Podcasts about NSF

In this 152nd in a series of live discussions with Bret Weinstein and Heather Heying (both PhDs in Biology), we discuss the state of the world through an evolutionary lens. This week, we discuss the free exchange of ideas, in light of Matt Taibbi's revelations about how the story of Hunter Biden's laptop was censored inside Twitter, and Kanye West going explicitly antisemitic on InfoWars. Why should Kanye be heard, given that what he is saying is awful? We also discuss chemist Anna Krylov's newest piece, who has seen first-hand what happens when ideas are censored and events disappeared. The National Science Foundation awards a large amount of money to an organization called Hacks/Hackers, who use that money to “discover” that Bret is a conservative who spreads vaccine misinformation. Meanwhile, Pfizer mocks people who try to figure things out for themselves, and receives a Reuters Pharma Award. That is not a typo. ***** Our sponsors: Ned: is a CBD company that uses USDA certified organic full spectrum hemp oil, and creates specialty blends to help with stress and sleep. Visit www.helloned.com/darkhorse to get 15% off. Vivo Barefoot: Shoes for healthy feet—comfortable and regenerative, enhances stability and tactile feedback. Go to www.vivobarefoot.com/us/darkhorse to get 20% off, and a 100-day free trial. Public Goods: Get $15 off your first order at Public Goods, your new everything store, at https://www.publicgoods.com/darkhorse or with code DARKHORSE at checkout. ***** Our book, A Hunter-Gatherer's Guide to the 21st Century, is available everywhere books are sold, and signed copies are available here: https://darvillsbookstore.indielite.org Check out our store! Epic tabby, digital book burning, saddle up the dire wolves, and more: https://darkhorsestore.org Heather's newsletter, Natural Selections (subscribe to get free weekly essays in your inbox): https://naturalselections.substack.com Find more from us on Bret's website (https://bretweinstein.net) or Heather's website (http://heatherheying.com). Become a member of the DarkHorse LiveStreams, and get access to an additional Q&A livestream every month. Join at Heather's Patreon. Like this content? Subscribe to the channel, like this video, follow us on twitter (@BretWeinstein, @HeatherEHeying), and consider helping us out by contributing to either of our Patreons or Bret's Paypal. Looking for clips from #DarkHorseLivestreams? Check out our other channel:  https://www.youtube.com/channel/UCAWCKUrmvK5F_ynBY_CMlIA Theme Music: Thank you to Martin Molin of Wintergatan for providing us the rights to use their excellent music. ***** Mentioned in this episode: Matt Taibbi thread, the twitter files: https://twitter.com/mtaibbi/status/1598822959866683394 The Peril of Politicizing Science, Krylov 2021: https://pubs.acs.org/doi/full/10.1021/acs.jpclett.1c01475 The Diversity Problem on Campus, Abbot & Marinovic 2021: https://www.newsweek.com/diversity-problem-campus-opinion-1618419 From Russia with Love: Science and Ideology Then and Now, Krylov 2022: https://hxstem.substack.com/p/from-russia-with-love-science-and Hacks/Hackers awarded large NSF grant to advise people “what to say”: https://www.hackshackers.com/hacks-hackers-partners-advance-phase-ii/ Feds fund 'expert-informed' internet toolkit to help users fight 'misinformation': https://thenationaldesk.com/news/americas-news-now/feds-fund-expert-informed-internet-toolkit-to-help-users-fight-misinformation Pfizer moSupport the show

Link from the show:* Connect with Matthew on Twitter* Connect with Ryan on Twitter* Subscribe to the newsletterAbout my guest:Dr. Matthew W. Johnson, Ph.D., is Professor of Psychiatry and Behavioral Sciences at Johns Hopkins. He is one of the world's most published scientists on the human effects of psychedelics, and has conducted seminal research in the behavioral economics of drug use, addiction, and risk behavior. Dr. Johnson earned his Ph.D. in experimental psychology at the University of Vermont in 2004.Working with psychedelics for 16 years, Dr. Johnson published psychedelic safety guidelines in 2008, helping to resurrect psychedelic research. As Principle Investigator he developed and published the first research on psychedelic treatment of tobacco addiction in 2014. Dr. Johnson and colleagues published the largest study of psilocybin in treating cancer distress in 2016. His 2018 psilocybin abuse liability review recommended placement in Schedule-IV upon potential medical approval. He is Principle Investigator on funded studies investigating psilocybin in the treatment of opioid dependence and PTSD. Beyond psilocybin, in 2011 Dr. Johnson published the first-ever blinded human research showing psychoactive effects of salvinorin A, the active constituent in Salvia divinorum. He also published in 2017 the first data indicating that MDMA pill testing services may reduce harm, specifically by reducing drug consumption of unknown or undesired adulterants.Dr. Johnson is recognized for his research in behavioral economics, behavioral pharmacology, and behavior analysis. He has conducted seminal and widely cited research applying behavioral economic principles such as delay discounting and demand analysis to decision making within addiction, drug consumption, and risk behavior. This includes research determining delay discounting to be a fundamental behavioral process underlying addiction across drug classes, using economic demand analysis to determine the roles of nicotine and nonpharmacological factors in the abuse liability tobacco and other nicotine products, and using delay discounting, probability discounting, and demand analysis to understand sexual risk including condom non-use in casual sex situations. He conducted the first research administering cocaine to humans in determining that cocaine increases sexual desire and affects sexual decision making. He has conducted similar research administering methamphetamine and alcohol, examining effects on sexual decision making. He has published studies on drugs across nearly all psychoactive classes, including studies of cocaine, methamphetamine, tobacco/nicotine, alcohol, opioids, cannabis, benzodiazepines, psilocybin, dextromethorphan, salvinorin A, GHB, caffeine, and cathinone analogs compounds (so-called “bath salts”).Dr. Johnson was 2019 President of the Psychopharmacology and Substance Abuse Division of the American Psychological Association, and is current President of the International Society for Research on Psychedelics, an organization he founded with colleagues. He has received continuous NIH funding as Principal Investigator since 2009. He has reviewed for >75 journals and has served as guest editor on two special issues on psychedelics. Dr. Johnson has reviewed grants for NIH, NSF, the US Military, and multiple governments outside of the US. He is a standing member of the Addictions Risks and Mechanisms (ARM) NIH study section. He has provided invited presentations in 13 nations.Dr. Johnson has been interviewed widely by media about psychedelics and other drugs. These have included interviews by the New York Times, the Washington Post, the Wall Street Journal, the Globe and Mail, the Daily Mail, USA Today, the Chicago Tribune, the San Francisco Chronicle, the Denver Post, the Baltimore Sun, CNN, CBS News, NBC News, the Atlantic, Newsweek, Vanity Fair, Marie Claire, Vogue, Whole Living, the Washingtonian, Scientific American, Nature, Vice, Insider, Inverse, Healthline, and Psychology Today. Dr. Johnson has appeared for interviews on numerous television and radio shows including 60 Minutes, CNN's Wolf Blitzer Situation Room, Fox Business News' Kennedy, the Dr. Oz Show, PBS' Retro Report, Labyrint (television show in the Netherlands), Spectrum News NY1, the BBC World Service, NPR's Morning Edition, NPR's Kojo Nnamdi Show, New Zealand Radio, and Newstalk Radio Ireland. Dr. Johnson's panel discussion with Tim Ferriss at the Milken Institute Global Conference was broadcast on the Tim Ferriss Podcast. Dr. Johnson and his research were featured in an episode of Breakthrough on the National Geographic Channel, produced by Ron Howard, and in Michael Pollan's best-selling book, How to Change Your Mind: What the New Science of Psychedelics Teaches Us About Consciousness, Dying, Addiction, Depression, and Transcendence. Get full access to Dispatches from the War Room at dispatchesfromthewarroom.substack.com/subscribe

There's rarely an expected path in science. This week's episode, produced in partnership with The Carl R. Woese Institute for Genomic Biology, features two stories from scientists of their cutting-edge research institute at the University of Illinois Urbana-Champaign who took unexpected journeys to get where they are today. Part 1: After a troubling personal experience with the health care system, Heng Ji decides to try to fix it. Part 2: When Brendan Harley is diagnosed with leukaemia in high school, it changes everything. Heng Ji is a professor at Computer Science Department, and an affiliated faculty member at Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign. She is also an Amazon Scholar. She received her B.A. and M. A. in Computational Linguistics from Tsinghua University, and her M.S. and Ph.D. in Computer Science from New York University. Her research interests focus on Natural Language Processing, especially on Multimedia Multilingual Information Extraction, Knowledge Base Population and Knowledge-driven Generation. She was selected as "Young Scientist" and a member of the Global Future Council on the Future of Computing by the World Economic Forum in 2016 and 2017. She was named as part of Women Leaders of Conversational AI (Class of 2023) by Project Voice. The awards she received include "AI's 10 to Watch" Award by IEEE Intelligent Systems in 2013, NSF CAREER award in 2009, PACLIC2012 Best paper runner-up, "Best of ICDM2013" paper award, "Best of SDM2013" paper award, ACL2018 Best Demo paper nomination, ACL2020 Best Demo Paper Award, NAACL2021 Best Demo Paper Award, Google Research Award in 2009 and 2014, IBM Watson Faculty Award in 2012 and 2014 and Bosch Research Award in 2014-2018. She was invited by the Secretary of the U.S. Air Force and AFRL to join Air Force Data Analytics Expert Panel to inform the Air Force Strategy 2030. She is the lead of many multi-institution projects and tasks, including the U.S. ARL projects on information fusion and knowledge networks construction, DARPA DEFT Tinker Bell team and DARPA KAIROS RESIN team. She has coordinated the NIST TAC Knowledge Base Population task since 2010. She was the associate editor for IEEE/ACM Transaction on Audio, Speech, and Language Processing, and served as the Program Committee Co-Chair of many conferences including NAACL-HLT2018 and AACL-IJCNLP2022. She is elected as the North American Chapter of the Association for Computational Linguistics (NAACL) secretary 2020-2023. Her research has been widely supported by the U.S. government agencies (DARPA, ARL, IARPA, NSF, AFRL, DHS) and industry (Amazon, Google, Facebook, Bosch, IBM, Disney). Heng Ji is supported by NSF AI Institute on Molecule Synthesis, and collaborating with Prof. Marty Burke at Chemistry Department at UIUC and Prof. Kyunghyun Cho at New York University and Genetech on using AI for drug discovery. Dr. Brendan Harley is a Professor of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. His research group develops biomaterial that can be implanted in the body to regenerate musculoskeletal tissues or that can be used outside the body as tissue models to study biological events linked to endometrium, brain cancer, and stem cell behavior. He's a distance runner who dreams of (eventually) running ultramarathons. Follow him @Prof_Harley and www.harleylab.org. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Wetlands like the marshes located just outside the city of New Orleans, LA are often overlooked, but these ecosystems provide many vital services. Wetlands, like all ecosystems, are under threat by climate change and particularly the increased tropical storms and hurricanes that tear across our coasts. So how can we monitor vast areas of wetlands to check their health, year after year? Statistician-in-Residence Tiffany Christian explains how researchers can remotely monitor wetland health and spot disruption of annual growth cycles. Find our transcript here: LINK Curious to learn more? Check out these additional links: Review of storm effects on wetlands: https://www.sciencedirect.com/science/article/abs/pii/S1872203215000657#:~:text=Impacts%20of%20tropical%20storms%20on%20wetland%20landscape%20changes,change%20wetland%20morphology%20and%20elevation Using remote sensing to detect changes in wetlands growing season: https://www.sciencedirect.com/science/article/pii/S0304380015002197 Overview of wetland health in Louisana and impact of MRGO: https://mississippiriverdelta.org/coalition-unveils-findings-on-mrgo-ecosystem-recovery-15-years-after-hurricane-katrina/ and the associated white paper https://mrgomustgo.org/wp-content/uploads/2020/09/MRGO-White-Paper-10-01-2020.pdf Importance of wetlands: https://www.epa.gov/wetlands/why-are-wetlands-important#:~:text=Wetlands%20and%20People,our%20use%20at%20no%20cost. Follow more of IMSI’s work: www.IMSI.institute, (twitter) @IMSI_institute, (instagram) IMSI.institute Follow Tiffany Christian: https://www.linkedin.com/in/tiffany-christian-733137b5/ This episode was audio engineered by Tyler Damme. Music by Blue Dot Sessions. The Institute for Mathematical and Statistical Innovation (IMSI) is funded by NSF grant DMS-1929348.

