Synthetic test function in two dimensions that is continuous and also smooth. The "data deluge" in scientific research stems in large part from the growing sophistication of experimental instrumentation and optimizing tools -- often using machine- and deep-learning methods -- to analyze increasingly large data sets. But what is equally important for improving scientific productivity is the optimization of data collection -- aka "data taking" -- methods. Toward this end, Marcus Noack, a postdoctoral scholar at Lawrence Berkeley National Laboratory in the Center for Advanced Mathematics for Energy Research Applications (CAMERA), and James Sethian, director of CAMERA and Professor of Mathematics at UC Berkeley, have been working with beamline scientists at Brookhaven National Laboratory to develop and test SMART (Surrogate Model Autonomous Experiment), a mathematical method that enables autonomous experimental decision making without human interaction. A paper describing SMART and its application in experiments at Brookhaven's National Synchrotron Light Source II (NSLS-II) are described in "A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering," published in Scientific Reports.

EXECUTIVE SUMMARY With the growing number of energy regulations and certifications, the demand to reduce energy consumption has become an important aspect of building management. Advances in the internet of things (IoT) technologies and smart building strategies are playing a huge role in helping change the industry through a shift from energy management to enterprise management systems. Technologies like machine learning and artificial intelligence (AI) are making it possible for a smart building to forecast, predict and optimize its operations and increase energy efficiencies.

Enterprise seems to be entering a new era ruled by data. What was once the realm of science fiction, AI in business intelligence is evolving into everyday business as we know it. Companies can now use machines algorithms to identify trends and insights in vast reams of data and make faster decisions that potentially position them to be competitive in real-time. It's not a simple process for companies to incorporate machine learning into their existing business intelligence systems, though Skymind CEO and past Emerj podcast guest Chris Nicholson advises that it doesn't have to be daunting. "AI is just a box," he says.

The development and adoption of advanced technologies including smart automation and artificial intelligence has the potential not only to raise productivity and GDP growth but also to improve well-being more broadly, including through healthier life and longevity and more leisure. Alongside such benefits, these technologies also have the potential to reduce disruption and the potentially destabilizing effects on society arising from their adoption. Tech for Good: Smoothing disruption, improving well-being (PDF–1MB) examines the factors that can help society achieve such benefits and makes a first attempt to calculate the impact of technology adoption on welfare growth beyond GDP. Our modeling suggests that good outcomes for the economy overall and for individual well-being come about when technology adoption is focused on innovation-led growth rather than purely on labor reduction and cost savings through automation. This needs to be accompanied by proactive transition management that increases labor market fluidity and equips workers with new skills. Technology for centuries has both excited the human imagination and prompted fears about its effects. Today's technology cycle is no different, provoking a broad spectrum of hopes and fears. Opinion surveys suggest people tend to have a nuanced view of technology but nonetheless worry about the risks: while generally positive about longer-term benefits, especially for health, many are also concerned about the negative impact on their lives, in particular in the areas of job security, material living standards, safety, and trust.

Damage due to earthquakes poses a threat to humans worldwide. To estimate the hazard, scientists use historical earthquake data and ground motion recorded by seismometers at different locations. However, the current approaches are mostly empirical and may not capture the full range of ground shaking in future large earthquakes due to a lack of historical geological data. This leads to significant uncertainties in hazard estimates. Not only that, due to the lack of sufficient historical data, scientists mostly rely on simulated data, which is computationally expensive.

In today's multifaceted energy world, a growing number of prosumer assets are increasing the complexity of power grids. This is even more important in an ever-changing climate that more and more generates huge storms such as the Typhoon Lekima which caused 9.3 Billion in damage (5th Costliest known Pacific typhoons) and more than 90 deaths in the Philippines, Taiwan and China earlier this year, or the recent monstrous Category 5 Hurricane Dorian in the Atlantic Ocean. The director-general of the Bahamas Ministry of Tourism and Aviation, Joy Jibrilu, details the damage left in the aftermath from Hurricane Dorian and what the Bahamas will need to move forward especially on the infrastructures. This looks too similar to what we've seen in Porto Rico two years ago which suffered severe damage from the category 5 hurricane Maria. The blackout as a result of Maria has been identified as the largest in US history and the second-largest in world history.

A scammer used deepfake technology to swindle a U.K. energy company out of $243,000. By mimicking the voice of the CEO of the energy firm's parent company, the person behind the deepfake swindling convinced the energy firm's CEO to transfer the money within the hour, according to The Wall Street Journal. It's a troubling vision of the future -- in which digital misinformation becomes so sophisticated that even a phone calls from your boss becomes suspicious. The crime was reported to investigators by the energy firm's insurance company, Euler Hermes Group SA, which declined to name either of the companies involved, the WSJ reports. So far, no one has identified a suspect, but the money that the energy CEO sent to a Hungarian account has since been traced to Mexico, and from there it was more widely distributed.

Organic electrosynthesis can transform the chemical industry by introducing electricity-driven processes that are more energy efficient and that can be easily integrated with renewable energy sources. However, their deployment is severely hindered by the difficulties of controlling selectivity and achieving a large energy conversion efficiency at high current density due to the low solubility of organic reactants in practical electrolytes. This control can be improved by carefully balancing the mass transport processes and electrocatalytic reaction rates at the electrode diffusion layer through pulsed electrochemical methods. In this study, we explore these methods in the context of the electrosynthesis of adiponitrile (ADN), the largest organic electrochemical process in industry. Systematically exploring voltage pulses in the timescale between 5 and 150 ms led to a 20% increase in production of ADN and a 250% increase in relative selectivity with respect to the state-of-the-art constant voltage process.

We present a systematic framework for the Nesterov's accelerated gradient flows in the spaces of probabilities embedded with information metrics. Here two metrics are considered, including both the Fisher-Rao metric and the Wasserstein-$2$ metric. For the Wasserstein-$2$ metric case, we prove the convergence properties of the accelerated gradient flows, and introduce their formulations in Gaussian families. Furthermore, we propose a practical discrete-time algorithm in particle implementations with an adaptive restart technique. We formulate a novel bandwidth selection method, which learns the Wasserstein-$2$ gradient direction from Brownian-motion samples. Experimental results including Bayesian inference show the strength of the current method compared with the state-of-the-art.

The business applications of artificial intelligence (AI) have been all over the news. Industries from manufacturing to insurance are implementing ways to utilize artificial intelligence, sometimes alongside other emerging technology like machine learning. In addition to businesses, AI can have a significant impact on real-world social challenges and the potential to bring valuable solutions to various societal issues. Fifteen members of Forbes Technology Council weigh in on how innovative applications of AI can be used to combat some of the seemingly unsolvable social crises facing the world today. AI could be used to transform and improve wildlife conservation.