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Genevera I. Allen (left) is professor in the Departments of Statistics, and the Electrical and Computer Engineering, at Rice University. Corinne Cath (right) is a doctoral student at the Alan Turing Institute, the national institute for data science in UK. Below are extracts of recent interviews that are most relevant to our audience. Links to full interviews are provided. Genevera, what do you think of the shift from "Statistics" to "Statistical Learning and Data Science" in the statistics community (The "Data vs Math" Question?)

Some people find it easy to engage in physical activities but research found that America is not as active as it seems to be. Mastering an active lifestyle is about good time management and developing physical exercise routines. A gym subscription, a workout buddy, a personal fitness trainer or weekly runs through the park can all contribute to a healthy exercise routine. According to the participation report released in 2015 by the Physical Activity Council, 25% of Americans live a sedentary lifestyle. Doctors are warning about the effects of physical inactivity which increases the risk for high blood pressure, cardiovascular disease, certain cancers, and obesity, while also putting a person at risk of anxiety and depression.

Researchers from OpenAI and DeepMind are hoping to make artificial intelligence safer using a new algorithm that learns from human feedback. Both companies are experts in reinforcement learning – an area of machine learning that rewards agents if they take the right actions to complete a task under a given environment. The goal is specified through an algorithm, and the agent is programmed to chase the reward, like winning points in a game. Reinforcement learning has been successful in teaching machines how to play games like Doom or Pong or drive autonomous cars via simulation. It's a powerful method to explore an agent's behavior, but it can be dangerous if the hard-coded algorithm is wrong or produces undesirable effects.

WASHINGTON – The United States appears poised to heighten scrutiny of Chinese investment in Silicon Valley to better shield sensitive technologies seen as vital to U.S. national security, current and former U.S. officials have told Reuters. Of particular concern is China's interest in fields such as artificial intelligence and machine learning, which have increasingly attracted Chinese capital in recent years. The worry is that cutting-edge technologies developed in the United States could be used by China to bolster its military capabilities, and perhaps even push it ahead in strategic industries. The U.S. government is now looking to strengthen the role of the Committee on Foreign Investment in the United States (CFIUS), the inter-agency committee that reviews foreign acquisitions of U.S. companies on national security grounds. An unreleased Pentagon report, viewed by Reuters, warns that China is skirting U.S. oversight and gaining access to sensitive technology through transactions that currently don't trigger CFIUS reviews. Such deals would include joint ventures, minority stakes and early-stage investments in start-ups.

Our ability to spot social signals may have changed the way we see colour, new research has found. The light receptors in our eyes are well-adapted to spot key social colour-changes, such as a blushing or angry face. These emotions can change facial blood-flow, forcing a subtle colour change that we have evolved to spot - an adaptation that allows humans to predict behaviour. The researchers found that human eyes are so good at spotting these colour changes that they can accurately read the facial mating signals of a female macaque monkey better than a digital camera. Our ability to spot social signals may have changed the way we see colour, new research has found.

New technologies and advanced analytics have the potential to create an exceptional customer experience. Unfortunately, most banking organizations continue to use the same outdated, siloed processes. Understanding the customer journey is at the foundation of being able to deliver the type of experience expected by today's consumer. Disconnects occur when organizations are unable to link online and offline customer engagements, and when internal silos create communications that don't reflect a customer's needs and behaviors in real-time. The New Marketing Reality report, produced by Econsultancy in association with IBM Watson Marketing, is based on a survey of more than 1,000 marketing, digital and ecommerce professionals.

This suicide risk-predicting algorithm could quite literally save lives. Sometimes people experiencing suicidal thoughts may alert others in a cry for help. In other instances, suicides can offer tragically few tell-tale clues that would have allowed friends, family, or other individuals to act differently. That is where a new piece of research from Vanderbilt University Medical Center comes into play. Researchers there have developed a machine learning algorithm designed to predict the likelihood of an individual attempting suicide.

Many efficient algorithms with strong theoretical guarantees have been proposed for the contextual multi-armed bandit problem. However, applying these algorithms in practice can be difficult because they require domain expertise to build appropriate features and to tune their parameters. We propose a new method for the contextual bandit problem that is simple, practical, and can be applied with little or no domain expertise. Our algorithm relies on decision trees to model the context-reward relationship. Decision trees are non-parametric, interpretable, and work well without hand-crafted features. To guide the exploration-exploitation trade-off, we use a bootstrapping approach which abstracts Thompson sampling to non-Bayesian settings. We also discuss several computational heuristics and demonstrate the performance of our method on several datasets.