Over the last 15 years there has been a surge in the use of machine learning to gain materials chemistry insights. These methods use existing data (largely computed with ab-initio methods) to train statistical models that can make useful predictions about whether chemical compounds will be stable, and the properties they are likely to exhibit. However, a large majority of the knowledge the scientific community has generated to date is recorded as "unstructured" text, and has therefore been largely inaccessible to machine-learning and statistical analysis. In recent years however, the Natural Language Processing (NLP) research community has made great progress on methods to computationally parse and learn from unstructured text. In our paper, we show how the application of an unsupervised NLP model can capture information from the materials chemistry literature in a way that also uncovers latent knowledge previously unknown to the research community.

Technology has always been about increasing productivity and efficiency, but its impact in the enterprise now transcends productivity in a few very important ways. One is that it plays a central role in organizations' business agility initiatives. Second is its impact on employee recruitment, productivity, and loyalty. In fact, the quality of employee experience today is often a reflection of the quality and adroitness of a company's digital prowess, and shapes perceptions about what makes an organization a top destination for talent. A truly digital experience refers not only to the ability to access information remotely, but also to seamlessly collaborate and innovate with colleagues.

The modern world is becoming increasingly technology driven. Many areas, such as healthcare, have been quick to realise the possibilities. AI and machine learning in oil & gas focused sectors has been slower to establish itself. This is largely because the industry has been slow to realise the potential. However this is slowly changing. Machine learning in oil & gas can be used to enhance the capabilities of this increasingly competitive sector. Not only can it help to streamline the workforce. The technology can also be used to optimise extraction and deliver accurate models. These benefits are just some of the reasons why machine learning in oil & gas is becoming increasingly important. Here are 10 ways that the impact of machine learning in oil & gas industries is being felt. One of the most noticeable impacts of machine learning in oil & gas focused industries is how it transforms discovery processes. Applications employing machine learning in oil & gas enable computers to quickly and accurately analyse huge amounts of data. This includes being able to sift precisely through signals and noise in seismic data.

"If we properly incorporate artificial intelligence, we can achieve a revolution with regard to sustainability. AI will be the driving force of the fourth industrial revolution," says Hendrik Fink, head of Sustainability Services at PricewaterhouseCoopers, Germany's largest auditing and consulting company. His comment aptly describes the opportunities opened up by new technical developments. In business, for example, it is now common to use systems that analyze and intelligently interpret meaningful data, with one application being the optimization of work and manufacturing processes. In the future, moreover, it will also be in the interest of small and medium-sized businesses to pay attention to sustainability and environmental issues.

Realizing this, and still needing to adapt to the increasingly industrialized world, more companies and people at large are moving towards adapting lower carbon-emitting solutions to generate electricity. Among the ways that we leverage on now is by harvesting solar or wind power to produce electricity. However, performance and reliability vary because solar panels and windmills rely on the weather to generate electricity, hence they would not be able to provide constant results all the time. Solar panels would not function under the cloudy or rainy sky, nor would windmills function when there's no breeze.

Machine learning (ML) is touted as a technology on the verge of changing how we plan and optimize not only our businesses, but also our lives. The onset of the climate crisis leads us to ask questions about how we can use this technology to help fight ― and eventually prevent ― overall climate change over the next few decades. I'd like to take a few minutes to help frame the discussion for anyone interested in using ML to combat this threat. However, it is important to understand that, like many other efforts aimed at combating climate change, it won't be a straight-forward and overnight process. Rather, it will require (re)thinking many of the ways we operate our businesses and ―even more ― how we operate as humans. The nature of ML is to find an underlying consistency in the data provided by an underlying data-generating system.

The team recently published a paper detailing their findings: "Creating Glasswing-Butterfly Inspired Durable Antifogging Omniphobic Supertransmissive, Superclear Nanostructured Glass Through Bayesian Learning and Optimization" in Materials Horizons (doi:10.1039/C9MH00589G). They recently presented this work at the ICML conference in the "Climate Change: How Can AI Help?" workshop. The nanostructured glass has random nanostructures, like the glasswing butterfly wing, that are smaller than the wavelengths of visible light. This allows the glass to have a very high transparency of 99.5% when the random nanostructures are on both sides of the glass. This high transparency can reduce the brightness and power demands on displays that could, for example, extend battery life.

Sure, computers can be used to play grandmaster-level chess (chess_computer), but can they make scientific discoveries? Researchers at the U.S. Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) have shown that an algorithm with no training in materials science can scan the text of millions of papers and uncover new scientific knowledge. A team led by Anubhav Jain, a scientist in Berkeley Lab's Energy Storage & Distributed Resources Division, collected 3.3 million abstracts of published materials science papers and fed them into an algorithm called Word2vec. By analyzing relationships between words the algorithm was able to predict discoveries of new thermoelectric materials years in advance and suggest as-yet unknown materials as candidates for thermoelectric materials. "Without telling it anything about materials science, it learned concepts like the periodic table and the crystal structure of metals," said Jain. "That hinted at the potential of the technique. But probably the most interesting thing we figured out is, you can use this algorithm to address gaps in materials research, things that people should study but haven't studied so far."

Modern Reinforcement Learning (RL) is commonly applied to practical problems with an enormous number of states, where function approximation must be deployed to approximate either the value function or the policy. The introduction of function approximation raises a fundamental set of challenges involving computational and statistical efficiency, especially given the need to manage the exploration/exploitation tradeoff. As a result, a core RL question remains open: how can we design provably efficient RL algorithms that incorporate function approximation? This question persists even in a basic setting with linear dynamics and linear rewards, for which only linear function approximation is needed. This paper presents the first provable RL algorithm with both polynomial runtime and polynomial sample complexity in this linear setting, without requiring a "simulator" or additional assumptions. Concretely, we prove that an optimistic modification of Least-Squares Value Iteration (LSVI)---a classical algorithm frequently studied in the linear setting---achieves $\tilde{\mathcal{O}}(\sqrt{d^3H^3T})$ regret, where $d$ is the ambient dimension of feature space, $H$ is the length of each episode, and $T$ is the total number of steps. Importantly, such regret is independent of the number of states and actions.

The complexity, heterogeneity and scale of electrical networks have grown far beyond the limits of exclusively human-based management at the Smart Grid (SG). Likewise, researchers cogitate the use of artificial intelligence and heuristics techniques to create cognitive and autonomic management tools that aim better assist and enhance SG management processes like in the grid reconfiguration. The development of self-healing management approaches towards a cognitive and autonomic distribution power network reconfiguration is a scenario in which the scalability and on-the-fly computation are issues. This paper proposes the use of Case-Based Reasoning (CBR) coupled with the HATSGA algorithm for the fast reconfiguration of large distribution power networks. The suitability and the scalability of the CBR-based reconfiguration strategy using HATSGA algorithm are evaluated. The evaluation indicates that the adopted HATSGA algorithm computes new reconfiguration topologies with a feasible computational time for large networks. The CBR strategy looks for managerial acceptable reconfiguration solutions at the CBR database and, as such, contributes to reduce the required number of reconfiguration computation using HATSGA. This suggests CBR can be applied with a fast reconfiguration algorithm resulting in more efficient, dynamic and cognitive grid recovery strategy.