Now that the HomePod is nearly here, Apple is dribbling out details of what its first smart speaker will do... including, apparently, that it's a power miser. The company has posted environmental data showing that the HomePod uses no more than 9.25W of power when playing music at 50 percent volume. As MacRumors noted, that's less than the consumption of a typical LED light bulb (such as the 10W of a Philips Hue A19). You're going to use more power if you crank it up, of course, but you probably won't cringe at your electricity bill if you stream music all day. And importantly, the speaker should consume little power when it's silent.

The Consumer Technology Association (CTA) and financial firm Deloitte have made a major investment at this year's Consumer Electronics Show to highlight smart cities technology and encourage attendees to explore the solutions presented by this emerging sector. Companies that are pioneering new solutions include major brands like Bosch, Ford, Nissan, and Ericsson. They and others will be on hand to explore new opportunities in smart transportation, smart grids, public safety, cybersecurity, health care, artificial intelligence, virtual reality (VR) and augmented reality (AR), and more. This year, more than 11,000 square feet of the show, which is held annually in Las Vegas, will be dedicated to The Smart Cities Marketplace, presented by the CTA, the industry's trade organization, and Deloitte. It will feature smart city-enabling technology and solutions.

Artificial intelligence achieved prominence in 2017 as companies looked to automate how they derive value from data. Following years focused on collecting data, where Hadoop and big data management dominated the conversation, organizations are now turning their attention to machine learning and other forms of AI to better extract meaning for that data and to open up new business models, products, and services. For perspective, 451 Research expects the total data market to reach nearly $140 billion in 2021. Couple that with a rapid rise in job creation for AI skills and you have the telltale signs of big opportunities. Recently, the AI Index report from Stanford noted that the share of jobs requiring AI skills in the U.S. has grown four and a half times since 2013. To put data to use with a focus on AI, organizations should recognize that organizational change is as important as technological change.

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide you a one-stop source of late-breaking news to help you keep abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Technology is getting smarter, faster. Experts including the authors of The Second Machine Age, Erik Brynjolfsson and Andrew McAfee, examine the impact that "thinking" machines may have on top-management roles.

This nascent AI technique – which requires no input data, substantially less computing power, and in which the evolutionary-like AI learns from itself – could soon evolve to enable its application to real-world problems in the natural sciences. Collaboration with Earth scientists to identify the systems – from climate science, materials science, biology, and other areas – which can be codified to apply reinforcement learning for scientific progress and discovery is vital. For example, DeepMind co-founder, Demis Hassabis, has suggested that in materials science, a descendant of AlphaGo Zero could be used to search for a room temperature superconductor – a hypothetical substance that allows for incredibly efficient energy systems.

In this transcript from an interview conducted by insideHPC, Mike Bernhardt discusses the CANDLE project for cancer research with Rick Stevens from Argonne National Lab. The CANcer Distributed Learning Environment (CANDLE) is an ECP application development project targeting new computational methods for cancer treatment with precision medicine. What is CANDLE all about? It has to do with building a scalable deep-learning environment that can be applied to a variety of problems in cancer, initially. CANDLE is designed to run on the big machines that we have at the US Department of Energy (DOE). The goal is to have an easy-to-use environment that can take advantage of the full power of these big systems to search through large combinations of deep-learning models to find optimal models for making predictions in cancer.

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This paper addresses the optimal control problem known as the Linear Quadratic Regulator in the case when the dynamics are unknown. We propose a multi-stage procedure, called Coarse-ID control, that estimates a model from a few experimental trials, estimates the error in that model with respect to the truth, and then designs a controller using both the model and uncertainty estimate. Our technique uses contemporary tools from random matrix theory to bound the error in the estimation procedure. We also employ a recently developed approach to control synthesis called System Level Synthesis that enables robust control design by solving a convex optimization problem. We provide end-to-end bounds on the relative error in control cost that are nearly optimal in the number of parameters and that highlight salient properties of the system to be controlled such as closed-loop sensitivity and optimal control magnitude. We show experimentally that the Coarse-ID approach enables efficient computation of a stabilizing controller in regimes where simple control schemes that do not take the model uncertainty into account fail to stabilize the true system.

John Zysman (Zysman.john@gmail.com) is a Professor Emeritus in the Department of Political Science, University of California, Berkeley, Berkeley, CA, cofounder of the Berkeley Roundtable on the International Economy, and convener of the Berkeley Project Work in an Era of Intelligent Tools and Systems. Martin Kenney (mfkenney@ucdavis.edu) is Distinguished Professor of Human and Community Development at the University of California, Davis, and Senior Project Director at the Berkeley Roundtable on the International Economy; he is also an Affiliated Faculty at Instituto di Management at the Scuola Superiore Sant'Anna, Pisa, Italy.