Myrkranetið (dark web) samanstendur af vefsíðum og öðru efni sem einungis er hægt að nálgast með sérstökum hugbúnaði, heimildum eða stillingum. Þar sem myrkranetið býður upp á nafnleynd og ýmis skúmaskot hafa hlutar þess orðið vettvangur margskonar glæpastarfsemi. Til að fræðast meira um þennan myrka afkima internetsins fékk Guðmundur Oddsson dósent í félagsfræði við HA hann Christopher Copeland í settið en sá er rannsóknarlektor við Tarleton State University í Texas í Bandaríkjunum. Christopher dvaldi á haustmisseri við námsbraut Háskólans á Akureyri í lögreglufræðum sem NSF sérfræðingur á sviði netöryggismála.

Initial Results from the First of Its Kind Clinical Study Demonstrate cbdMD's Proprietary Broad Spectrum Hemp Extract Reduces Pain in Healthy Adults.Charlotte, North Carolina--(Newsfile Corp. - November 17, 2022) - cbdMD, Inc. (NYSE American: YCBD) (NYSE American: YCBDpA), one of the nation's leading, highly trusted and widely recognized CBD companies, today announces the first results from its recently concluded human clinical trial conducted at the University of South Carolina. These studies began in the fall of 2020 and the first results to be reported show the Company's proprietary broad spectrum hemp extract reduces pain in healthy adults. The ground breaking randomized, double blind, placebo controlled study was designed to explore the effects of cbdMD's proprietary broad spectrum cannabinoid blend on sleep, immunity, inflammation, mood, and pain in healthy adult subjects to further support the efficacy of the Company's products. The Company and its subsidiary cbdMD Therapeutics, LLC will be releasing further results from the human clinical as they are finalized over the next few weeks and will eventually be publishing the results in a peer reviewed journal. The Company anticipates these favorable outcomes will form the basis for a number of future cbdMD product formulations. The study's results will also serve as preliminary data for additional future investigational studies executed by cbdMD's Therapeutics Division.Study participants were given cbdMD's proprietary broad spectrum hemp extract blend containing 100 mg of CBD and other minor cannabinoids once per day for three months. The first results coming out of the study show that the perception and intensity of pain in healthy adults is significantly reduced with daily consumption of the Company's flagship broad spectrum blend. In anticipation of these results, the Company has recently launched a new flagship product in tincture, gummy and softgel which contains the clinically studied broad spectrum hemp extract blend containing 100 mg per serving of CBD. The flagship blend is contained in all of the Company's broad spectrum products, including its industry first NSF for Sport products. In order to make these efficacious products available to more people in need, the Company has repositioned itself to be CBD brand of choice by providing the highest potency products at the best value in the industry."Once published, the data from this study will be submitted to the Food and Drug Administration (FDA) for Structure Function Claim Notifications (SFCNs), the process whereby manufacturers inform FDA of their intent to make statements about their products' benefits on the structure or function of their body," said Lance Blundell, cbdMD's General Counsel and Co-Chair of cbdMD Therapeutics, LLC. "cbdMD leads the industry with our commitment to science. We also lead the industry with our belief that as a responsible and trusted dietary supplement manufacturer we must comply with all applicable regulations. We have conducted the required toxicological studies and we have now completed the required human clinical in order to make structure function claims about our proprietary CBD products. We hold a firm belief that our products are not drug precluded as the Citizen's Petition we filed earlier this year stated. Therefore, we will submit our SFCNs to the FDA as all federally compliant dietary supplement companies should. We will continue to advocate and fight for regulatory clarity and fair treatment for our industry. While others are content to sit back and wait, we will pave the way for legal cannabinoid products.""Our products help solve customers' problems. The data from this study will guide our product development roadma

Today we feature a broad, thoughtful conversation with Dr. Ron Buckmire, Professor of Mathematics and Associate Dean for Curricular Affairs at Occidental College in Los Angeles, Califronia. Ron was born in Grenada, grew up in the States, and earned his undergraduate, masters and PhD degrees from Renssalear Polytehnic Institute in Troy, New York. He studied applied mathematics and recently published Improving Applied Mathematics Education with Jessica Libertini in 2021. He has served as a Program Director in the NSF's Division of Undergraduate Education in the Washington, DC area . In this conversation Ron talks about “leaving the campground cleaner than you found it,” the role of immigration in a professional life, the importance of funding, and expanding people through education and understanding.

Today we feature a broad, thoughtful conversation with Dr. Ron Buckmire, Professor of Mathematics and Associate Dean for Curricular Affairs at Occidental College in Los Angeles, Califronia. Ron was born in Grenada, grew up in the States, and earned his undergraduate, masters and PhD degrees from Renssalear Polytehnic Institute in Troy, New York. He studied applied mathematics and recently published Improving Applied Mathematics Education with Jessica Libertini in 2021. He has served as a Program Director in the NSF's Division of Undergraduate Education in the Washington, DC area . In this conversation Ron talks about “leaving the campground cleaner than you found it,” the role of immigration in a professional life, the importance of funding, and expanding people through education and understanding.

Gamma Ray bursts are some of the most powerful explosions observed in the cosmos and frequently herald the birth of a new black hole. A recent gamma ray burst, GRB 221009A, appears to be the brightest of all time and was observed very quickly by multiple instruments. In this podcast, Jillian Rastinejad discusses the discovery of this gamma ray burst and the follow up observations with the Gemini International Observatory.    Bios:  - Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona.. - Jillian Rastinejad is a PhD student at Northwestern University in Evanston, IL. She utilizes many of the world's largest optical telescopes in her quest to better understand the sources of the Universe's most powerful explosions, gamma-ray bursts. When she's not chasing gamma-ray bursts, Jillian enjoys going for runs on Lake Michigan and exploring Chicago's many neighborhoods.   Links:  https://noirlab.edu/public/news/noirlab2224/ https://www.facebook.com/NOIRLabAstro https://twitter.com/NOIRLabAstro https://www.instagram.com/noirlabastro/ https://www.youtube.com/noirlabastro   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

When you have to go, where does it go? That's what our listener Ellie wants to know. Prepare for potty humor and science as we hear from civil engineer and toilet expert Francis de los Reyes! Who invented the toilet? And what's “The Great Stink?” We'll be plunging into the past, present, and future of flushing to discover how toilets don't just save us from stinky smells - they save lives. Are you a teacher? Participate in our NSF-funded research project! Sign up here: https://bit.ly/TumbleTeachers Learn more about toilets and sanitation around the world, on our bonus interview episode with Francis de los Reyes. It's available to Patreons who pledge just $1 a month at patreon.com/tumblepodcast. We have free resources about this episode on the blog on our website: https://www.sciencepodcastforkids.com/single-post/how-do-toilets-work All episodes have transcripts now!

In this episode of Two Bees in a Podcast, released on November 9, 2022, Dr. Jamie Ellis and Amy Vu speak with Gabriela Quinlan, an NSF postdoctoral research fellow from the department of entomology at Penn State University, on climate effects of grasslands on honey bees in the US. This episode concludes with a Q&A segment. Check out our website ufhoneybee.com for additional resources from today's episode.

Nina Fefferman is back! Today, I'm at the University of Tennessee in Knoxville. We talk about beauty and utility of math and how to get young learners excited about mathematic reasoning. We also discuss the incredible collaborative work being done at NIMBioS, which is a National Science Foundation (NSF) Synthesis Center supported through NSF's Biological Sciences Directorate via a Cooperative Agreement with the University of Tennessee. Learn more: http://www.nimbios.org/ Dr. Fefferman is a professor in the Ecology and Evolutionary Biology Department at the University of Tennessee. Her research focuses on the mathematics of epidemiology, evolutionary and behavioral ecology, and self-organizing behaviors, especially of systems described by networks. Thank you for watching and being an inquisitive being.

