Monday, November 15, 2021

g-f(2)656 THE BIG PICTURE OF THE DIGITAL AGE (11/15/2021), MIT SMR + BCG, ME, MYSELF AND AI, EPISODE 302, is an exceptional "Full Package Golden Knowledge Container"




ULTRA-condensed knowledge


"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital age, Artificial Intelligence, Full Pack Golden Knowledge Container (11/15/2021)  g-f(2)426 


Opportunity, MIT SMR + BCG

EXCEPTIONAL “Full Pack Golden Knowledge Container”, geniouxfacts  


      • Within the fabulous treasure of the digital age, containers of rare golden knowledge can be fished.
      • The “ME, MYSELF AND AI, EPISODE 302” podcast is an EXCEPTIONAL Full-Pack Golden Knowledge Container because it looks at why and how Artificial Intelligence (AI) is critical to the success of a giant like ExxonMobil.
      • Sarah Karthigan: I am currently responsible for leading the design and execution of self-healing strategies for IT operations, using artificial intelligence. Self-healing, at its core, is proactively monitoring, detecting, and remediating issues without human intervention.
      • Sarah Karthigan: There are plenty of opportunities for artificial intelligence in the energy sector.
        • At its core, what we do here at ExxonMobil is ensure that we are able to offer reliable, affordable energy to the masses. So the scale of energy itself is quite unimaginable, and the data that we work with is also massive. Big data is not new to the energy sector, so we deal with just huge volumes of data. Without artificial intelligence, without data science, or without machine learning, you can imagine the amount of effort that goes into just processing and analyzing that data. And with artificial intelligence, it is such a big, big, big advantage. The potential that AI carries with respect to just overall improving efficiency and cost effectiveness is huge.


      Genioux knowledge fact condensed as an image


      References


      Developing an Appetite for AI: ExxonMobil’s Sarah Karthigan, Sam Ransbotham and Shervin Khodabandeh, November 2, 2021, MIT Sloan Management Review, MIT SMR, Boston Consulting Group, BCG.


      ABOUT THE HOSTS


      Sam Ransbotham (@ransbotham) is a professor in the information systems department at the Carroll School of Management at Boston College, as well as guest editor for MIT Sloan Management Review’s Artificial Intelligence and Business Strategy Big Ideas initiative. Shervin Khodabandeh is a senior partner and managing director at BCG and the coleader of BCG GAMMA (BCG’s AI practice) in North America. He can be contacted at shervin@bcg.com.

      Me, Myself, and AI is a collaborative podcast from MIT Sloan Management Review and Boston Consulting Group and is hosted by Sam Ransbotham and Shervin Khodabandeh. Our engineer is David Lishansky, and the coordinating producers are Allison Ryder and Sophie Rüdinger.



      Extra-condensed knowledge


      Learned lessons, MIT SMR + BCG 


      • Sarah Karthigan.
        • We also use artificial intelligence for areas where we are able to automate manual tasks, thereby improving safety and productivity. And if we are able to get people [out] of harm’s way, that’s a huge application for artificial intelligence in the energy sector. Additionally, ExxonMobil is an energy company, but at its core, again, we are a technology company, and so we can use AI to help our scientists and engineers in their decision-making process. We are able to augment their decision-making, connect the dots, and help discover insights of value [for] them at a much faster pace, so there are plenty of applications.
        • My team and I, we have worked on several use cases. And again, when you think about big data, clearly you can think of potential applications of deep learning when it comes to image processing. Now whether that’s [at] the front end of the value chain — you know, you can start with seismic image processing to even leak and flare detection — so we can use artificial intelligence for just, again, plenty of use cases. So that’s one side of things. You can also use artificial intelligence — and we have used it for demand sensing, for dynamic pricing, for dynamic revenue management. Also, we have used it for trading. So there [are] just so many different applications that my team has been involved in.


      Condensed knowledge




      Lessons learned, MIT SMR + BCG


      • Shervin Khodabandeh: Sarah, tell us a bit about self-healing. I think you mentioned building AI systems that can preempt issues or problems or errors or faults — I don’t want to put words in your mouth — without human intervention. Could you give us some examples of those?
      • Sarah Karthigan: It all starts with monitoring, right? How well can we monitor our systems, capture the right type of data, and then integrate data, which is probably sitting across silos today? It all begins with that: capturing the data and bringing it all together and integrating it so you’re able to have visibility across the different silos that we have in place. It starts with observability. And then, once you have the data in place, now we are talking about: How can we utilize the data? How can we analyze it? How can we teach a machine? How can we train a machine to extract insights out of that data, to look at patterns, to see what typically happens before an incident occurs? It is able to look for those patterns. It’s able to understand the history and detect anomalies, and thereby it is able to prompt — either an end user, or you can just go ahead and close the loop out with automation altogether — and kick off the necessary automations that need to happen, need to occur, so we are able to remediate the issue even before it becomes an issue. That is kind of the life cycle of self-healing.
      • Shervin Khodabandeh: Yeah, that’s very helpful. And tell us a bit about the number of use cases, if you will. How big is this group’s span of impact and work?
      • Sarah Karthigan: There are multiple groups within ExxonMobil, because, as you were saying, given the scale of the company, it’s not possible to just centralize all of the data science capability in just one group, so we do have data scientists. We have AI engineers — machine learning engineers — embedded into the different business functions so they are able to work very closely with the business. And the opportunities — there are many. We are working on a myriad of those use cases, and they only continue to grow.


      Some relevant characteristics of this "genioux fact"

      • Category 2: The Big Picture of the Digital Age
      • [genioux fact deduced or extracted from MIT SMR + BCG]
      • This is a “genioux fact fast solution.”
      • Tag Opportunities those travelling at high speed on GKPath
      • Type of essential knowledge of this “genioux fact”: Essential Analyzed Knowledge (EAK).
      • Type of validity of the "genioux fact". 

        • Inherited from sources + Supported by the knowledge of one or more experts.


      References


      “genioux facts”: The online programme on MASTERING “THE BIG PICTURE OF THE DIGITAL AGE”, g-f(2)656, Fernando Machuca, November 15, 2021, blog.geniouxfacts.com, geniouxfacts.com, Genioux.com Corporation.


      ABOUT THE AUTHORS


      PhD with awarded honors in computer science in France

      Fernando is the director of "genioux facts". He is the entrepreneur, researcher and professor who has a disruptive proposal in The Digital Age to improve the world and reduce poverty + ignorance + violence. A critical piece of the solution puzzle is "genioux facts". The Innovation Value of "genioux facts" is exceptional for individuals, companies and any kind of organization.




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