My colleagues and I had the privilege of attending Tata Consultancy Services' (TCS) Analyst Day event held in Boston on September 21, 2017. There were several interesting and informative presentations covering topics such as the concept of Business 4.0 and how TCS is deploying Digital, deep domain expertise and its deep and vast portfolio of services and solutions to provide its customers with exponential value through mass customization, leveraging ecosystems to help its customers embrace and manage risk while maximizing business outcomes. As one of the ARC Analysts focused on upstream and midstream oil & gas it was great to learn more about how TCS is leveraging'cognitive automation' through its solutions such as Ignio, an product that provides some very powerful horsepower through its self-learning capability, empowered by machine learning and artificial intelligence (AI), that can move customers from predictive maintenance to prescriptive maintenance, thereby extending the life (and availability) of an asset such as a pump or compressor and also optimizing that asset's performance and the process for which it is being utilized. I know first-hand that TCS is successfully helping customers in Australia with pump optimization and increasing pump availability as well as, more importantly, helping to increase the customer's gas processing operations by saving over 100-man days per year, reducing pump downtime and increasing production.
We wanted to learn more about the opportunities and maturity level with technologies like big data, machine learning, artificial intelligence and the internet of things. For condition monitoring, these analytical tools can assist operators improve maintenance regimens and optimize inspection intervals by combining asset specific, industry, historical and real-time data into the data driven predictive and decision processes. "Big data" in the pipeline assessment business context includes the vast quantities of data coming from inspection devices full of sensors, such as pigs, and increasingly from embedded or remote sensors the most common which are catholic protection and corrosion coupons in addition we have a many types of asset property data, historical assessments, operational state, soils and environmental information which will exist in many formats, often unstructured such as in documents or photographs or other images. In the hackathon we taught our algorithms on previously pigged areas of pipelines where we had the inspection history to determine levels of corrosion as well as many forms of non-inspection information including those relating to pipe properties, corrosion protection history, coating type, local climate data, soil properties and previous field examination results.
In other words, their artificial intelligence program pays for itself in less than 18 months. Expedia's artificial intelligence program helps users to find the cheapest rates for hotels and flights worldwide. AutoTrader uses a machine learning artificial intelligence program to improve their car valuations. Chief Digital Officer Marc Florette says that timely detection of a crack in a high-pressure gas pipeline can save the company millions of dollars.
SEE ALSO: Drones are smuggling so much contraband into prisons that the UK created a'squad' Turgeon had used a drone, which he says he later returned, to record video of Native American-led protests against the construction of the Dakota Access Pipeline, which would run through Native American land. For this, Turgeon was arrested and charged with felony and misdemeanor counts of reckless endangerment as well as a misdemeanor count of physical obstruction of a government function, according to Motherboard. At about 33 minutes into the video below, you can see what is alleged to be Turgeon's drone flying nowhere close to a North Dakota Highway Patrol plane that is also in frame. The misdemeanor reckless endangerment charge came from allegedly flying a drone above protesters, "creating a substantial risk of serious bodily injury or death."
The R programming language already offers a general purpose package for piping function output to new functions, magrittr. The aim of this project is to provide a similar syntax, but much more focused on machine learning and utilizing a large number of preprocessing methods that are available in other R packages. For comparison, the Python machine learning toolbox Scikit learn offers Pipeline and FeatureUnion functions to chain multiple estimators and transformer functions into one call. As stated in their documentation the main advantage is that only one call to fit and predict is required for a complete pipeline fit and it is possible to optimize jointly over the whole space of hyperparameters.
The effort started in 2015 when GE announced Predix Cloud--an online platform to network and collect data from sensors on industrial machinery such as gas turbines or windmills. "We were using machine learning, but I would call it in a custom way," says Bill Ruh, GE's chief digital officer and CEO of its GE Digital business (GE calls its division heads CEOs). Today GE revealed the purchase of two AI companies that Ruh says will get them there. Bit Stew Systems, founded in 2005, was already doing much of what Predix Cloud promises--collecting and analyzing sensor data from power utilities, oil and gas companies, aviation, and factories.