If you live in the United States, have you already voted? If not, go vote! The bedrock of American democracy is the idea of citizens voting for candidates to represent their interests. However, determining how to cluster voters into districts has always been a fraught topic, particularly when it comes to ensuring that minorities have representation. How can we create the most fair electoral districts? How can we use geometric topographical analysis to recognize gerrymandering strategies like “packing” and “cracking”? We brought in Tufts University mathematician and founder of the Metric Geometry and Gerrymandering Group Redistricting Lab Moon Duchin to help us discuss these pressing issues. Find our transcript here: LINK Curious to learn more? Check out these additional links: Duchin’s collaboration about ranked choice voting: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3778021 Brief review of Duchin’s scientific paper: https://www.newamerica.org/political-reform/reports/evaluating-the-effects-of-ranked-choice-voting/the-future-is-proportional-improving-minority-representation-through-new-electoral-systems-gerdus-benade-ruth-buck-moon-duchin-dara-gold-and-thomas-weighill/ Quanta’s interview with Duchin: https://www.quantamagazine.org/moon-duchin-on-fair-voting-and-random-walks-20200407/ Follow more of IMSI’s work: www.IMSI.institute, (twitter) @IMSI_institute, (instagram) IMSI.institute Follow Moon Duchin: https://math.tufts.edu/people/faculty/moon-duchin This episode was audio engineered by Tyler Damme. Music by Blue Dot Sessions. The Institute for Mathematical and Statistical Innovation (IMSI) is funded by NSF grant DMS-1929348.

In this episode of the Female Athlete Nutrition podcast, I talk with Olympian and professional rock climber Kyra Condie. We start off with a little coffee debate: home brewed or coffee shop?! We touch on the effects of caffeine on performance, both positive and negatives, and how our genes determine how we respond to caffeine. Kyra shares her start in climbing: after attending a rock climbing birthday party she was hooked! Without a school climbing team, and missing out on time with friends for training, Kyra explains how she questioned her future in the sport. Kyra discusses how surgery to correct her scoliosis reminded her of her climbing passion, and we hear how she continues to navigate challenges to overcome pain and movement limitations related to her back. We talk about climbing being introduced into the Olympics for the first time in Tokyo 2020, a competition Kyra competed in. Kyra explains the different forms of climbing: bouldering, lead, and speed climbing, and how the sport is evolving.  Climbing places a strong emphasis on strength-to-weight ratio, and disordered eating is common in this sport. Kyra speaks to the experiences she has had watching other athletes get caught up in being lighter and underfueling, and how she has learned that being stronger is better than being lighter. Kyra highlights how not all successful athletes struggle with food, showing that there is another way. Kyra credits her years of consistent injury-free training to fueling enough and not restricting. As a vegetarian athlete, Kyra shares how she is intentional about protein and supplements with creatine. We highlight the importance of ensuring all supplements are safe for sport and not contaminated with banned substances: look for “NSF” and “Informed Choice” certifications. Kyra Condie's Official Bio: Kyra is a 26 year old professional climber and University of Minnesota alumni. Kyra was born and raised in Shoreview, Minnesota, but currently lives in Salt Lake City, Utah. She started climbing when she was 11 years old at her original home gym, Vertical Endeavors in St. Paul, MN. After she had completely fallen in love with the sport of climbing, she discovered that she had severe, idiopathic scoliosis. It was devastating news, but she wouldn't let her condition hold her back. On March 12, 2010 she had a spinal fusion surgery to correct her more than 70 degree curvature and took several months to recover. Though she started competitive climbing before her surgery, she didn't win her first major competition until after her time off. Since then, she has qualified for the Tokyo 2020 Olympic Games, become a bouldering World Cup finalist, and is a regular at pro competitions around the US.  Follow Kyra on Instagram @kyra_condie and the Female Athlete Nutrition podcast @female.athlete.nutrition   THANK YOU TO OUR SPONSORS: Inside Tracker: www.insidetracker.com/riseup for 20% off the entire store for a limited time only! THIS PODCAST IS ALSO SUPPORTED BY: Practice Better Click the link below to view plans and get a 14-day free trial. Then use the code RISEUP20 for 20% off your first 4 months: https://practicebetter.grsm.io/runp This episode is brought to you by the Female Athlete System of Transformation, the FAST track for female athletes to overcome disordered eating and fuel to their highest performance: Learn more about Lindsey's Services and the Team at Rise Up Nutrition: www.RiseUpNutritionRUN.com Worried that you have RED-S? Curious to know how we could help or how you can recover fast?! Download the RED-S Recovery Race & see how you place for more support: www.RiseUpNutritionRUN.com/REDS

What's it like to be a young woman in engineering? Barbara Simpson shares some undergraduate and PhD experiences and talks about the importance of having positive, confidence-building mentors. Now a research engineer at Stanford University, she says, “This is where I was meant to be.” Ultimately, she says, diversity in engineering makes for more ideas, perspective and fun.Follow Simpson's blog: https://simpsoba.wordpress.comAnd follow her on Instagram: @simpsobaTwitter: @StanfordEng @HinsdaleOSU, #NSFStories, @NSF, @NheriEco

In 2016, Salome Maswime's five-year mid-career award from the South African Medical Research Council gave the clinician and global health researcher some much-needed funding security, enabling her to recruit staff and offer bursaries to graduate students as she established her own research group. In the United States, the National Science Foundation (NSF) offers something similar through its Mid-Career Advancement programme.Maswime and Leslie Rissler, a biologist and NSF programme director, tell Julie Gould that research outputs can easily suffer when scientists entering the mid-career stage suddenly get swamped with administrative and teaching duties, which is why the awards were set up.In the final episode of Muddle of the Middle, a six-part Working Scientist podcast, Gould also hears the pros and cons of making the mid-career stage better structured to support the development of skills and competencies, as it is in Brazil. Hosted on Acast. See acast.com/privacy for more information.

For this episode, we're joined by Thyaga Nandagopal, Division Director for the Division of Innovation and Technology Ecosystems, in the newly launched TIP Directorate at NSF. He discusses the directorate's programs, priorities, and future plans.

 This is a continuation of our conversation from Episode 27, where we introduced  current and past participants in conversations about the National Science Foundation's Alliances for Graduate Education and the Professoriate (AGEP) program. In this episode, we discussed the importance of institutional change and the scholars shared their advice for program officers and project leaders on programmatic strategies for advancing equity in the STEM professoriate and for new and prospective scholars on how to thrive as graduate students, postdoctoral scholars or early career faculty members.Our guests include:Dr. Luis De Jesus Baez, Assistant Professor of Chemistry at the University at BuffaloBrianna Gonzalez, Integrative Neuroscience Ph.D. Candidate at Stony Brook UniversityGretchen Johnson, Biology Ph.D. Candidate at Howard UniversityDiego Padilla-Garcia, Ph.D. Candidate in Psychological and Brain Sciences at the University of California Santa BarbaraDr. Lecia Robinson, Assistant Professor of Biology at Tuskegee UniversityDr. Tammi Taylor, Assistant Professor of Biology at Jackson State UniversityDr. Shavonn Whiten, Lead Scientist at Booz Allen HamiltonDr. Michael D. Whitt, Assistant Professor of Biomedical Engineering at California State University – San Luis ObispoWhen you listen, please do these three (3) things for me:Take a moment to SUBSCRIBE, leave a 5-STAR RATING, a GREAT REVIEW, and SHARE with others.Follow me and #EngineeringChangePodcast  on Twitter.Visit engineeringchangepodcast.com for more information and to connect with me.

Spaceflight News— Epsilon failure (nasaspaceflight.com) (spacenews.com) (youtu.be) (PDF: jaxa.jp)— Skyrora failure (skyrora.com) (youtube.com)— The Firefly Paradox (spacenews.com) (firefly.com) (twitter.com/seradata)— Rockets showing up at places (bbc.com) (techcrunch.com)Short & Sweet— Lucy flyby (NSF via web.archive.org) (lucy.swri.edu) (twitter.com/LucyMission)— TESS mission goes into safe mode (cnet.com)— China launched ASO-S solar observatory (nasaspaceflight.com)This Week in Spaceflight History— 2008, Launch of Chandrayaan-1 (en.wikipedia.org) (timesofindia.indiatimes.com) (eoportal.org) (pds.nasa.gov) — NASA contributed the Miniature Synthetic Aperture Radar experiment (nasa.gov)— Next week (10/25 - 10/31) in 2014: Swan song.

Onsite at the NHERI OSU wave laboratory, Dan Zehner and Barb Simpson talk about “real time hybrid simulation.” This technique joins numeric models with physical experimentation enables engineers to deal with those pesky problems of scale. In a current project, Simpson is studying wind, wave, and soil behavior at play in offshore wind turbines — which in 2016 had average hub-heights of over 330 feet.#RTHS #hybridsimulation #windturbine #SSI #SFI #womeninengineering #windengineeringLinkedIn: https://www.linkedin.com/in/barbara-simpson-9255445b/Twitter: @StanfordEng @HinsdaleOSU, #NSFStories, @NSF, @NheriEco 

Matt Price is the co-founder and President of Activate, a program teaching scientists how to bring their research to market. He was previously the Managing Director of Cyclotron Road, and comes with experiences in the  Renewable Energy space , Materials Science, and Venture Capital industry. In this episode of lab to startup, we talk about the Activate fellowship program, its origins, especially how they navigated carving out a niche but much needed space for supporting scientists to build startups. We will discuss details about the application process, challenges that the fellows generally face in this space, the support they provide, and what they actually look for in an idea or a founder trying to commercialize research. We will then find out more about the recently announced NSF fellowships for scientists and engineers in collaboration with the Activate fellowship program. Shownotes https://www.activate.org - What is Activate? - Origin story and Cyclotron Road - Business model of Activate vs incubators/accelerators - How VC funding model might not be appropriate for scientific entrepreneurs exploring technology translation - Activate fellows receive money, time and space t find out what kind of capital will help them build a company - Solving the brain drain problem - Types of industries covered - Activate fellowship: application process and selection criteria, timelines, fellowship amounts, and other benefits the program offers. - What success looks like: Economic value created; understanding market dynamics-capture value; identify the right source of capital - What they look for in an idea/founder - Scratching the intellectual itch - Activator readiness level - NSF Fellowships: https://beta.nsf.gov/tip/updates/nsf-launches-entrepreneurial-fellowship-engineers - Hit milestones and receive funding from partner VCs - Delivering intimacy at scale - Apply to the program: https://www.activate.org/apply

