An overemphasis on logic often thwarts the efforts of enthusiastic but naive leaders who are trying to build a data-driven culture; however, an underemphasis on logic can be just as damaging to success. Aristotle's rhetorical triangle teaches us that persuasion comes in three forms: ethos (credibility), pathos (emotion), and logos (logic). Although I encourage you to focus heavily on ethos and pathos, without logos, your organization may have a tough time making the transformation. Data-driven concepts must make logical sense before your organization fully adopts them. SEE: SAP's new big data service can give you a big shortcut to the mother lode of customer insights To build a logical foundation for your data-driven culture, you must answer the question, "why is it better to trust a data system over my own judgment?"

Last month, law firm Baker & Hostetler announced that it would employ IBM's artificially intelligent lawyer, Ross, to help ease its tedious workload. In a statement, the firm's chief technology officer said, "we believe that emerging technologies like cognitive computing and other forms of machine learning can help enhance the services we deliver to our clients." Ross, a system built on the back of IBM's Watson, claims to be able to interpret questions lawyers ask it, and read "through the entire body of law and returns a cited answer and topical readings from legislation, case law and secondary sources to get you up-to-speed quickly." But the first thing I noticed about Ross wasn't how many legal documents it can search at once, or how accurate it claims to be. It was the name: Ross.

Identification of patients with overt cardiorespiratory insufficiency or at high risk of impending cardiorespiratory insufficiency is often difficult outside the venue of directly observed patients in highly staffed areas of the hospital, such as the operating room, intensive care unit (ICU) or emergency department. And even in these care locations, identification of cardiorespiratory insufficiency early or predicting its development beforehand is often challenging. The clinical literature has historically prized early recognition of cardiorespiratory insufficiency and its prompt correction as being valuable at minimizing patient morbidity and mortality while simultaneously reducing healthcare costs. Recent data support the statement that integrated monitoring systems that create derived fused parameters of stability or instability using machine learning algorithms, accurately identify cardiorespiratory insufficiency and can predict their occurrence. In this overview, we describe integrated monitoring systems based on established machine learning analysis using various established tools, including artificial neural networks, k?nearest neighbor, support vector machine, random forest classifier and others on routinely acquired non?invasive and invasive hemodynamic measures to identify cardiorespiratory insufficiency and display them in real?time with a high degree of precision.

It's not a bad time to be a Data Scientist. Serious people may find interest in you if you turn the conversation towards "Big Data", and the rest of the party crowd will be intrigued when you mention "Artificial Intelligence" and "Machine Learning". Even Google thinks you're not bad, and that you're getting even better. There are a lot of'smart' algorithms that help data scientists do their wizardry. It may all seem complicated, but if we understand and organize algorithms a bit, it's not even that hard to find and apply the one that we need. Courses on data mining or machine learning will usually start with clustering, because it is both simple and useful. It is an important part of a somewhat wider area of Unsupervised Learning, where the data we want to describe is not labeled.

Built-in search, artificial intelligence, machine learning and a knowledge graph connecting billions of entities is how Google plans to ultimately compete and win in many markets where it isn't first today. It's easy to note the me-too items outlined at Google I/O. Android N has a few new features, but doesn't advance the ball that much. Mobile platforms have hit the service pack, incremental update mode. Google Home is Google's answer to Amazon's Echo.

Recently we walked you through on how to train a sentiment analysis classifier for hotel reviews using Scrapy and MonkeyLearn. This tutorial is a perfect example on how we can combine web scraped data and machine learning for discovering valuable insights about a particular industry. With this model we were able to analyze millions of reviews and understand if guests love or hate different hotels. But besides understanding the sentiment of a review, wouldn't be interesting to understand what particular aspects do the guests love or hate about a particular hotel? This post will cover how you can create a machine learning classifier to understand the different aspects of hotel reviews.

CTO Corner is BITS's monthly publication covering emerging trends and technologies in the financial services industry. Artificial Intelligence (AI), defined as the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages, has been around for over 60 years.1 In his 1950 paper "Computing Machinery and Intelligence," Alan Turing opens with: "I propose to consider the question'can machines think?'"2 He proposed a test of a machine's ability to exhibit intelligent behavior, equivalent to, or indistinguishable from, that of a human being, which is now known as the Turing Test.3 AI as an academic discipline began at the famous 1955 Dartmouth conference organized by John McCarthy from Stanford University and Marvin Minsky from MIT.4 This CTO Corner explores both the potential for AI to transform the financial services industry and challenges it presents.

MindMeld, a pioneer in conversational AI technology, is today releasing the MindMeld for TV platform, which the company bills as the first solution designed for television and over-the-top content providers -- like Netflix and Hulu -- to deliver best in class discovery through a conversational interface. That company boasts investors and customers like Google, Samsung, Intel, Telefonica, IDG, Spotify, and other major global brands. Since MindMeld provides data services and the underlying tech-for-voice interface, the company couldn't explain exactly which products in market are 100 percent powered by their technology. But the company's voice platform is fast, alarmingly accurate, and likely baked into tons of products we interact with in some form or another. MindMeld was founded in 2011 and is backed by over 15 million in funding.

In December 2015 Leman Solicitors hosted the Future of Law event for 100 corporate counsel. We spoke about IBM's Watson which is being used to build A.I. applications in several sectors, notably ROSS in law. It's part of a disruption that we say is going to fundamentally change the way legal services are delivered. Another article in today's Business Insider UK reports: "Ask ROSS to look up an obscure court ruling from 13 years ago, and ROSS will not only search for the case in an instant -- without contest or complaint -- but it'll offer opinions in plain language about the old ruling's relevance to the case at hand." The article can be found here.

Google Inc.'s latest technological marvels point to a future where you'll never need to visit websites, write a term paper or stress over what to buy for your mother's birthday. For consumers seeking convenience and speed, it all sounds great. But how will any of it make Google money? The Mountain View, Calif., company's apparent transition from go-to search engine to omnipresent virtual assistant probably will require rethinking its advertising business. By tracking Web browsing, emails, chats and more, Google has become a dominant force in digital ads.