The Downsides of Rapid Changes in Technology and AI with T Scott
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T. Scott Clendaniel is an Artificial Intelligence Pioneer with 35 years’ proven track record of ROI improvements. He’s also a Guest Lecturer at Johns Hopkins University and University of Maryland, Harvard Innovation Labs’ Experfy, Artificial Intelligence course author and the Chief Data Officer of the Board of Directors at Gartner/ Evanta (DC region)
T. Scott’s LinkedIn: https://www.linkedin.com/in/tscottclendaniel/
T. Scott’s Twitter: https://twitter.com/Strat_AI?s=20
T. Scott’s Website: https://www.boozallen.com
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Here’s the timestamps for the episode:
(00:00) – Introduction
(01:43) – The pace of advancement has changed but problem solving leans more towards software development than problem solving itself.
(03:18) – Deep learning can’t provide solutions unless data is applied beyond the models.
(05:38) – Model building must be fully interpretable to be able to be fixed if needed
(07:15) – Protecting the rights of consumers and increasing the requirements on transparency of the models.
(12:55) – Ethics groups, reviewing policies and the “adverse impact test” for algorithms.
(15:46) –Overestimating AI’s impact in the future of work.
(16:49) – Automation and augmented intelligence: humans using computers to solve existing problems, as opposed to being replaced by them.
(21:22) – AI applications in specific industries for specific problems, focusing education on the good and the bad in AI.
(25:10) – Sharing the “wealth of knowledge” about predictive analytics..
(27:09) – Open sourcing education so that anyone can learn how to build and use models that are going to impact them.
(31:06) – New research on algorithms to find advanced sophisticated solutions to problems.
(34:07) – Data in general and Artificial Intelligence, specifically, can be used in good ways or detrimental ways.