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?
Caterpillar Inc. (often shortened to CAT) is an American Fortune 100 corporation that designs, develops, engineers, manufactures, markets, and sells machinery, engines, financial products, and insurance to customers via a worldwide dealer network. It is the world's largest construction-equipment manufacturer. In 2018, Caterpillar was ranked number 65 on the Fortune 500 list and number 238 on the Global Fortune 500 list. Caterpillar stock is a component of the Dow Jones Industrial Average . CATERPILLAR INC.&tbm isch Caterpillar is the world's leading manufacturer of construction and mining equipment, diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. We are a leader and proudly have the largest global presence in the industries we serve.
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.
The move from automated to autonomous process manufacturing is right around the corner. This article comes from the May 2021 issue of Intech Focus: Process Control and Safety. For process manufacturing, the ultimate promise of Industry 4.0 is autonomous manufacturing. Autonomous control of manufacturing processes is required, not to eliminate human workers, but to build resilient and highly responsive manufacturing supply chains. Resilience is required to enhance the top and bottom lines of a manufacturing enterprise.
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, the twelve-year-old Silicon Valley startup that is bringing machine learning forms of AI to various industries such as oil and gas, on Wednesday said it is partnering with data analytics upstart Snowflake, the cloud-based vendor of data warehouses and other wares. The duo promised to take customers from start to production deployment of apps in one month. The arrangement provides for Snowflake users to "be provided with access to the C3 AI Suite and pre-built C3 AI applications that address a range of industries and enterprise AI use cases," the two companies said. C3.ai's chief product officer, Houman Behzadi, said the partnership "will create significant time and operational efficiencies for Snowflake's customers and solidify Snowflake as the operational data platform of choice for enterprise AI applications." Snowflake's leader of its product efforts, Christian Kleinerman, commented that the collaboration "will accelerate the development and deployment of complex AI and machine learning use cases," adding that the "C3 AI Suite and C3 AI's pre-built enterprise-grade models significantly speed and simplify the development of enterprise AI applications."
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."
Please welcome new Cambrian-AI Analyst Gary Fritz, who contributed to this article. Artificial Intelligence applications are starting to show up in everything from cell phones to supertankers. But at the edge, they are running into the same roadblocks that traditional applications have fought for years: they need more speed. What's a burgeoning neural net to do? To make matters worse, machine learning models are growing at an exponential rate, doubling in size every 3.5 months.
As with many things, timing is everything, and in the weeks after word drifted out that Australia's Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61 was binning its secure microkernel research, the world of cyber attacks manifested in the real world in new ways. From oil pipelines, to meat works, to a more traditional Russian-backed phishing campaign, the cyberdial has been turned up and the frequency of attacks, particularly in the ransomware space, has hit deluge-like levels. And yet, while the torrent of malware is far from unexpected, people lining up with jerry cans and fighting with each other because someone might have clicked on a dodgy email certainly is. The need to develop a better foundation, and more secure ways of computing, would appear to be more necessary than ever -- but not at the CSIRO, where artificial intelligence is the order of the day. "We think Australia needs artificial intelligence for industry 4.0, for our sovereign capability, for digital agriculture, and to deal with environmental hazards," CSIRO CEO Dr Larry Marshall told Senate Estimates on Thursday night.