What is exponential technology and how is it changing the world? Georgetown University’s Dr. Elizabeth “Libbie” Prescott and Harvard’s Dr. David A. Bray tell Michael Krigsman of CXOTalk about rapid changes in exponential and emerging technology, its implications on public service and public policy – and the legal or ethical implications for government policy.
For more information, see https://www.cxotalk.com/episode/exponential-technology-public-policy
Prescott works at the intersection of science, technology, and security as the Deputy Director and Education Portfolio Lead for MD5 National Security Technology Accelerator (MD5 NSTA) and Adjunct Associate faculty at Georgetown University in the Center for Security Studies in the Walsh School of Foreign Service. She previously served at the U.S. Department of State as a Special Assistant to the Deputy Secretary of State for Management and Resources Heather Higginbottom; Counselor and Strategic Advisor to the Science and Technology Adviser to the Secretary of State E. William Colglazier; and Science and Technology Adviser to the Assistant Secretary of State for the Bureau of East Asian and Pacific Affairs Kurt Campbell.
Bray was named one of the top "24 Americans Who Are Changing the World" under 40 by Business Insider in 2016. He was also named a Young Global Leader by the World Economic Forum for 2016-2021. He also accepted a role of Co-Chair for an IEEE Committee focused on Artificial Intelligence, automated systems, and innovative policies globally for 2016-2017 and has been serving as a Visiting Executive In-Residence at Harvard University since 2015 focusing on leadership strategies for our networked world. He has also been named a Marshall Memorial Fellow for 2017-2018 and will travel to Europe to discuss Trans-Atlantic issues of common concern including exponential technologies and the global future ahead.
From the transcript:
(06:08) Alright. So, let’s dive in. Data-enabled healthcare; Libby, you want to explain what do we mean by that and this concept of exponential technologies; where does it fit together?
(06:27) . So, when I talk about biotechnology, or biology, or healthcare in general, I think we're seeing a merging of data and health. And, I think, in the past, a lot of the delivery of our healthcare system, which I often would… I think the more appropriate descriptor for that is really a "disease-care system" because we're not optimizing for the health of an individual or even of a population, but actually, we're just really treating things in […].
(07:28) What data is allowing us to do is to think about understanding what the human, as an organism, actually is operating in closer to real-time. And, with that, we will be able to do a lot of unique things that are not only to prevent, hopefully, the negative outcomes, which would be disease-oriented, but also then you get into the optimizing of our not only just our health, but also our behaviors and our performance. And, that’s where we really start thinking about what does it mean to be performing at full functionality? What does augmenting of a human mean? And, to some extent, we are comfortable with certain types of permanent changes that we already do to ourselves as humans. But, as we get more data and understand more about really what it is to be in real-time at a biochemical level, operating in different contexts, I think we’re going to have an entirely different way we think about even what it means to be human.
(15:52) Yes, so if I could just give an enthusiastic “yes” to what Libby said; you think about it, and this just gets a little bit to the people-centered internet dimension that I’ll be wearing a hat on in a future life.
(17:42) The other thing that I think Libby also was touching on is more and more of this is going to come from the consumer today. I mean, we’ve already seen this in general IT trends that consumer space is, in some respects now, influencing what happens in technology enterprises. We shouldn’t be surprised if the same things start happening in the bio space and the healthcare space that consumer trends will start influencing the insurance and the enterprise influences as well. And, what do I mean by that? Well, there are companies already that are using machine learning to identify one, who could be in a clinical trial automatically. I mean, trying to do this manually can take between three to six to nine months, if you do it manually. A machine can actually look at, if you choose to share data and say, “You would be perfect for this therapeutic drug treatment, would you like to do it or not?” And, that’s being done in near-real time as opposed to delays of six to nine months.