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

[Audio]  Podcast: Play in new window | DownloadSubscribe: Google Podcasts | Spotify | Stitcher | TuneIn | RSST. 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 LinksT. 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.comApple Podcasts:  https://podcasts.apple.com/us/podcast/humain-podcast-artificial-intelligence-data-science/id1452117009Spotify:  https://open.spotify.com/show/6tXysq5TzHXvttWtJhmRpSRSS: https://feeds.redcircle.com/99113f24-2bd1-4332-8cd0-32e0556c8bc9YouTube Full Episodes: https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1ragYouTube Clips:  https://www.youtube.com/channel/UCxvclFvpPvFM9_RxcNg1rag/videosSupport 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/brandsPrivacy & Opt-Out: https://redcircle.com/privacy