I welcome Angel Muniz, a 3D Generalist and Technical Artist who shares about her transition into the game industry, talking with Bungie, getting her first job, and going back to school. Hear career advice about how the art field is changing, creating automation, learning integration, and getting your first position. Learn about knowing your value, having mentors, passion & curiosity, and advancing your career. We then discuss hackathons, growing your skills, her VR portfolio, and her love of learning. Hear about emotional intelligence, politics, listening, bodystorming, and how we process information. Discover AI machine learning, building an experience to help with anxiety, XR, wearables, aerospace, and NASA.  Learn about integrating XR into education, roles outside of triple-A, different projects she's worked on, challenges around hiring, toxic environments, and thoughts on being neurodivergent. Hear about overwhelming job descriptions, changing skills, the metaverse, wantrepreneurs, reading the room, and not being critical when playing games.  Towards the end we talk about networking, connecting with the IGDA, Global Game Jam, the value of GDC, and the game dev community in Houston. Hear about games like Graveyard Keeper, kind words for this podcast, where to connect online, new tech battles, keeping your skills sharp, her mentor's advice on making progress each day, and info on my YouTube presentation about careers outside of traditional game development. Bio: Angel Muniz is originally from Texas, a former NASA JSC - Animation & Graphics Lab 3D Intern, who worked on growing the game industry in Houston —mainly in the XR field. Angel served as a Co-PI for a National Science Foundation Grant & Adjunct Professor for the Dynamic Reality Technology program (AAS in XR) at Coastal Alabama College. With a background in public speaking for high-performance art/PBR & optimization, she was a keynote speaker for the University of Pennsylvania, a guest lecturer at the University of Houston - Computer Science, Microsoft's HoloAcademy in Redmond, Immersive Tech Conference, IGDA, and more. For over 7 years, Angel has worked as a 3D Generalist and Technical Artist - CGI for Amazon AWS, TenGun, TYM Studio, Practical VR, Ong Innovations, and served as a consultant to many more. She's been a professional mentor for the game developer community co-creating and participating in boot camps, hackathons, and one-on-one sessions with artists and developers. She is currently building an application in XR and plans to apply for her 2nd NSF grant to fund her research in behavioral health, communication, and technology. Angel is an advocate for neurodiverse individuals in the field as she continues her work in the game industry. She is an avid learner and seeks to build and utilize new tools in automation for game art in the industry. Show Links: * Substance Designer website * SMU Guildhall website * Digipen website * NSF Grants website * Rain on Your Parade Steam * IGDA website * Global Game Jam website * GDC website * Graveyard Keeper website * The Survivalists Steam * Detroit: Become Human Wikipedia * Joowon Kim SMU Guildhall * Careers Options Beyond Traditional Game Development me on YouTube   Connect Links: *Angel Muniz LinkedIn *Angel Muniz website Game Dev Advice Links:  * Game Dev Advice Patreon: please help support the show if you find it useful * Game Dev Advice Twitter * Game Dev Advice email: info@gamedevadvice.com * Game Dev Advice website * Level Ex Careers: I'm hiring lots of roles! * Game Dev Advice hotline: (224) 484-7733 * Subscribe and go to the website for full show notes with links Learn more about your ad choices. Visit podcastchoices.com/adchoices

The National Science Foundation's (NSF's) Alliances for Graduate Education and the Professoriate (AGEP) program funds teams of institutions that work together to identify, mitigate, and ultimately eradicate, systemic barriers to success for STEM faculty from racially and ethnically minoritized identities while studying, disseminating, and reproducing those strategies to produce large-scale, sustainable change.  This episode features current and past participants in conversations about AGEP program impacts on their success. We discussed the importance of having strong mentoring networks at all stages of our journeys; they shared components of AGEP projects that have translated into professional practice and tangible outcomes for them; and we discussed how AGEP projects foster a sense of community that helps scholars succeed. Our guests include:Dr. Luis De Jesus Baez, Assistant Professor of Chemistry at the University at BuffaloBrianna Gonzalez, Integrative Neuroscience Ph.D. Candidate at Stony Brook UniversityGretchen Johnson, Biology Ph.D. Candidate at Howard UniversityDiego Padilla-Garcia, Ph.D. Candidate in Psychological and Brain Sciences at the University of California Santa BarbaraDr. Lecia Robinson, Assistant Professor of Biology at Tuskegee UniversityDr. Tammi Taylor, Assistant Professor of Biology at Jackson State UniversityDr. Shavonn Whiten, Lead Scientist at Booz Allen HamiltonDr. Michael D. Whitt, Assistant Professor of Biomedical Engineering at California State University – San Luis ObispoWhen you listen, please do these three (3) things for me:Take a moment to SUBSCRIBE, leave a 5-STAR RATING, a GREAT REVIEW, and SHARE with others.Follow me and #EngineeringChangePodcast  on Twitter.Visit engineeringchangepodcast.com for more information and to connect with me.

The Dark Energy Camera (DECam) is an extremely powerful imager mounted on the Victor M. Blanco 4-meter Telescope at Cerro Tololo Inter-American Observatory in Chile. DECam has 570 megapixels that cover 3 square degrees of the sky. In this podcast, Alistair Walker describes the history of the Dark Energy Camera, its first 10 years in operation, and looks to what the future holds. Bios:  - Rob Sparks is in the Communications, Education and Engagement group at NSF's NOIRLab in Tucson, Arizona.. - Dr. Alistair Walker is a staff astronomer and former director of Cerro Tololo Inter-American Observatory in Chile. He earned his PHD at the University of Cape Town and has been at CTIO since 1987. His research interests include Galaxy and Local Group Stellar Populations, the Distance Scale, and Near-Field Cosmology.   Links:  NOIRLab social media channels can be found at https://www.facebook.com/NOIRLabAstro https://twitter.com/NOIRLabAstro https://www.instagram.com/noirlabastro/ https://www.youtube.com/noirlabastro   We've added a new way to donate to 365 Days of Astronomy to support editing, hosting, and production costs.  Just visit: https://www.patreon.com/365DaysOfAstronomy and donate as much as you can! Share the podcast with your friends and send the Patreon link to them too!  Every bit helps! Thank you! ------------------------------------ Do go visit http://www.redbubble.com/people/CosmoQuestX/shop for cool Astronomy Cast and CosmoQuest t-shirts, coffee mugs and other awesomeness! http://cosmoquest.org/Donate This show is made possible through your donations.  Thank you! (Haven't donated? It's not too late! Just click!) ------------------------------------ The 365 Days of Astronomy Podcast is produced by the Planetary Science Institute. http://www.psi.edu Visit us on the web at 365DaysOfAstronomy.org or email us at info@365DaysOfAstronomy.org.

USPS sorting and delivery consolidation plan Ivan Butts, president of the National Association of Postal Supervisors, discusses how the U.S. Postal Service's plans to consolidate mail processing operations across the country could impact postal workers and supervisors   Accelerating US biotechnology Theresa Good, division director for NSF's Directorate for Biological Sciences, discusses a new executive order for advancing biotechnology and biomanufacturing and the National Science Foundation's efforts in this area   Writing the Space Force's service song Jamie Teachenor, songwriter of Space Force's ‘Semper Supra,' discusses his process of composing the Space Force's new, official service song that was unveiled Sept. 20

Sugar Lab is billed as the world's first digital bakery offering a fanciful array of sweet and savory treats all produced on 3D printers. On this episode of Tech Bites, host Jennifer Leuzzi talks with Sugar Lab founder and CEO Kyle von Hasseln about inventing the technology as an architect student a decade ago and how that led to his building the only NSF-certified commercial-scale 3D food printer. Working with a team of traditional pastry chefs and digital designers, Sugar Lab is inventing new confections and ways to disrupt commercial food manufacturing.Photo Courtesy of Sugar Lab.Heritage Radio Network is a listener supported nonprofit podcast network. Support Tech Bites by becoming a member!Tech Bites is Powered by Simplecast.

Welcome to the podcast Elaine Aguayo, a masters student studying bioarchaeology at San Diego State University. In this episode, we discuss how she fell in love with anthropology, her first field experience where we met, as well as her advice for community college students looking to transfer to a four-year university. The main topic of discussion is the fieldwork she has been conducting for the last 5 months on an NSF funded project in Oaxaca Mexico, along with her advisor. She explains her experience as a Mexican-American working in Mexico, and the highlights from this excavation. We also discuss the theoretical framework of her thesis, and the general questions she and her advisor were investigating at the site. Lastly, we discuss her goals for after graduate school, with her ultimate goal being to work as a forensic anthropologist on the Mexican border. --- Support this podcast: https://anchor.fm/gabby-campbell1/support

JAHON HOBBEHEYDAR | Jahon Hobbeheydar, Chief Strategy and Innovation Officer at NSF International, joins host Gregg Garrett to discuss the linkage of strategy and innovation. As someone who lives in the center of these two functions, Jahon leads NSF to the future. Of course, Jahon speaks about his Top 3: his former boss, Thad, who helped him bring out his intellectual best, a persona of colleagues like Sharon, Tom, and Jen who help remove cultural inertia, and his daughter, Sarah, who makes him remember the underlying “why”. And you have to hear what Jahon has to say about shifting from making points to understanding them. 

