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These scenarios describe two predictions in what will be an algorithmic and smart machine driven world where people and machines must define harmonious relationships.

Google Search itself provides one of the most familiar examples of predictive intelligence. When you enter keywords in the search box, Google predicts what you are interested in and then presents you with results that match that intent. Since it released the first version of its Prediction API in 2010, Google has made some of these methods available to developers. Adoption among developers has not been high because machine learning requires a lot of infrastructure and validation to produce accurate results. Developers have also reported discomfort with basing products on black box APIs.

"Visual displays are extremely powerful tools, which means manipulation of these tools can spread inaccurate information and influence public perception. The sheer volume of images making their way through the Internet requires viewers to have a higher level of visual literacy than in years before in order to prevent manipulation. In this article you'll learn The types of displays that can be misleading What can be done to make visual displays less misleading"

Good managers-even great ones-can make spectacularly bad choices. Some of them result from bad luck or poor timing, but a large body of research suggests that many are caused by cognitive and behavioral biases.

How should statisticians be communicating with the media? This meeting explores the relationship between statisticians, journalists and the public, and the statistician's role in providing expert statistical comments on media stories. Speakers present their experiences of working with the media, reflecting on the challenges of communicating statistical ideas for non-technical audiences, whilst preserving the integrity of the story

Humans are biased, and the biases we encode into machines are then scaled and automated. This is not inherently bad (or good), but it raises the question: how do we operate in a world increasingly consumed with "personal analytics" that can predict race, religion, gender, age, sexual orientation, health status and much more.

IBM's Watson - the future of machine learning

It's a BIG presentation - takes a while to download and open (doesn't seem to work in Chrome at all)

New McKinsey research shows that a payday could be arriving faster than expected. A new class of company is emerging-one that uses collaborative Web 2.0 technologies intensively to connect the internal efforts of employees and to extend the organization's reach to customers, partners, and suppliers. We call this new kind of company the networked enterprise. Results from our analysis of proprietary survey data show that the Web 2.0 use of these companies is significantly improving their reported performance.

A quick trip through time to examine the origins of machine learning as well as the most recent milestones.

The Washington Post's stats on every fatal police shooting this year, over 900 so far.

The Center for Evidence-Based Management (CEBMa) is a non-profit member organization dedicated to promoting evidence-based practice in the field of management. We provide support and resources to managers, consultants, organizations, teachers, academics and others interested in learning more about evidence-based management.