Tuesday, November 16, 2021

g-f(2)660 THE BIG PICTURE OF THE DIGITAL AGE (11/16/2021), MIT SMR + BCG, ME, MYSELF AND AI, EPISODE 303, is an exceptional "Full Pack Golden Knowledge Container"




ULTRA-condensed knowledge


"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageArtificial Intelligence, Full Pack Golden Knowledge Container (11/16/2021)  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 discusses critical use cases of AI on Nasdaq, the evolution of AI, and the evolution of AI and humans working together.
      • Douglas Hamilton works across business units at Nasdaq to deploy artificial intelligence anywhere the technology can expedite or improve processes related to global trading.
      • In this episode of Me, Myself, and AI, Douglas Hamilton joins hosts Sam Ransbotham and Shervin Khodabandeh to explain how the global financial services and technology company uses AI to predict high-volatility indexes specifically and to offer more general advice for those working with high-risk scenarios.


      Genioux knowledge fact condensed as an image


      References




      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 


      • Douglas Hamilton.
        • What we are very concerned about, of course, is making sure our accuracy is very high, making sure our square scores, whatever, are very high; making sure that the metrics that are associated with business value are incredibly high. However, in order to make sure we’re hedging our risks, what is as important, if not more important, is being keenly aware of the distribution of the error associated with your model.
        • No matter what project we’re working on, whether it’s in our index space, whether it’s in our corporate services space, whether it’s in productivity and automation, or if it’s in new capabilities, we want to make sure that our error is distributed very uniformly, or at least reasonably uniformly, across all the constituent groups that we might be unleashing this model on — making sure that if there are areas where it doesn’t perform well, we have a good understanding of the calibrated interval of our models and systems, so that when we’re outside of that calibrated interval, frankly, at the very least, we can give somebody a warning to let them know that they’re in the Wild West now and they should do this at their own risk. And maybe it’s a little caveat emptor at that point, but at least you know.
        • Really, I think those are the two most important things to help manage those risks: being eminently concerned about the distribution of your error, and being really, really well aware about where your model works and where it doesn’t. 

      Condensed knowledge




      Lessons learned, MIT SMR + BCG


      • Shervin Khodabandeh: Let me use that as a segue to ask my next question. So you’ve been in the AI business for some time. How do you think the state of the art is evolving, or has evolved, or is going to evolve in the years to come? Obviously, technically it has been [evolving], and it will. But I’m more interested in [the] nontechnical aspects of that evolution. How do you see that?
      • Doug Hamilton: When I first got started, the big papers that came out were probably [on] the [generative adversarial network] and [residual neural network]; both came out actually about the same time. [In a ] lot of ways, to me that represented the pinnacle of technical achievement in AI. Obviously, there’s been more since then, obviously we’ve done a lot, obviously a lot of things have been solved. But at that point, we figured a lot of things out. And it opened the door to a lot of really good AI and machine learning solutions. When I look at the way the technology has progressed since then, I see it as a maturing ecosystem that enables business use.
        • So whether this is things like transfer learning, to make sure that when we solve one problem, we can solve another problem, which is incredibly important for achieving economies of scale with AI groups, or it’s things like AutoML that help to make everybody at least … this kind of idea of a citizen data scientist, where software engineers and analysts can do enough machine learning research or machine learning work that they can prove something out before they bring it to a team like ours or their software engineering team. I think these are the sorts of maturing technologies that we’ve seen come along that make machine learning much more usable in business cases.
        • I think beyond that, historically what we’ve seen is the traditional business case for artificial intelligence have been all-scale plays. I think these maturing technologies are these technologies that are allowing us to mature models, reuse them, and achieve economies of scale around the AI development cycle. As these get better and better, we’re going to see more use cases open up for “Computers are good at it.” And we’ve certainly seen it when we look at how hedge funds and high-frequency traders operate. They’re all using machine learning all over the place, because it’s better for research purposes than ad hoc trial and error and ad hoc rules. By the same token, we’ve seen it in game-playing machines for years. So the idea that we’ll have more and more of these situations where [the] computer is just better at it, I think we’re going to see that more and more.


      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)660, Fernando Machuca, November 16, 2021, blog.geniouxfacts.comgeniouxfacts.comGenioux.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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