This episode of All About Fitness picks up in chapter 1 Smarter Workouts: The Science of Exercise Made Simple where you will learn how exercise influences change in your muscles. Chapter 1 covers how exercise changes the body; it's important understand that exercise is a physical stress imposed upon the body and that different types of stress cause different reactions in your muscles. There are two specific types of stress stimulus that cause muscle growth: metabolic and mechanical overload; in this section of Smarter Workouts you will learn what those are and how they change your muscles. Finally, your muscles contain different types of muscle fibers which themselves respond to various types of exercise. If you know how your body responds to exercise, then you will know the best types of exercise that are right for your body. Gnarly Nutrition's products are designed to help you to achieve your fitness goals with their clean, effective and delicious supplements which are free from GMOs and NSF certified. Use code AAFitness to save 15% off of your order! https://gognarly.com Learn how to design your own workouts; order a copy of Smarter Workouts: the Science of Exercise Made Simple https://amzn.to/3dBVfg4 Learn how exercise can slow the aging process and allow you to add YEARS to your life with Ageless Intensity: High Intensity Workouts to Slow the Aging Process https://amzn.to/3eYieSC Learn more about your ad choices. Visit megaphone.fm/adchoices

Today we have an exciting meet-up with Stanford-based structural engineer Barbara Simpson — in person at the Oregon State University Wave Laboratory, a NHERI experimental facility. Simpson's passionate about building computer programming skills in undergraduate engineers, particularly those in underrepresented groups who may lack such experience. She applies parallel processing in her own investigations in soil-to-fluid interactions in floating offshore wind turbines.Read more about Simpson's work training undergraduate engineers in programming:https://stem.oregonstate.edu/people/barbara-simpsonOn her blog, Simpson discusses using computational and experimental methods to solve problems in natural hazards engineering:https://simpsoba.wordpress.com/LinkedIn: https://www.linkedin.com/in/barbara-simpson-9255445b/Twitter: @StanfordEng @HinsdaleOSU, #NSFStories, @NSF, @NheriEco

Christina Katopodis, PhD, is a Postdoctoral Research Associate and the Associate Director of Transformative Learning in the Humanities, a three-year initiative at the City University of New York (CUNY) supported by the Mellon Foundation. She is the winner of the 2019 Diana Colbert Innovative Teaching Prize and the 2018 Dewey Digital Teaching Award. She has authored or co-authored articles published in ESQ: A Journal of Nineteenth-Century American Literature and Culture, ISLE: Interdisciplinary Studies in Literature and Environment, MLA's Profession, Hybrid Pedagogy, Inside Higher Ed, Synapsis, and Times Higher Ed.The learning process is something you can incite, really incite, like a riot. - Audre LordeCathy N. Davidson is the Senior Advisor on Transformation to the Chancellor of the City University of New York (CUNY), a role which includes work with all twenty-five campuses serving over 500,000 students. She is also the Founding Director of the Futures Initiative and Distinguished Professor of English, as well as the M.A. in Digital Humanities and the M.S. in Data Analysis and Visualization programs at the Graduate Center (CUNY). The author or editor of over twenty books, she has taught at a range of institutions, from community college to the Ivy League. She held two distinguished professor chairs at Duke University, where she taught for twenty-five years and also became the university's (and the nation's) first Vice Provost for Interdisciplinary Studies. She is cofounder and codirector of “the world's first and oldest academic social network,” the Humanities, Arts, Science, and Technology Alliance and Collaboratory (HASTAC.org, known as “Haystack”). Founded in 2002, HASTAC has over 18,000 network members.Davidson's many prizewinning books include the classics Revolution and the Word: The Rise of the Novel in America and Closing: The Life and Death of an American Factory (with photographer Bill Bamberger). Most recently, she has concentrated on the science of learning in the “How We Know” Trilogy: Now You See It: How the Brain Science of Attention Will Transform the Way We Live, Work, and Learn; The New Education: How to Revolutionize the University to Prepare Students for a World in Flux; and, co-authored with Christina Katopodis, The New College Classroom (due August 2022).Davidson has won many awards, prizes, and grants throughout her career including from the Guggenheim Foundation, ACLS, NEH, NSF, the MacArthur Foundation, and others. She is the 2016 recipient of the Ernest L. Boyer Award for “significant contributions to higher education.” She received the Educator of the Year Award (2012) from the World Technology Network and, in 2021, the Council of Colleges of Arts and Sciences presented Davidson with its annual Arts and Sciences Advocacy Award. She has served on the board of directors of Mozilla, was appointed by President Barack Obama to the National Council on the Humanities, and has twice keynoted the Nobel Prize Committee's Forum on the Future of Learning. She lives in New York City. Hosted on Acast. See acast.com/privacy for more information.

Smarter Workouts: The Science of Exercise Made Simple was written to help you understand how exercise changes your body and the most effective exercise solutions to help enhance your quality of life. Chapter 1 of Smarter Workouts is titled, "How Exercise Changes Your Body" and organizes exercise into three main categories: force production (which includes the entire spectrum of endurance, strength and power), mobility and metabolic conditioning. Muscles control physical force in your body; when a muscle contracts it produce a force to move an object, when a load is heavier than a muscle can control, it responds to that external force by lengthening. In the first part of How Exercise Changes Your Body, you will start learning about how your muscles work and the most effective ways to use exercise to make them more efficient at generating force. Tip: form follows function; helping your muscles to perform more effectively is the first step towards helping them look their best. Gnarly Nutrition's products are designed to help you to achieve your fitness goals with their clean, effective and delicious supplements which are free from GMOs and NSF certified. Use code AAFitness to save 15% off of your order! https://gognarly.com Learn how to design your own workouts; order a copy of Smarter Workouts: the Science of Exercise Made Simple https://amzn.to/3dBVfg4 Learn how exercise can slow the aging process and allow you to add YEARS to your life with Ageless Intensity: High Intensity Workouts to Slow the Aging Process https://amzn.to/3eYieSC Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode Garth interviewed Jordan Wagge from Avila University in Kansas City, MO.  Jordan is one of the international leaders in the Collaborative Replications and Education Project (CREP), with her focus on projects through a social justice, diversity, and sustainability lens. These interests may harken back to undergraduate days, when she started as a political science major before switching to psychology. She specializes in teaching statistics and research methods (to both undergraduate and graduate students), and recently, she's been examining confirmation bias in one's teaching.  Jordan's habits pay off -- whether it's a preference for multi-tasking so that she can double-dip, or being persistent, as in her recent award of an NSF grant after three submissions over five years.

For this episode, host Sadie Witkowski goes a bit off-script. Sadie is joined by sports reporter Jon Zaghloul to interview rookie Cubs pitcher and applied math University of Chicago undergraduate Wilson Cunningham. This conversation ranges from interests in mathematics and statistics to applying those statistics to baseball. Find our transcript here: LINK Curious to learn more? Check out these additional links: University of Chicago’s story about Wilson’s baseball career: https://news.uchicago.edu/story/cubs-prospect-pitcher-wilson-cunningham-balances-rigorous-uchicago-college-education-baseball-training Chicago Tribune’s story about Wilson’s baseball career: https://www.chicagotribune.com/news/ct-university-chicago-cubs-baseball-rookie-pitcher-20220723-66rzhlqhuredta67nchmfcg7xy-story.html Discover more about UChicago’s Computational and Applied Mathematics Program: https://cam.uchicago.edu/ Hear more of Jon’s reporting: https://linktr.ee/jonzsports Follow more of IMSI’s work: www.IMSI.institute, (twitter) @IMSI_institute, (instagram) IMSI.institute Follow Wilson Cunningham: @wils_cunningham Follow Jon Zaghloul: @JonZSports This episode was audio engineered by Tyler Damme. Music by Blue Dot Sessions. The Institute for Mathematical and Statistical Innovation (IMSI) is funded by NSF grant DMS-1929348.

Canary Cry News Talk #539 - 09.26.2022 - Recorded Live to Tape! MU POO HEAD - Russian Savior, Royal Robot, UFO Logo, Hive Mind, Xi Op, Ozone Champ  Harvard: Index of MSM Ownership (Harvard.edu)   SHOW NOTES Podcast = T- 2:57   Timestamps by Jade Bouncerson - From D-Live HELLO, RUN DOWN 4:59 V / 2:02 P   RUSSIA 6:18 V / 3:21 P R36th Anniversary of Russian Who Saved the World (Vox)   DAY/PERSONAL/EXEC.  20:28 V / 17:31 P   FLIPPY 37:52 V / 34:55 P Deranged Scheme Proposes Mounting Queen's Coffin on Robot Arm (Futurism)   CHINA 46:33 V / 43:36 P *China bets big on brain research with massive cash infusion, monkey studies (Science)  → Scientists at US's top nuclear lab recruited by China to design missiles and drones (NBC News)  *Xi Jinping Trends Online Amid Coup Rumors (Newsweek) [origin, Indian politician tweet] → Here's how rumors of Xi Jinping's ‘arrest' and ‘coup' started (Print) [origin, Chinese defect] → China coup rumors quashed by state media (AsiaMarket) [cancelled flights/trains note]   PARTY TIME 1:13:15 V / 1:10:18 P BREAK 1: TREASURE    COVID 1:32:41 V / 1:29:44 P Pfizer's chief executive tests positive for C19 for second time in two months (Sky News) PELOSI 1:36:13 V / 1:33:16 P Nancy Pelosi booed during surprise appearance at NYC festival (Fox News) UFO 1:44:18 V / 1:41:21 P  Sharp-Eyed Viewers Notice Stunning Apparent Addition To Key Intelligence Logo (HuffPo) Source: New Director of Nat'l Intel Logo has UFO (AirDomainIntelligence.mil) → You can't have .mil address unless you are part of defense department (is this true?)   HIVE MIND Moved to next show We are building a “species-level brain” with big data and ubiquitous sensors (Big Think)   BREAK 3: TALENT 1:52:15 V / 1:49:18 P   ANTARCTICA 2:09:36 V / 2:06:39 P We did it! Harmful chemicals in ozone layer drop by 50%, NOAA says (Interesting Engineering) NANC connects to neXat's at Ukrainian Antarctic base (RealWire)   BREAK 4: TIME 2:29:37 V / 2:26:40 P END       This Episode was Produced By: EXECUTIVE PRODUCERS RAB** ep538** Felicia D**   Producers Malik W, Sir MORV Knight of the Burning Chariots, Sir JC Knight of the Technosquatch, Sir LX Protocol V2 Knight of the Berrean Protocol, Sir Casey the Shield Knight, Gail M, Veronica D, Sir Darrin Knight of the Hungry Panda's, Runksmash, Sir Scott Knight of Truth   Audio Production Jonathan F Kalub James M   Visual Art Dame Allie of the Skillet Nation Sir Dove Knight of Rusbeltia   CLIP PRODUCER Emsworth, FaeLivrin, Epsilon   TIMESTAPERS Jackie U, Jade Bouncerson, Christine C, Pocojoyo, Joelle S   SOCIAL MEDIA DOERS Dame MissG of the OV and Deep Rivers   LINKS HELP JAM   MICROFICTION Runksmash - Stacy pulls Derwin off of the two bafoons and notices he's extremely hot, the ink circuits that drive him char through his paper skin, before he passes he utters in a deep voice, “Another has been printed…” then in a flash he is not but dust and ash.   ADDITIONAL STORIES Magic seeds: Bill Gates biotechnological innovation critical to produce crops (GLP)  The Future of Progress (Gates Foundation)  (Video) Simulation of US and Russia Nuclear War (YouTube)  Ukraine, EU's dictatorship and change of global order: Lavrov speaks at UNGA (RT) You really don't understand how bad it could get in Europe this year (Fortune)  The American right's future involves waging a 'religious battle' against the left (NBC News)  Fault along L.A., O.C. coast could unleash huge earthquake, study shows (LA Times)  US Psy-Ops exposed: Washington worried about getting caught (RT)  NASA will intentionally crash a spacecraft into an asteroid at 15,000mph on MONDAY – and the method could one day save Earth from a deadly impact (DailyMail)  DART Sets Sights on Asteroid Target (NASA)  Massive sinkholes leave two missing and three injured in Guatemala (abc AU) Robots Are Taking Over Chinese Factories (Forbes) Nancy Pelosi booed during surprise appearance at NYC festival (Fox News)  Pfizer's chief executive tests positive for COVID-19 for second time in two months (Sky News)  USPS spied on ‘MAGA', right-wing, gun rights activists, docs show (American Military News)  DOJ Admits Federal Gov Ran Informants Inside Oath Keepers, Jan. 6 (Gateway Pundit)  Flesh-eating 'Tranq dope' Xylazine accounts for a third of OD deaths in Philadelphia (DailyMail) Chinese Drones in Antarctica creating Museum (Air and Space) BYU biology prof receives 3-yr NSF grant to research in Antarctica (Daily Universe) The art shrines buried deep inside of the South Pole Ice Tunnels (Boing Boing) China may now have air superiority over US in Pacific (Asian Times)  Scientists Studying Octopus arm for better Robot Arm (Newsweek)

