The Downsides of Rapid Changes in Technology and AI with T. Scott

[Audio]   Podcast: Play in new window | Download Subscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSS 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)  Episode Links 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  Podcast Details:  Podcast website: https://www.humainpodcast.com Apple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009 Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpS RSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9 YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag YouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videos Support and Social Media:   – Check out the sponsors above, it’s the best way to support this podcast – Support on Patreon: https://www.patreon.com/humain/creators   – Twitter:  https://twitter.com/dyakobovitch – Instagram: https://www.instagram.com/humainpodcast/ – LinkedIn: https://www.linkedin.com/in/davidyakobovitch/ – Facebook: https://www.facebook.com/HumainPodcast/ – HumAIn Website Articles: https://www.humainpodcast.com/blog/ Outline:  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. Advertising Inquiries: https://redcircle.com/brands Privacy & Opt-Out: https://redcircle.com/privacy