To understand high precision observations of exoplanets and brown dwarfs, we need detailed and complex general circulation models (GCMs) that incorporate hydrodynamics, chemistry, and radiation. For this study, we specifically examined the coupling between chemistry and radiation in GCMs and compared different methods for the mixing of opacities of different chemical species in the correlated-k assumption, when equilibrium chemistry cannot be assumed. We propose a fast machine learning method based on DeepSets (DS), which effectively combines individual correlated-k opacities (k-tables). We evaluated the DS method alongside other published methods such as adaptive equivalent extinction (AEE) and random overlap with rebinning and resorting (RORR). We integrated these mixing methods into our GCM (expeRT/MITgcm) and assessed their accuracy and performance for the example of the hot Jupiter HD~209458 b. Our findings indicate that the DS method is both accurate and efficient for GCM usage, whereas RORR is too slow. Additionally, we observed that the accuracy of AEE depends on its specific implementation and may introduce numerical issues in achieving radiative transfer solution convergence. We then applied the DS mixing method in a simplified chemical disequilibrium situation, where we modeled the rainout of TiO and VO, and confirmed that the rainout of TiO and VO would hinder the formation of a stratosphere. To further expedite the development of consistent disequilibrium chemistry calculations in GCMs, we provide documentation and code for coupling the DS mixing method with correlated-k radiative transfer solvers. The DS method has been extensively tested to be accurate enough for GCMs; however, other methods might be needed for accelerating atmospheric retrievals.

Electrons and their behavior pose fascinating questions for quantum physicists, and recent innovations in sources, instruments and facilities allow researchers to potentially access even more of the information encoded in quantum materials. However, these research innovations are producing unprecedented--and until now, indecipherable--volumes of data. "The information content in a piece of material can quickly exceed the total information content in the Library of Congress, which is about 20 terabytes," said Eun-Ah Kim, professor of physics in the College of Arts and Sciences, who is at the forefront of both quantum materials research and harnessing the power of machine learning to analyze data from quantum material experiments. "The limited capacity of the traditional mode of analysis--largely manual--is quickly becoming the critical bottleneck," Kim said. A group led by Kim has successfully used a machine learning technique developed with Cornell computer scientists to analyze massive amounts of data from the quantum metal Cd2Re2O7, settling a debate about this particular material and setting the stage for future machine learning aided insight into new phases of mater.

While the words "artificial intelligence" generally conjure up visions of Skynet, HAL 9000, and the Demon Seed, machine learning and other types of AI technology have already been brought to bear on many analytical tasks, doing things that humans can't or don't want to do--from catching malware to predicting when jet engines need repair. As the scale and complexity of storage workloads increase, it becomes more and more difficult to manage them efficiently. Jobs that could originally be planned and managed by a single storage architect now require increasingly large teams of specialists--which sets the stage for artificial intelligence (née machine learning) techniques to enter the picture, allowing fewer storage engineers to effectively manage larger and more diverse workloads. Storage administrators have five major metrics they contend with, and finding a balance among them to match application demands approaches being a dark art. Throughput: Throughput is the most commonly understood metric at the consumer level.

The exploitation of phase-change materials (PCMs) in diverse technological applications can be greatly aided by a better understanding of the microscopic origins of their functional properties. Over the last decade, simulations based on electronic-structure calculations within density functional theory (DFT) have provided useful insights into the properties of PCMs. However, large simulation cells and long simulation times beyond the reach of DFT simulations are needed to address several key issues of relevance for the performance of devices. One way to overcome the limitations of DFT methods is to use machine learning (ML) techniques to build interatomic potentials for fast molecular dynamics simulations that still retain a quasi-ab initio accuracy. Here, we review the insights gained on the functional properties of the prototypical PCM GeTe by harnessing such interatomic potentials.

The Transportation Security Administration is looking to Silicon Valley startups to help it bring machine learning to security screening to improve the accuracy of airport baggage scanners. Through an Other Transaction Solicitation, the Department of Homeland Security's Science & Technology Directorate and TSA's Office of Requirements and Capabilities Analysis are looking for a new way to detect evolving threats carried in airline passenger luggage. Rapidly changing consumer electronics, the RFI said, are an example of a dynamic threat vector that evolves faster than next-generation detector hardware. TSA personnel looking at baggage scanner images might miss subtle new differences in how newly introduced consumer devices are wired or put together. The agency wants developers to come up with AI-based methods that could automate detection algorithm training, allowing detection hardware to "intuitively recognize" such subtleties and new objects that come through airports in luggage.

Opportunities to expand the use of machine learning in payments range from using Web-sourced data to more accurately predict borrower delinquency to using virtual assistants to improve customer service. Machine learning is one of many tools in the advanced analytics toolbox, one with a long history in the worlds of academia and supercomputing. Recent developments, however, are opening the doors to its broad-scale applicability. Companies, institutions, and governments now capture vast amounts of data as consumer interactions and transactions increasingly go digital. At the same time, high-performance computing is becoming more affordable and widely accessible.

Machine learning is no longer the stuff of science fiction, nor is it all that new. Its development dates back to the mid-20th century and was defined in 1959 by Arthur Samuel as a "field of study that gives computers the ability to learn without being explicitly programmed". As the modern world becomes increasingly dependent on data-driven technologies, machine learning, along with artificial intelligence (AI), has captured the human imagination. It is clear that businesses are spending huge amounts of time and money scrambling to adopt the latest and greatest technologies in the hope of out-pacing and out-smarting their rivals. However, without a customer-centric, business-relevant big data strategy that is embraced company-wide, all the technology in the world won't sell a thing.