It is possible to monitor Sun's extreme ultraviolet (EUV) irradiance using deep learning. Scientists at the NASA Frontier Development Lab (FDL) have shown that deep learning can help get more value out of our current ability to monitor the Sun by providing virtual instruments to supplement physical devices. The Sun is vital for survival, yet solar flares, which typically occurs a few times a year, can cause severe interruptions in space and on Earth. These interruptions can affect rocket, satellites, and even frameworks here on Earth, including GPS route, radio interchanges, and the power grid. FDL team member and co-author Alexander Szenicer (Oxford University) said, "Our research shows how a deep neural network can be trained to mimic an instrument on the Solar Dynamics Observatory (SDO). By inferring what ultraviolet radiation levels that sensor would have detected based on what the other instruments on SDO are observing at any given time, we demonstrate it is possible to increase the scientific productivity of NASA missions and to increase our capability to monitor solar sources of space weather."

The National Microbiome Data Collaborative (NMDC), a new initiative aimed at empowering microbiome research, is gearing up its pilot phase after receiving $10 million from the U.S. Department of Energy (DOE) Office of Science. Spearheaded by Lawrence Berkeley National Laboratory (Berkeley Lab), in partnership with Los Alamos (LANL), Oak Ridge (ORNL), and Pacific Northwest (PNNL) national laboratories, the NMDC will leverage DOE's existing data-science resources and high-performance computing systems to develop a framework that facilitates more efficient use of microbiome data for applications in energy, environment, health, and agriculture. Nearly every ecosystem and organism on Earth hosts a diverse community of microorganisms – its microbiome. Yet we know little about the functions of individual microbes, let alone how they interact with each other, their hosts, or their environments, and how their activity varies over time or in response to perturbations. The past decade has seen tremendous advances in genome and metagenome DNA-sequencing technologies, which has led to an unprecedented volume of microbiome data being generated.

Nowadays artificial intelligence (AI) and machine learning are impacting our daily lives in many different ways. They help businesses make decisions and optimize operations for some of the world's leading companies. As a result, there will be a huge change in jobs and employment in the future. These are practical examples of artificial intelligence (AI) and machine learning. Based on the operation of natural language, machine learning and advanced analytics, Hello Barbie can listen and answer a child.

By the end of this decade, a milestone is reached in artificial intelligence, with computers now routinely passing the Turing Test.** This test is conducted by a human judge who is made to engage in a natural language conversation with one human and one machine, each of which tries to appear human. Participants are placed in isolated locations. For several decades, information technology had seen exponential growth – leading to vast improvements in computer processing power, memory, bandwidth, voice recognition, image recognition, deep learning and other software algorithms. By the end of the 2020s, it has reached the stage where an independent judge is literally unable to tell which is the real human and which is not.* Answers to certain "obscure" questions posed by the judge may appear childlike from the AI – but they are humanlike nonetheless.*

Many systems require optimisation over multiple objectives, where objectives are characteristics of the system such as energy consumed or increase in time to perform the work. Optimisation is performed by selecting the `best' set of input parameters to elicit the desired objectives. However, the parameter search space can often be far larger than can be searched in a reasonable time. Additionally, the objectives are often mutually exclusive -- leading to a decision being made as to which objective is more important or optimising over a combination of the objectives. This work is an application of a Genetic Algorithm to identify the Pareto frontier for finding the optimal parameter sets for all combinations of objectives. A Pareto frontier can be used to identify the sets of optimal parameters for which each is the `best' for a given combination of objectives -- thus allowing decisions to be made with full knowledge. We demonstrate this approach for the HTC-Sim simulation system in the case where a Reinforcement Learning scheduler is tuned for the two objectives of energy consumption and task overhead. Demonstrating that this approach can reduce the energy consumed by ~36% over previously published work without significantly increasing the overhead.

How water acts affects everything from storm clouds to ice sheets. Computer scientists want to model water's various properties. Accurate and computationally efficient molecular-level descriptions of large samples of ice-water systems are difficult to build. The numerous molecules and various timescales remain a challenge despite advances in computing hardware. Now, a team developed machine-learning–based water models that correctly predict water's key features, such as the melting point of ice.

The industrial sector in the US, driven by high labor costs and the quick time-to-market, has been pushing to enhance production efficiency and lower operation costs, leading to an increase in the use of industrial artificial intelligence (AI) applications, according to a new report from ABI Research. The total installed base of AI-enabled devices in industrial manufacturing will show a compound annual growth rate of nearly 65% through 2024. US manufacturers have been aggressive with the adoption of industrial AI solutions; this has given birth to pure-play AI players in the US and will keep the US as the global leader in industrial AI solutions for some time to come. Over time, however, China will catch up, as investments are poured into AI and related technologies, says Lian Jye Su, principal analyst at ABI Research. Cloud service providers, smart manufacturing platform vendors, pure-play industrial AI platform and service providers, edge industrial AI gateway and server vendors, and chipset vendors are partnering with each other to bring AI into industrial manufacturing.

In 2012, Geoffrey Hinton's research team used only two NVIDIA GPUs to train AlexNet, the revolutionary network architecture that handily won the ImageNet Large Scale Visual Recognition Challenge. It probably never occurred to these groundbreaking researchers that just seven years later, a new team of researchers would use almost 10,000 times more GPUs to train their AI model. A research team from NVIDIA, Oak Ridge National Laboratory (ORNL), and Uber has introduced new techniques that enabled them to train a fully convolutional neural network on the world's fastest supercomputer, Summit, with up to 27,600 NVIDIA GPUs. They managed to achieve an impressive, near-linear scaling of 0.93 on distributed training and produce a model capable of atomically-accurate reconstruction of materials -- a longstanding scientific problem involving materials imaging. In June 2018 the US Department of Energy's Oak Ridge National Laboratory in Tennessee unveiled the world's fastest supercomputer Summit, whosecomputing power reaches 200 petaflops.

Energy game-theoretic frameworks have emerged to be a successful strategy to encourage energy efficient behavior in large scale by leveraging human-in-the-loop strategy. A number of such frameworks have been introduced over the years which formulate the energy saving process as a competitive game with appropriate incentives for energy efficient players. However, prior works involve an incentive design mechanism which is dependent on knowledge of utility functions for all the players in the game, which is hard to compute especially when the number of players is high, common in energy game-theoretic frameworks. Our research proposes that the utilities of players in such a framework can be grouped together to a relatively small number of clusters, and the clusters can then be targeted with tailored incentives. The key to above segmentation analysis is to learn the features leading to human decision making towards energy usage in competitive environments. We propose a novel graphical lasso based approach to perform such segmentation, by studying the feature correlations in a real-world energy social game dataset. To further improve the explainability of the model, we perform causality study using grangers causality. Proposed segmentation analysis results in characteristic clusters demonstrating different energy usage behaviors. We also present avenues to implement intelligent incentive design using proposed segmentation method.