We have entered the "Data Decade," says RSA Security* CTO Dr. Zulfikar Ramzan. Here are three "Data Decade" trends that will reshape how we talk about, approach, and manage data. The concept of "data" has been embedded in our lives and across every industry since the beginning of time – in various incarnations and forms. In the security and risk industry, data is the lifeblood for how you assess business risk and respond to threats. As we embark on this new decade, I believe data will become the foremost currency that drives the digital economy.

At a warehouse on the outskirts of Berlin recently, a new addition to the warehouse, a robot, drew press attention. The New York Times called the component-sorting robot "a major advance in artificial intelligence and the ability of machines to perform human labor." A video demo of the robot in action revealed the robot placing various items, with different shapes, in different containers. "As millions of products move through warehouses run by Amazon, Walmart and other retailers, low-wage workers must comb through bin after bin of random stuff--from clothes and shoes to electronic equipment--so that each item can be packaged and sent on its way. Machines had not really been up to the task, until now," said The New York Times.

In 2012, Chicago PD collaborated with the RAND Corporation and the Illinois Institute of Technology to automatically generate "risk scores" for people they arrested, which were supposed to predict the likelihood that the person would be a "party to violence" in the future (this program was called "TRAP" -- Targeted Repeat-Offender Apprehension Program" -- seemingly […]

The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) evaluates algorithms for object detection and image classification at large scale. This is was the benchmark that is used to date to evaluate the performance of the classification model. It also provided the Data to train big models. Apple acquired Siri for an amount reportedly in the vicinity of $200 million. The company immediately started working out how to bake Siri into the iPhone. And Jobs got hands-on about making the AI assistant user-friendly.

WELLINGTON – Mike Moore, who served as New Zealand's prime minister before leading the World Trade Organization during a tumultuous time when thousands protested in Seattle riots, died early Sunday. He died at his home in Auckland, his wife Yvonne Moore said. He had suffered a number of health complications since having a stroke five years ago. Moore was an advocate for both advancing the rights of blue-collar workers and for expanding international trade, a combination which, to some, seemed at odds with itself. Although he had a long political career in New Zealand, Moore's tenure as prime minister was brief: just two months in 1990 before he was defeated in an election.

Alphabet CEO Sundar Pichai said artificial intelligence is "more profound than fire or electricity." Author and historian Yuval Noah Harari said, "If you have enough data about me, enough computing power and biological knowledge, you can hack my body, my brain, my life, and you can understand me better than I understand myself." And a recent Brookings Institution report prophesied that the country or region leading in AI in 2030 will rule the planet until at least 2100. It's clear the AI revolution will impact society drastically, both for good and bad. Recently, using natural-language processing and machine-learning techniques, Canadian company BluDot sifted through global news reports, airline data, and reports of animal disease outbreaks to issue an alert about the current Coronavirus outbreak, days ahead of official organizations such as the World Health Organization.

Managing Marketing is a weekly podcast hosted by TrinityP3. Each one is a conversation with a marketing thought-leader, professional, practitioner or expert on the issues and topics of interest to marketers and business leaders everywhere. In this special series, TrinityP3's Anton Buchner, discusses the rise of Artificial Intelligence and the impact it is having on marketing. Henry Innis is the Chief Strategy Officer and Founder of Mutiny Group, a team of data scientists, engineers and strategists that help put the rigour and measurability back into marketing. He talks about how cloud computing and advances in deep learning models that sit within a neural network now help marketers to look forward and predict results, rather than viewing data as a retrospective exercise. Welcome to Managing Marketing, a weekly podcast where we sit down and talk with marketing thought leaders and experts on the issues and topics of interest to marketers and business leaders everywhere. To discuss this I'm sitting down today with Henry Innes. Henry is the chief strategy officer and founder of Mutiny. Now before we jump in I know your background a little bit. We met I think first when you were at Edge. It was probably my first advertising job. You've been an angel investor advisor. You've been through a couple of different agencies, VML, YNR. I think you were on the STW High Performers Programme--hotshot--years ago.

Patrícia Alves-Oliveira is an upcoming postdoctoral researcher working with Professor Maya Cakmak in the Human-Centered Robotics Lab at the University of Washington. She recently completed her PhD in Human-Robot Interaction at the Lisbon University Institute supervised by Profs. Ana Paiva and Patrícia Arriaga, also working as a visiting scholar in the Human-Robot Collaboration and Companionship Laboratory at Cornell University, supervised by Professor Guy Hoffman. Patrícia's research focuses on the use of social robots as intervention tools to enrich creative behaviors in children. Patrícia's overarching goal as a Human-Robot Interaction researcher is to investigate how and where robots can be used to empower innate human qualities and experiences.

In our latest work, "Detection of anemia from retinal fundus images via deep learning" published in "Nature Biomedical Engineering" we find that a deep learning model can quantify hemoglobin using de-identified photographs of the back of the eye and common metadata (e.g. Compared to just using metadata, deep learning improved the detection of anemia (as measured using the AUC), from 74 percent to 88 percent. To ensure these promising findings were not the result of chance or false correlations, other scientists helped to validate the model--which was initially developed on a dataset of primarily Caucasian ancestry--on a separate dataset from Asia. The performance of the model was similar on both datasets, suggesting the model could be useful in a variety of settings.

As of the beginning of January 2020, it's my great pleasure to join The Machine Learning Times as editor in chief! I've taken over the main editorial duties from Eric Siegel, who founded the ML Times (also the founder of the Predictive Analytics World conference series). As you've likely noticed, we've renamed to The Machine Learning Times what until recently was The Predictive Analytics Times. In addition to a new, shiny name, this rebranding corresponds with new efforts to expand and intensify our breadth of coverage. One particular area of focus will be to increase our coverage of deep learning.