If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
But wait, hasn't there been a mathematical method for optimizing portfolios around for some years? Right, it's called the Modern portfolio theory (MPT) by economist Harry Markowitz, introduced in a 1952 essay, for which he was later awarded a Nobel Memorial Prize in Economic Sciences. The simple idea of the model is diversification in investing: owning different kinds of financial assets is less risky than owning only one type. Its key insight is that an asset's risk and return should not be assessed by itself, but by how it contributes to a portfolio's overall risk and return. And how can we make it AI?
All others have a large and varying degree of missing values. Within the missingno library, there are four types of plots for visualising data completeness: the barplot, the matrix plot, the heatmap, and the dendrogram plot. Each has its own advantages for identifying missing data. Let's take a look at each of these in turn. The barplot provides a simple plot where each bar represents a column within the dataframe. The height of the bar indicates how complete that column is, i.e, how many non-null values are present.
Flexible plant operations are highly desirable in today's power generation industry. Every plant owner desires increased ramp rates and the ability to operate at lower loads so their plants will remain "in the money" longer in today's competitive power markets. This goal, while laudable, remains elusive. The ADEX self-tuning artificial intelligence (AI) system allows plants to continuously optimize plant performance at any operating point rather than being constrained to a static "design point" commonly found in gas- and coal-fired plants. Better yet, no changes to the plant distributed control system (DCS) are required.
This course will give an overview of all the topics we shall be looking at in this course. We shall begin by describing the oil value chain – the exploration and development, how oil is produced, shipped, and marketed. Moving further, we will learn about the importance of oil in the industry, both as a fuel and as a raw material in various forms in the global economy. Then, we will go through a brief history of oil – how it all began, and the different'kinds' of oil discoverers. We will be introduced to the major players in the oil market – the top producers and the major consumers. We will then see how oil is formed, how it sits deep within the earth and how we discover and refine it. We will learn about the different types of oils, and the methods employed to extract them. This will be followed by a brief overview of the different means of transporting oil, and the risks and benefits associated with the different methods of oil transport. Lastly, we shall look into the different oil benchmarks that prevail globally.
C3.ai's Digital Transformation Institute announced the 21 winners of their contest centered around healthcare, energy and climate-related projects. The company offered between $100,000 and $250,000 to groups that could start projects using AI and digital transformation to address COVID-19, climate security and energy efficiency. Out of the 52 submissions that came in since February, 21 were selected for the grants, with each focusing on efforts to "improve resilience, sustainability, and efficiency" using "carbon sequestration, carbon markets, hydrocarbon production, distributed renewables, and cybersecurity." S. Shankar Sastry, a co-director of C3.ai DTI and a leading computer science professor at the University of California, Berkeley, said the world was now being threatened by the pandemic, powerful wildfires, rising seas, monster storms and other severe weather threats. Marta Gonzalez, an associate professor at the University of California, Berkeley, is looking to create a platform that could collate more data about wildfires. The project will involve "crowdsourcing and very high-resolution remote sensing for an AI-driven fuel model identification; models of wildfire behavior, intensity, spread, informed by downscaled climate change predictions, historic catastrophic wildfires, environmental monitoring; and egress models that combine large-scale mobile phone data facilitated by data-driven optimization models and computation."
The need to transform traditional mining operations is clear. Extraction companies have had to work harder to find fewer valuable resources than previously, and they have had to do it with less-skilled and experienced workers. Companies have invested in digital tools and systems to transform ways of working and overcome these challenges. The World Economic Forum has forecast that $425 billion of value will be added to the mining industry through digitalization between 2017 and 2025. Research by Berg Insight found the total number of connected mining devices and equipment was just under 0.6 million items worldwide in 2018, but by 2023, it will reach around 1.2 million.
As data accessibility and analysis capabilities have rapidly advanced in recent years, new digital platforms driven by artificial intelligence (AI) and machine learning (ML) are increasingly finding practical applications in industry. "Data are so readily available now. Several years ago, we didn't have the manipulation capability, the broad platform or cloud capacity to really work with large volumes of data. We've got that now, so that has been huge in making AI more practical," says Paige Morse, industry marketing director for chemicals at Aspen Technology, Inc. (Bedford, Mass.; www.aspentech.com). While AI and ML have been part of the digitalization discussion for many years, these technologies have not seen a great deal of practical application in the chemical process industries (CPI) until relatively recently, says Don Mack, global alliance manager at Siemens Industry, Inc. (Alpharetta, Ga.; www.industry.usa.siemens.com). "In order for AI to work correctly, it needs data. Control systems and historians in chemical plants have a lot of data available, but in many cases, those data have just been sitting dormant, not really being put to good use. However, new digitalization tools enable us to address some use cases for AI that until recently just weren't possible." This convergence of technologies, from smart sensors to high-performance computing and cloud storage, along with advances in data science, deep learning and access to free and open-source software, have enabled the field of industrial AI to move beyond pure research to practical applications with business benefits, says Samvith Rao, chemical and petroleum industry manager at MathWorks (Natick, Mass.; www.mathworks.com).
Engineering Application of Data Science can be defined as using Artificial Intelligence and Machine Learning to model physical phenomena purely based on facts (field measurements, data). The main objective of this technology is the complete avoidance of assumptions, simplifications, preconceived notions, and biases. One of the major characteristics of Engineering Application of Data Science is its incorporation of Explainable Artificial Intelligence (XAI). While using actual field measurements as the main building blocks of modeling physical phenomena, Engineering Application of Data Science incorporates several types of Machine Learning Algorithms including artificial neural networks, fuzzy set theory, and evolutionary computing. Predictive models of Engineering Application of Data Science (data-driven predictive models) are not represented through unexplainable "Black Box". Predictive models of Engineering Application of Data Science are reasonably explainable.
LKAB, Minalyze AB and Sentian say they have joined forces in a consortium to develop the latest technology for scanning drill core. In March 2020, LKAB started a test with the Minalyzer CS drill core scanner where the goal was to improve the workflow for core logging – ie how the results of exploration drilling are analysed. The test led to a permanent installation in Kiruna (Sweden) and expansion to Malmberget where data from the Minalyzer CS is used to help geological logging of the drill core. The consortium of LKAB, Minalyze and Sentian are now set to take the use of data to the next level when boreholes in LKAB's deposits are to be investigated. The new artificial intelligence application being developed by the trio will make the analysis much faster, with the time to evaluate a drill core reduced from weeks to minutes, with increased accuracy.
As was mentioned earlier in this article, Type Curves that are generated using mathematical equations are very "well-behaved" (continuous, non-linear, certain shape that changes in a similar fashion from curve to curve). Figure 16 demonstrates few more examples of Type Curves that have been generated in reservoir engineering. The question is, "what is the main characteristic of a model that is capable of generating series of well-behave Type Curves?" The immediate, simple answer to this question would be: "the model that is capable of generating a series of well-behave Type Curves is a physics-based model developed by one or more mathematical equations. The well-behave Type Curves that clearly explain the behavior of the physics-based model are generated through the solutions of the mathematical equations."