Order the Leading Equity Book Today! Kelly J. Cross, Ph.D. Dr. Kelly Cross, Assistant Professor of Chemical Engineering at University of Nevada Reno, is a data-informed, transformational mission-focused culturally responsive practitioner, researcher, and educational leader. She earned her Bachelors of Science in Chemical Engineering from Purdue University in 2007 and Masters of Science in Materials Science and Engineering from the University of Cincinnati in 2011. Cross completed her doctoral program in the Engineering Education department at Virginia Tech in 2015 and worked as a post-doctoral researcher with the Illinois Foundry for Innovation in Engineering Education at the University of Illinois at Urbana-Champaign (UIUC). Dr. Cross worked in the Department of Bioengineering working to redesign the curriculum through the NSF funded Revolutionizing Engineering Departments (RED) grant. She is a member of the ASEE Leadership Virtual Community of Practice (LVCP) that organizes and facilitates Safe Zone Training workshops. Dr. Cross has conducted workshops on managing personal bias in STEM, online and in-person, in addition to faculty training on power and privilege. Her research interests include diversity and inclusion in STEM, intersectionality, teamwork and communication skills, assessment, and identity construction. Her teaching philosophy focuses on student centered approaches such as problem-based learning and culturally relevant pedagogy. Dr. Cross' complimentary professional activities promote inclusive excellence through collaboration. She is an NSF CAREER awardee, delivered multiple distinguished lectures, and has received a national mentoring award. Show Highlights DEI support for queer individuals in STEM Engineering Education and the Queer Community Faculty and staff support Connect with Kelly Email Cross, K.J., Farrell, S. & Hughes, B. (Ed.). (2022). Queering STEM Culture in US Higher Education: Navigating Experiences of Exclusion in the Academy, published by Routledge an imprint of Taylor & Francis Group, LLC. eBook: 978-1-003-16925-3 Cross, K.J., Gaskins, W.B., & Coley, B.C (2022) We Cannot Address What We Do Not Acknowledge: An Autoethnography in 2020. Special Issue titled "All #BlackLivesMatter" by Understanding & Dismantling Privilege (UDP), (ISSN 2152-1875), Volume XII, Issue 1. www.wpcjournal.com. Duckworth, M & Cross, K.J. (2022). Antiracist Holistic Change in “STEM” Higher Education. In Ruth, B.J., Blithe, S. & Bauer, J. Badass Feminist Politics, published by Rutgers University Press. rutgersuniversitypress.org Jensen, K. & Cross, K. (2021). Engineering Stress Culture: Relationships between Mental Health, Engineering Identity, and Sense of Inclusion. Journal of Engineering Education. http://doi.org/10.1002/jee.20391 *Cross, K.J. (2020), Racism is the manifestation of White supremacy and antiracism is the answer. J Eng Educ. doi:10.1002/jee.20362 (Top downloaded JEE article)  Additional Resources Book Dr. Eakins Watch The Art of Advocacy Show Learn more about the Advocacy Room Free Course on Implicit Bias 20 Diversity Equity and Inclusion Activities FREE AUDIO COURSE: Race, Advocacy, and Social Justice Studies

We discuss each of the three categories of risk identified in the guidance (consumer compliance, third-party, and litigation), plaintiffs' legal theories in class actions challenging NSF fees, the role of arbitration clauses and contract language in defending class actions, the FDIC's suggested risk mitigation practices, issues to consider in navigating FDIC expectations for remediation of self-identified NSF fee issues and plaintiffs' demands in class action settlements, the position of other federal regulators on re-presentment NSF fees, and state focus on NSF fees. Alan Kaplinsky, Ballard Spahr Senior Counsel, hosts the conversation joined by Martin Bryce, a partner and senior litigator in the firm's Consumer Financial Services Group, and Kristen Larson, Of Counsel in the CFS Group.

Do you want to know how to design your own workout program? Do you want to learn the most effective exercises for your body? Do you want to understand the science of how exercise changes your body? All About Fitness is back from a brief hiatus with a little twist, before returning to our interview format, we're going to do a book-on-tape; specifically my first book, Smarter Workouts: The Science of Exercise Made Simple.  In this series, I will be reading from the book and providing commentary on how to apply the information to your own workouts. If you have a copy of Smarter Workouts, you can follow along and take notes. In a 20 year career as a fitness educator, I have worked with national certifications and equipment companies to educate thousands of personal trainers all over the world; by listening to this series and learning the information in Smarter Workouts, you too will have the same foundation of knowledge as a certified personal trainer (no, you will not be certified as a personal trainer, but you will know enough to become one). The series starts where all should, at the beginning; this episode is the introduction of Smarter Workouts which identifies common exercise mistakes that you are probably making and what you can expect to learn from the book. Gnarly Nutrition's products are designed to help you to achieve your fitness goals with their clean, effective and delicious supplements which are free from GMOs and NSF certified. Use code AAFitness to save 15% off of your order! https://gognarly.com Learn how to design your own workouts; order a copy of Smarter Workouts: the Science of Exercise Made Simple https://amzn.to/3dBVfg4 Learn how exercise can slow the aging process and allow you to add YEARS to your life with Ageless Intensity: High Intensity Workouts to Slow the Aging Process https://amzn.to/3eYieSC Learn more about your ad choices. Visit megaphone.fm/adchoices

