What will be the next thing to revolutionize data science in 2019? Reinforcement learning will be the next big thing in data science in 2019. While RL has been around for a long time in academia, it has hardly seen any industry adoption at all. Why? Partly because there have been plenty of low-hanging fruits to pick in predictive analytics, but mostly because of the barriers in implementation, knowledge and available tools. The potential value in using RL in proactive analytics and AI is enormous, but it also demands a greater skillset to master.

This installment of Research for Practice features a curated selection from Alex Ratner and Chris Ré, who provide an overview of recent developments in Knowledge Base Construction (KBC). While knowledge bases have a long history dating to the expert systems of the 1970s, recent advances in machine learning have led to a knowledge base renaissance, with knowledge bases now powering major product functionality including Google Assistant, Amazon Alexa, Apple Siri, and Wolfram Alpha. Ratner and Re's selections highlight key considerations in the modern KBC process, from interfaces that extract knowledge from domain experts to algorithms and representations that transfer knowledge across tasks.

Virtual assistants like Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri employ conversational experiences and language-understanding technologies to help users accomplish a range of tasks, from reminder creation to home automation. Voice is the primary means of engagement, and voice-activated assistants are growing in popularity; estimates as of June 2017 put the number of monthly active users of voice-based assistant devices in the U.S. at 36 million.a Many are "headless" devices that lack displays. Smart speakers (such as Amazon Echo and Google Home) are among the most popular devices in this category. Speakers are tethered to one location, but there are other settings where voice-activated assistants can be helpful, including automobiles (such as for suggesting convenient locations to help with pending tasks5) and personal audio (such as for providing private notifications and suggestions18).

Self-driving cars are being developed by several major technology companies and carmakers. When a driver slams on the brakes to avoid hitting a pedestrian crossing the road illegally, she is making a moral decision that shifts risk from the pedestrian to the people in the car. Self-driving cars might soon have to make such ethical judgments on their own -- but settling on a universal moral code for the vehicles could be a thorny task, suggests a survey of 2.3 million people from around the world. The largest ever survey of machine ethics1, published today in Nature, finds that many of the moral principles that guide a driver's decisions vary by country. For example, in a scenario in which some combination of pedestrians and passengers will die in a collision, people from relatively prosperous countries with strong institutions were less likely to spare a pedestrian who stepped into traffic illegally.

A small metal droplet can propel a wheeled robot forward with a simple electric current. The technique paves the way for larger robots that can trundle like tumbleweeds through unfriendly terrain. Shi-Yang Tang at the University of Wollongong in Australia and his colleagues started with a plastic wheel about five centimetres across with walls along its edges, shaped like a car tyre. Inside the wheel they placed a drop of liquid metal made mostly of gallium.

Kremlin spokesman Dmitry Peskov said he could not rule out that President Vladimir Putin would raise the alleged drone attack with U.S. President Donald Trump. The two leaders are expected to meet in Paris on Nov. 11.

IBM has announced AI OpenScale, a service that aims to bring visibility and explainability of AI models for enterprises. When it comes to adopting AI for business use, there are multiple concerns among enterprise customers. Lack of visibility of the model, unwanted bias, interoperability among tools and frameworks, compliance in building and consuming AI models are some of the critical issues with AI. IBM AI OpenScale provides explanations into how AI models are making decisions, and automatically detects and mitigates bias to produce fair, trusted outcomes. It attempts to bring confidence to enterprises by addressing the challenges involved in adopting artificial intelligence.