Since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. In the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. The complexity of such big data repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. Canonical correlation analysis (CCA) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. This doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. Our primer discusses the rationale, promises, and pitfalls of CCA in biomedicine.

SAN FRANCISCO: There's a dirty little secret about artificial intelligence: It's powered by hundreds of thousands of real people. From makeup artists in Venezuela to women in conservative parts of India, people around the world are doing the digital equivalent of needlework _drawing boxes around cars in street photos, tagging images, and transcribing snatches of speech that computers can't quite make out. Such data feeds directly into machine learning'' algorithms that help self-driving cars wind through traffic and let Alexa figure out that you want the lights on. These repetitive tasks pay pennies apiece. But in bulk, this work can offer a decent wage in many parts of the world _ even in the U.S.

Welcome to this user friendly group passionate about data science, machine learning, and artificial intelligence! Whether you are new to the subject or a veteran, our goal is to help you advance in your journey. The goal is to learn and we welcome all questions. We recognize this can be an intimidating subject so this group will try to explain the background concepts and demonstrate application. Big Data will be part of the subject area since we need to be able to analyze data regardless of volume or structure.

As organizations plan their customer strategies they foresee an onslaught of customer interactions coming their way. The fallacy of believing that adding self-service options will decrease customer requests is now known. When your organization opens channels for customer to interact with you, even with self-service options, customers will interact with you more. This engagement is a good thing. But the only way to handle all of your volume – in an effective manner without adding more staff – is by leveraging Artificial Intelligence. Listening to John Pisarek of Interactions at Call Center Week Winter the scenario of about projecting more customer interaction volume and not getting additional staff to handle it is a common reality for many contact center leaders.

Mike Iacobucci wants to help you stop screaming at customer service agents--specifically, the automated ones that misunderstand what you say or require an endless series of button pushes to complete a simple task. Iacobucci is president and CEO of Interactions, a company that sells a new kind of interactive voice-response software for customer-care phone systems. With its software, instead of having to go through an interminable series of push-button choices or stick to overly simple verbal commands, you can talk just as you would to a human representative--and, surprisingly, it actually works. Interactions' software is, hopefully, more than a solution to impossibly annoying automated support systems. Rather than relying entirely on software to handle calls, Interactions automatically hands speech that its software can't cope with over to human agents, who select an appropriate response.