Augmented reveals the stories behind the new era of industrial operations, where technology will restore the agility of frontline workers. The topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati (https://www.iaicenter.com/). In this conversation, we talk about how AI does many things but to be applicable; the industry needs it to work every time, which puts additional constraints on what can be done by when. If you liked this show, subscribe at augmentedpodcast.co (https://www.augmentedpodcast.co/). If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation (https://www.augmentedpodcast.co/81). Augmented is a podcast for industry leaders, process engineers, and shop floor operators, hosted by futurist Trond Arne Undheim (https://trondundheim.com/) and presented by Tulip (https://tulip.co/). Follow the podcast on Twitter (https://twitter.com/AugmentedPod) or LinkedIn (https://www.linkedin.com/company/75424477/). Trond's Takeaway: Industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Transcript: TROND: Welcome to another episode of the Augmented Podcast. Augmented brings industrial conversations that matter, serving up the most relevant conversations on industrial tech. Our vision is a world where technology will restore the agility of frontline workers. In this episode of the podcast, the topic is Industrial AI. Our guest is Professor Jay Lee, the Ohio Eminent Scholar, and the L.W. Scott Alter Chair Professor in Advanced Manufacturing, and the Founding Director of the Industrial AI Center at the University of Cincinnati. In this conversation, we talk about how AI does many things but to be applicable, industry needs it to work every time, which puts on additional constraints on what can be done by when. Augmented is a podcast for industrial leaders, process engineers, and shop floor operators hosted by futurist Trond Arne Undheim and presented by Tulip. Jay, it's a pleasure to have you here. How are you today? JAY: Good. Thank you for inviting me to have a good discussion about industrial AI. TROND: Yeah, I think it will be a good discussion. Look, Jay, you are such an accomplished person, both in terms of your academics and your industrial credentials. I wanted to quickly just go through where you got to where you are because I think, especially in your case, it's really relevant to the kinds of findings and the kinds of exploration that you're now doing. You started out as an engineer. You have a dual degree. You have a master's in industrial management also. And then you had a career in industry, worked at real factories, GM factories, Otis elevators, and even on Sikorsky helicopters. You had that background, and then you went on to do a bunch of different NSF grants. You got yourself; I don't know, probably before that time, a Ph.D. in mechanical engineering from Columbia. The rest of your career, and you correct me, but you've been doing this mix of really serious industrial work combined with academics. And you've gone a little bit back and forth. Tell me a little bit about what went into your mind as you were entering the manufacturing topics and you started working in factories. Why have you oscillated so much between industry and practice? And tell me really this journey; give me a little bit of specifics on what brought you on this journey and where you are today. JAY: Well, thank you for talking about this career because I cut my teeth from the factory early years. And so, I learned a lot of fundamental things in early years of automation. In the early 1980s, in the U.S, it was a tough time trying to compete with the Japanese automotive industry. So, of course, the Big Three in Detroit certainly took a big giant step, tried to implement a very good manufacturing automation system. So I was working for Robotics Vision System at that time in New York, in Hauppage, New York, Long Island. And shortly, later on, it was invested by General Motors. And in the meantime, I was studying part-time in Columbia for my mechanical engineering, Doctor of Engineering. And, of course, later on, I transferred to George Washington because I had to make a career move. So I finished my Ph.D. Doctor of Science in George Washington later. But the reason we stopped working on that is because of the shortage of knowledge in making automation work in the factory. So I was working full-time trying to implement the robots automation in a factory. In the meantime, I also found a lack of knowledge on how to make a robot work and not just how to make a robot move. Making it move means you can program; you can do very fancy motion. But that's not what factories want. What factories really want is a non-stop working system so they can help people to accomplish the job. So the safety, and the certainty, the accuracy, precision, maintenance, all those things combined together become a headache actually. You have to calibrate the robot all the time. You have to reprogram them. So eventually, I was teaching part-time in Stony Brook also later on how to do the robotic stuff. And I think that was the early part of my career. And most of the time I spent in factory and still in between the part-time study and part-time working. But later on, I got a chance to move to Washington, D.C. I was working for U.S. Postal Service headquarters as Program Director for automation. In 1988, post service started a big initiative trying to automate a 500 mil facility in the U.S. There are about 115 number one facilities which is like New York handled 8 million mail pieces per day at that time; you're talking about '88. But most are manual process, so packages. So we started developing the AI pattern recognition, hand-written zip code recognition, robotic postal handling, and things like that. So that was the opportunity that attracted me actually to move away from automotive to service industry. So it was interesting because you are working with top scientists from different universities, different companies to make that work. So that was the early stage of the work. Later on, of course, I had a chance to work with the National Science Foundation doing content administration in 1991. That gave me the opportunity to work with professors in universities, of course. So then, by working with them, I was working on a lot of centers like engineering research centers and also the Industry-University Cooperative Research Centers Program, and later on, the materials processing manufacturing programs. So 1990 was a big time for manufacturing in the United States. A lot of government money funded the manufacturer research, of course. And so we see great opportunity, like, for example, over the years, all the rapid prototyping started in 1990s. It took about 15-20 years before additive manufacturing came about. So NSF always looks 20 years ahead, which is a great culture, great intellectual driver. And also, they're open to the public in terms of the knowledge sharing and the talent and the education. So I think NSF has a good position to provide STEM education also to allow academics, professors to work with industry as well, not just purely academic work. So we support both sides. So that work actually allowed me to understand what is real status in research, in academics, also how far from real implementation. So in '95, I had the opportunity to work in Japan actually. I had an opportunity...NSF had a collaboration program with the MITI government in Japan. So I took the STA fellowship called science and technology fellow, STA, and to work in Japan for six months and to work with 55 organizations like Toyota, Komatsu, Nissan, FANUC, et cetera. So by working with them, then you also understand what the real technology level Japan was, Japanese companies were. So then you got calibration in terms of how much U.S. manufacturing? How much Japanese manufacturing? So that was in my head, actually. I had good weighting factors to see; hmm, what's going on here between these two countries? That was the time. So when I came back, I said, oh, there's something we have to do differently. So I started to get involved in a lot of other things. In 1998, I had the opportunity to work for United Technologies because UTC came to see me and said, "Jay, you should really apply what you know to real companies." So they brought me to work as a Director for Product Environment Manufacturing Department for UTRC, United Technology Research Center, in East Hartford. Obviously, UTC business included Pratt & Whitney jet engines, Sikorsky helicopters, Otis elevators, Carrier Air Conditioning systems, Hamilton Sundstrand, et cetera. So all the products they're worldwide, but the problem is you want to support global operations. You really need not just the knowledge, what you know, but also the physical usage, what you don't know. So you know, and you don't know. So how much you don't know about a product usage, that's how the data is supposed to be coming back. Unfortunately, back in 1999, I have to tell you; unfortunately, most of the product data never came back. By the time it got back, it is more like a repair overhaul recur every year to a year later. So that's not good. So in Japan, I was experimenting the first remote machine monitoring system using the internet actually in 1995. So I published a paper in '98 about how to remotely use physical machine and cyber machine together. In fact, I want to say that's the first digital twin but as a cyber-physical model together. That was in my paper in 1998 in Journal of Machine Tools and Manufacture. TROND: So, in fact, you were a precursor in so many of these fields. And it just strikes me that as you're going through your career here, there are certain pieces that you seem to have learned all along the way because when you are a career changer oscillating between public, private, semi-private, research, business, you obviously run the risk of being a dilettante in every field, but you seem to have picked up just enough to get on top of the next job with some insight that others didn't have. And then, when you feel like you're frustrated in that current role, you jump back or somewhere else to learn something new. It's fascinating to me because, obviously, your story is longer than this. You have startup companies with your students and others in this business and then, of course, now with the World Economic Forum Lighthouse factories and the work you've been doing for Foxconn as well. So I'm just curious. And then obviously, we'll get to industrial AI, which is so interesting in your perspective here because it's not just the technology of it; it is the industrial practice of this new domain that you have this very unique, practical experience of how a new technology needs to work. Well, you tell me, how did you get to industrial AI? Because you got there to, you know, over the last 15-20 years, you integrated all of this in a new academic perspective. JAY: Well, that's where we start. So like I said earlier, I realized industry we did not have data back in the late 1990s. And in 1999, dotcom collapsed, remember? TROND: Yes, yes. JAY: Yeah. So all the companies tried to say, "Well, we're e-business, e-business, e-commerce, e-commerce," then in 2000, it collapsed. But the reality is that people were talking about e-business, but in the real world, in industrial setting, there's no data almost. So I was thinking, I mean, it's time I need to think about how to look at data-centric perspectives, how to develop such a platform, and also analytics to support if one-day data comes with a worry-free kind of environment. So that's why I decided to transition to an academic career in the year 2000. So what I started thinking, in the beginning, was where has the most data? As we all know, the product lifecycle usage is out there. You have lots of data, but we're not collecting it. So eventually, I called a central Intelligent Maintenance System called IMS, not intelligent manufacturing system because maintenance has lots of usage data which most developers of a product don't know. But if we have a way to collect this data to analyze and predict, then we can guarantee the product uptime or the value creation, and then the customer will gain most of the value back. Now we can use the data feedback to close-loop design. That was the original thinking back in the year 2000, which at that time, no cell phone could connect to the internet. Of course, nobody believed you. So we used a term called near-zero downtime, near-zero downtime, ZDT. Nobody believed us. Intel was my first founding member. So I made a pitch to FANUC in 2001. Of course, they did not believe it either. Of course, FANUC in 2014 adopted ZDT, [laughs] ZDT as a product name. But as a joke, when I talked to the chairman, the CEO of the company in 2018 in Japan, Inaba-san that "Do you know first we present this ZDT to your company in Michigan? They didn't believe it. Now you guys adopted." "Oh, I didn't know you use it." So when he came to visit in 2019, they brought the gift. [laughs] So anyway, so what happened is during the year, so we worked with the study of 6 companies, 20 companies and eventually they became over 100 companies. And in 2005, I worked with Procter & Gamble and GE Aircraft Engine. They now became GE Aviation; then, they got a different environment. So machine learning became a typical thing you use every day, every program, but we don't really emphasize AI at that time. The reason is machine learning is just a tool. It's an algorithm like a support-vector machine, self-organizing map, and logistic regression. All those are just supervised learning or now supervised learning techniques. And people use it. We use it like standard work every day, but we don't talk about AI. But over the years, when you work with so many companies, then you realize the biggest turning point was Toyota 2005 and P&G in 2006. The reason I'm telling you 2005 is Toyota had big problems in the factory in Georgetown, Kentucky, where the Camry factory is located. So they had big compressor problems. So we implemented using machine learning, the support-vector machine, and also principal component analysis. And we enable that the surge of a compressor predicted and avoided and never happened. So until today -- TROND: So they have achieved zero downtime after that project, essentially. JAY: Yeah. So that really is the turning point. Of course, at P&G, the diaper line continues moving the high volume. They can predict things, reduce downtime to 1%. There's a lot of money. Diaper business that is like $10 billion per year. TROND: It's so interesting you focus on downtime, Jay, because obviously, in this hype, which we'll get to as well, people seem to focus so much on fully automated versus what you're saying, which is it doesn't really, you know, we will get to the automation part, but it is the downtime that's where a lot of the savings is obviously. Because whether it's a lights out or lights on, humans are not the real saving here. And the real accomplishment is in zero downtime because that is the industrialization factor. And that is what allows the system to keep operating. Of course, it