his PhD from Oxford University in 1965 where he studied under the eminent philosopher Gilbert Ryle. In his book, DARWIN'S DANGEROUS IDEA: EVOLUTION AND THE MEANINGS OF LIFE, Dennett present a thought experiment that defends strong artificial intelligence (SAI)--one that matches or exceeds human intelligence.[1] Dennett asks you to suppose that you want to live in the 25th century and the only available technology for that purpose involves putting your body in a cryonic chamber where you will be frozen in a deep coma and later awakened.

In May, a Tesla TSLA 0.39 % "autopilot" enthusiast in Florida became the first known fatality in a self-driving car. But this was no ordinary accident. The car performed exactly as designed, and the (non)driver's failure to take any corrective action could reasonably have been foreseen by the manufacturer. This unwelcome yet widely anticipated milestone may set back progress on what promises to be one of the most valuable technologies of the 21st century. In its rush to get hot new products into consumers' hands, Tesla--along with many other car manufacturers--has pursued a flawed vision of the future, one in which tomorrow's technology is simply layered on top of today's.