The Cognitive Software Group demonstrates the power of the cognitive AI systems in highly complex Enterprise data environments, in Banks, in Medical Research, in Pharmaceutical companies, in industrial environments. and Oil and Gas industries.

COVID-19, a low oil price and an industry facing increased environmental scrutiny has resulted in a turbulent 2020 for the oil and gas sector. As many North Sea fields reach maturity, stakeholders will be carefully considering their options including decommissioning and diversifying the energy mix. The National Decommissioning Centre (NDC) (a partnership between the University of Aberdeen, the Oil & Gas Technology Centre (OGTC), and industry) has said that efficient late-life management and decommissioning of assets is a "societal and economic necessity". Emerging tech and artificial intelligence (AI) can help achieve this. However, the contribution AI and new technology could have on decommissioning cannot be considered in isolation.

Deep reinforcement learning (DRL) is an emerging methodology that is transforming the way many complicated transportation decision-making problems are tackled. Researchers have been increasingly turning to this powerful learning-based methodology to solve challenging problems across transportation fields. While many promising applications have been reported in the literature, there remains a lack of comprehensive synthesis of the many DRL algorithms and their uses and adaptations. The objective of this paper is to fill this gap by conducting a comprehensive, synthesized review of DRL applications in transportation. We start by offering an overview of the DRL mathematical background, popular and promising DRL algorithms, and some highly effective DRL extensions. Building on this overview, a systematic investigation of about 150 DRL studies that have appeared in the transportation literature, divided into seven different categories, is performed. Building on this review, we continue to examine the applicability, strengths, shortcomings, and common and application-specific issues of DRL techniques with regard to their applications in transportation. In the end, we recommend directions for future research and present available resources for actually implementing DRL.

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. With the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our models using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

Information theory is an excellent framework for analyzing Earth system data because it allows us to characterize uncertainty and redundancy, and is universally interpretable. However, accurately estimating information content is challenging because spatio-temporal data is high-dimensional, heterogeneous and has non-linear characteristics. In this paper, we apply multivariate Gaussianization for probability density estimation which is robust to dimensionality, comes with statistical guarantees, and is easy to apply. In addition, this methodology allows us to estimate information-theoretic measures to characterize multivariate densities: information, entropy, total correlation, and mutual information. We demonstrate how information theory measures can be applied in various Earth system data analysis problems. First we show how the method can be used to jointly Gaussianize radar backscattering intensities, synthesize hyperspectral data, and quantify of information content in aerial optical images. We also quantify the information content of several variables describing the soil-vegetation status in agro-ecosystems, and investigate the temporal scales that maximize their shared information under extreme events such as droughts. Finally, we measure the relative information content of space and time dimensions in remote sensing products and model simulations involving long records of key variables such as precipitation, sensible heat and evaporation. Results confirm the validity of the method, for which we anticipate a wide use and adoption. Code and demos of the implemented algorithms and information-theory measures are provided.

Building destruction during war is a specific form of violence which is particularly harmful to civilians, commonly used to displace populations, and therefore warrants special attention. Yet, data from war-ridden areas are typically scarce, often incomplete and highly contested, when available. The lack of such data from conflict zones severely limits media reporting, humanitarian relief efforts, human rights monitoring, reconstruction initiatives, as well as the study of violent conflict in academic research. One approach has been to use remote sensing to identify destruction in satellite images[1]. This approach is gaining momentum as high-resolution imagery is becoming readily available and is updated ever quicker yielding weekly or even daily frequency. At the same time recent methodological advances related to deep learning have provided sophisticated tools to extract data from these images [2, 3, 4, 5].

In 2018, Alphabet's X lab said it was in the process of exploring how it could use artificial intelligence to improve farming. On Monday, X announced that its "computational agriculture" project is called Mineral. The Mineral team has spent the last several years "developing and testing a range of software and hardware prototypes based on breakthroughs in artificial intelligence, simulation, sensors, robotics and more." One of the tools that has come out of the project is a robotic plant buggy. Powered by solar panels, the machine makes its way across a farmer's field, examining every plant it passes along the way with an array of cameras and sensors.

Inside a lab at Stanford University's Precourt Institute for Energy, there are a half dozen refrigerator-sized cabinets designed to kill batteries as fast as they can. Each holds around 100 lithium-ion cells secured in trays that can charge and discharge the batteries dozens of times per day. Ordinarily, the batteries that go into these electrochemical torture chambers would be found inside gadgets or electric vehicles, but when they're put in these hulking machines, they aren't powering anything at all. Instead, energy is dumped in and out of these cells as fast as possible to generate reams of performance data that will teach artificial intelligence how to build a better battery. In 2019, a team of researchers from Stanford, MIT, and the Toyota Research Institute used AI trained on data generated from these machines to predict the performance of lithium-ion batteries over the lifetime of the cells before their performance had started to slip.

A popular YouTuber has created the first functioning lightsaber, using propane gas burning at around 4,000 C to create a retractable plasma beam. Canadian James Hobson, known as'the Hacksmith', has a following of ten million subscribers and works on turning popular science fiction items into reality. Inspired by a love of Star Wars, he has previously made various lightsabers, but wanted to produce'the world's first, retractable, plasma-based' version. Canadian James Hobson, known as'the Hacksmith', has a following of ten million subscribers and works on turning popular science fiction items into a reality. He claims to have built'the world's first, retractable, plasma-based lightsaber' For this, the internet-famous engineers used liquid petroleum gas, a fuel tucked away in many sheds and often used to power barbecues.

High-performance computing (HPC) is becoming more commercially accessible for businesses looking to solve large problems using advanced technologies like artificial intelligence, machine learning, big data and video special effects. High-performance computing has enabled animation houses, fintechs, cloud-based tech providers, aerospace and oil and gas industries to power up their operations, scale up their business and release applications to customers with speed, agility and affordability. "Enterprises need to think about scalability. CPUs are getting faster and GPU performance is more powerful. They can easily add one GPU card or change a CPU to upgrade to a high-performance computing system," said Andy Lin, the Business Development Manager at GIGABYTE.