Research shows that machine learning algorithms may learn and incorporate bias into their decision-making processes. They help make better decisions, in part, because of the large amounts of data that can be extracted – from content provided around decisions that were made in the past to the outcomes that resulted. Each transaction provides a historical map from which better decisions can be made. But the truth is, when it comes to people at work and many human capital decisions, there is no cookie cutter approach. And those past decisions may not form the best map to read from when informing future outcomes.

This week, Forrester Research, Inc. named Tamr a Strong Performer in "The Forrester Wave: Data Quality Solutions, Q4 2015" report. The report evaluated 13 vendors based on 30 criteria, including current offering, strategy and market presence. In addition to receiving the highest scores in the validation, cleansing and standardization, as well as the cloud, criteria, Tamr scored a 4.00 out of a possible 5.00 in data governance and stewardship and in data link, match and survivorship. Tamr also received the highest score possible (5.00) for product strategy. We believe that for a young company like Tamr to be named a Strong Performer in "The Forrester Wave: Data Quality Solutions, Q4 2015" report, it takes unnatural amounts of work by a team that, like ours, is fully on the same page.

"Two recent accomplishments are near and dear to my heart. First, I've worked on role discovery in complex networks. A complex network is an ecosystem with the nodes (i.e., entities) playing multiple functional roles over time. Automatically discovering the underlying roles of nodes in a network is a challenging problem. Given a network, intuitively two nodes belong to the same role (or behavioral cluster) if they have similar structural behavior. Examples of roles that can be automatically determined from data include'broker,' 'clique-member,' 'periphery-node,' etc. Roles enable numerous novel and useful tasks, such as transfer learning, sense making, and anomaly detection. "In 2012, together with colleagues at LLNL and CMU, I introduced RolX (short for Role eXtraction).

Really like the green surface - Love the 4 legged creatures - Disruptive Sales Model for a new era - Tweets are my own.

Many of these titles probably sound familiar to you. For a long time now, the concept of artificial intelligence has provided the masses with novels and blockbuster movies of science fiction, drama, comedy, and even unexpected stories of friendship. Hollywood and all of its fans have enjoyed these stories over the years – appreciating them for what they are: Entertainment. Today, artificial intelligence (AI) has a very different meaning. In fact, as AI has moved from the silver screen to the screens of modern computers used by virtually every segment of society, it has a remarkably different purpose.

Continuous mobile vision is limited by the inability to efficiently capture image frames and process vision features. This is largely due to the energy burden of analog readout circuitry, data traffic, and intensive computation. To promote efficiency, we shift early vision processing into the analog domain. This results in RedEye, an analog convolutional image sensor that performs layers of a convolutional neural network in the analog domain before quantization. We design RedEye to mitigate analog design complexity, using a modular column-parallel design to promote physical design reuse and algorithmic cyclic reuse.

These days, sensors are everywhere: Phones know where their users have been, what music they've listened to and which events they've agreed to attend. And while the data that results is a powerful tool for businesses, they aren't always connected to material generated on laptops, tablets and wearable devices. Maybe they should be, because, already, 70 percent of Americans own two or more connected devices. A percentage like that tells us that the days of one-screen users are numbered: Already, 88 percent of millennials are engaging in second-screen behaviors while watching videos online. Clearly, businesses must keep up with the times.

Since becoming actively involved with the artificial intelligence ecosystem in early 2015, Facebook Inc. has made numerous contributions ranging from niche software modules to entire server blueprints. The social networking giant expanded its repertoire yet again this week by open-sourcing a toolkit called Torchnet that provides building blocks for deep learning projects. As the name implies, it's designed for use with Torch, a popular AI development framework that has been adopted by several of Facebook's engineering teams. Torchnet's main selling point is a set of five programming abstractions meant to common tasks involved in implementing deep learning functionality. One module provides logic for training models and testing their accuracy, while another helps assess the results.

Starting this fall when iOS 10 and macOS Sierra are launched, there will be (optional) changes to Apple's privacy policy. The company has finally acknowledged publicly that it needs to collect at least some types of information if it wants to advance with its artificial intelligence ambitions. The change was announced by Apple SVP Craig Federighi, who said that the company will collect information in a different way to before, as it seeks to improve the ability of Siri and the iPhone to predict the information the user wants. Federighi touted this approach as differential privacy. Wired published an extensive piece about what differential privacy means and how Apple plans to implement it.

From Google to Amazon to Apple, software giants are integrating voice commands, search and a whole lot of data to makes their digital voices not just smarter, but more human. The promise of voice-activated AI is exciting, but in entirely digitizing our interactions we run the risk of losing real conversation. MIT's Sherry Turkle tackles the issue in her book, "Reclaiming Conversation." By retreating to our devices, she argues, we forget what it means to be human. And the hiring of poets and novelists to craft "characters" for these emerging AIs points to the fact that consumers clearly value the human element.