has to do with automation, but it's not just that. Can you then walk us through what then became industrial AI for you? Because as I've now understood it, it is a highly specific term to you. It's not just some sort of fluffy idea of very, very advanced algorithms and robots running crazy around autonomously. You have very, very specific system elements. And they kind of have to work together in some architectural way before you're willing to call it an industrial AI because it may be a machine tool here, and a machine tool there, and some data here. But for you, unless it's put in place in a working architecture, you're not willing to call it, I mean, it may be an AI, but it is not an industrial AI. So how did this thinking then evolve for you? And what are the elements that you think are crucial for something that you even can start to call an industrial AI? Which you now have a book on, so you're the authority on the subject. JAY: Well, I think the real motivation was after you apply all the machine learning toolkits so long...and a company like National Instruments, NI, in Austin, Texas, they licensed our machine learning toolkits in 2015. And eventually, in 2017, they started using the embedding into LabVIEW version. So we started realizing, actually, the toolkit is very important, not just from the laboratory point of view but also from the production and practitioners' point of view from industry. Of course, researchers use it all the time for homework; I mean, that's fine. So eventually, I said...the question came to me about 2016 in one of our industry advisory board meeting. You have so many successes, but the successes that happen can you repeat? Can you repeat? Can you repeatably have the same success in many, many other sites? Repeatable, scalable, sustainable, that's the key three keywords. You cannot just have a one-time success and then just congratulate yourself and forget it, no. So eventually, we said, oh, to make that repeat sustainable, repeatable, you have a systematic discipline. TROND: I'm so glad you say this because I have taken part in a bunch of best practice schemes and sometimes very optimistically by either an industry association or even a government entity. And they say, "Oh yeah, let's just all go on a bunch of factory visits." Or if it's just an IT system, "Let's just all write down what we did, and then share it with other people." But in fact, it doesn't seem to me like it is that easy. It's not like if I just explain what I think I have learned; that's not something others can learn from. Can you explain to me what it really takes to make something replicable? Because you have done that or helped Foxconn do that, for example. And now you're obviously writing up case studies that are now shared in the World Economic Forum across companies. But there's something really granular but also something very systemic and structured about the way things have to be explained in order to actually make it repeatable. What is the sustainability factor that actually is possible to not just blue copy but turn it into something in your own factory? JAY: Well, I think that there are basically several things. The data is one thing. We call it the data technology, DT, and which means data quality evaluation. How do you understand what to use, what not to use? How do you know which data is useful? And how do you know where the data is usable? It doesn't mean useful data is usable, just like you have a blood donation donor, but the blood may not be usable if the donor has HIV. I like to use an analogy like food. You got a fish in your hand; wow, great. But you have to ask where the fish comes from. [chuckles] If it comes from polluted water, it's not edible, right? So great fish but not edible. TROND: So there's a data layer which has to be usable, and it has to be put somewhere and put to use. It actually then has to be used. It can't just be theoretically usable. JAY: So we have a lot of useful data people collect. The problem is people never realized lots of them are not usable because of a lack of a label. They have no background, and they're not normalized. So eventually, that is a problem. And even if you have a lot of data, it doesn't mean it is usable. TROND: So then I guess that's how you get to your second layer, which I guess most people just call machine learning, but for you, it's an algorithmic layer, which is where some of the structuring gets done and some of the machines that put an analysis on this, put in place automatic procedures. JAY: And machine learning to me it's like cooking ware like a kitchen. You got a pan fry; you got a steamer; you got the grill. Those are tools to cook the food, the data. Food is like data. Cooking ware is like AI. But it depends on purpose. For example, you want fish. What do you want to eat first? I want soup. There's a difference. Do you want to grill? Do you want to just deep fry? So depending on how you want to eat it, the cooking ware will be selected differently. TROND: Well, and that's super interesting because it's so easy to say, well, all these algorithms and stuff they're out there, and all you have to do is pick up some algorithms. But you're saying, especially in a factory, you can't just pick any tool. You have to really know what the effect would be if you start to...for example, on downtime, right? Because I'm imagining there are very many advanced techniques that could be super advanced, but they are perhaps not the right tool for the job, for the workers that are there. So how does that come into play? Are these sequential steps, by the way? So once you figure out what the data is then, you start to fiddle with your tools. JAY: Well, there are two perspectives; one perspective is predict and prevent. So you predict something is going to happen. You prevent it from happening, number one. Number two, understand the root causes and potential root causes. So that comes down to the visible and invisible perspective. So from the visible world, we know what to measure. For example, if you have high blood pressure, you measure blood pressure every day, but that may not be the reason for high blood pressure. It may be because of your DNA, maybe because of the food you eat, because of lack of exercise, because of many other things, right? TROND: Right. JAY: So if you keep measuring your blood pressure doesn't mean you have no heart attack. Okay, so if you don't understand the reason, measuring blood pressure is not a problem. So I'm saying that you know what you don't know. So we need to find out what you don't know. So the correlation of invisible, I call, visible-invisible. So I will predict, but you also want to know the invisible reason relationship so you can prevent that relationship from happening. So that is really called deep mining those invisibles. So we position ourselves very clearly between visible-invisible. A lot of people just say, "Oh, we know what the problem is." The problem is not a purpose. For example, the factory manufacturing there are several very strong purposes, number one quality, right? Worry-free quality. Number two, your efficiency, how much you produce per dollar. If you say that you have great quality, but I spent $10,000 to make it, it is very expensive. But if you spend $2 to make it, wow, that's great. How did you do it? So quality per dollar is a very different way of judging how good you are. You got A; I spent five days studying. I got A; I spent two hours studying. Now you show the capability difference. TROND: I agree. And then the third factor in your framework seems to be platform. And that's when I think a lot of companies go wrong as well because platform is...at least historically in manufacturing, you pick someone else's platform. You say I'm going to implement something. What's available on the market, and what can I afford, obviously? Or ideally, what's the state of the art? And I'll just do that because everyone seems to be doing that. What does platform mean to you, and what goes into this choice? If you're going to create this platform for industrial AI, what kind of a decision is that? JAY: So DT is data, AT is algorithm, and PT is platform, PT platform. Platform means some common things are used in a shared community. For example, kitchen is a platform. You can cook. I can cook. I can cook Chinese food. I can cook Italian food. I can cook Indian food. Same kitchen but different recipe, different seasoning, but same cooking ware. TROND: Correct. Well, because you have a good kitchen, right? JAY: Yes. TROND: So that's -- JAY: [laughs] TROND: Right? JAY: On the platform, you have the most frequently used tool, not everything. You don't need 100 cooking ware in your kitchen. You probably have ten or even five most daily used. TROND: Regardless of how many different cuisines you try to cook. JAY: Exactly. That's called the AI machine toolkit. So we often work with companies and say, "You don't need a lot of tools, come on. You don't need deep learning. You need a good logistic regression and support-vector machine, and you're done." TROND: Got it. JAY: Yeah, you don't need a big chainsaw to cut small bushes. You don't need it. TROND: Right. And that's a very different perspective from the IT world, where many times you want the biggest tool possible because you want to churn a lot of data fast, and you don't really know what you're looking for sometimes. So I guess the industrial context here really constrains you. It's a constraint-based environment. JAY: Yes. So industry, like I said, the industry we talked about three Ps like I said: problems, purposes, and processes. So normally, problem comes from...the main thing is logistic problems, machine, and factory problems, workforce problems, the quality problems, energy problem, ignition problem, safety problems. So the problem happens every day. That's why in factory world, we call it firefighting. Typically, you firefight every day. TROND: And is that your metaphor for the last part of your framework, which is actually operation? So operation sounds really nice and structured, right? JAY: [chuckles] Yes. TROND: As if that was like, yeah, that's the real thing, process. We got this. But in reality, it feels sometimes, to many who are operating a factory; it's a firefight. JAY: Sometimes the reason lean theme work, Six Sigma, you turn a problem into a process, five Ss process, okay? And fishbone diagram, Pareto chart, and Kaizen before and after. So all the process, SOP, so doesn't matter which year workforce comes in, they just repeat, repeat, repeat, repeat, repeat. So in Toyota, the term used to be called manufacturing is just about the discipline. It's what they said. The Japanese industry manufacturing is about discipline, how you follow a discipline to everyday standard way, sustainable way, consistent way, and then you make good products. This is how the old Toyota was talking about, old one. But today, they don't talk that anymore. Training discipline is only one thing; you need to understand the value of customers. TROND: Right. So there are some new things that have to be added to the lean practices, right? JAY: Yes. TROND: As time goes by. So talk to me then more about the digital element because industrial AI to you, clearly, there's a very clear digital element, but there's so many, many other things there. So I'm trying to summarize your framework. You have these four factors: data, algorithms, platforms, and operations. These four aspects of a system that is the challenge you are dealing with in any factory environment. And some of them have to do with digital these days, and others, I guess, really have to do more with people. So when that all comes together, do you have some examples? I don't know, we talked about Toyota, but I know you've worked with Foxconn and Komatsu or Siemens. Can you give me an example of how this framework of yours now becomes applied in a context? Where do people pick up these different elements, and how do they use them? JAY: There's a matrix thinking. So horizontal thinking is a common thing; you need to have good digital thread including DT, data technology, AT, algorithms or analytics, PT, platform, edge cloud, and the things, and OT operation like scheduling, optimizations, stuff like that. Now, you got verticals, quality vertical, cost vertical, efficiency verticals, safety verticals, emission verticals. So you cannot just talk about general. You got to have focus on verticals. For example, let me give you one example: quality verticals. Quality is I'm the factory manager. I care about quality. Yes, the customer will even care more, so they care. But you have a customer come to your shop once a month to check. You ask them, "Why you come?" "Oh, I need to see how good your production." "How about you don't have to come? You can see my entire quality." "Wow, how do I do that?" So eventually, we develop a stream of quality code, SOQ, Stream Of Quality. So it's not just about the product is good. I can go back to connect all the processes of the quality segment of each station. Connect them together. Just like you got a fish, oh, okay, the fish is great. But I wonder, when the fish came out of water, when the fish was in the truck, how long was it on the road? And how long was it before reaching my physical distribution center and to my home? So if I have a sensor, I can tell you all the temperature history inside the box. So when you get your fish, you take a look; oh, from the moment the fish came out of the boat until it reached my home, the temperature remained almost constant. Wow. Now you are worry-free. It's just one thing. So you connect together. So that's why we call SOQ, Stream Of Quality, like a river connected. So by the time a customer gets a quality product, they can trace back and say, "Wow, good. How about if I let you see it before you come? How about you don't come?" I say, "Oh, you know what? I like it." That's what this type of manufacturing is about. It just doesn't make you happy. You have to make the customer happy, worry-free. MID-ROLL AD: In the new book from Wiley, Augmented Lean: A Human-Centric Framework for Managing Frontline Operations, serial startup founder Dr. Natan Linder and futurist podcaster Dr. Trond Arne Undheim deliver an urgent and incisive exploration of when, how, and why to augment your workforce with technology, and how to do it in a way that scales, maintains innovation, and allows the organization to thrive. The key thing is to prioritize humans over machines. Here's what Klaus Schwab, Executive Chairman of the World Economic Forum, says about the book: "Augmented Lean is an important puzzle piece in the fourth industrial revolution." Find out more on www.augmentedlean.com and pick up the book in a bookstore near you. TROND: So, Jay, you took the words out of my mouth because I wanted to talk about the future. I'm imagining when you say worry-free, I mean, you're talking about a soon-to-be state of manufacturing. Or are you literally saying there are some factories, some of the excellence factories where you've won awards in the World Economic Forum or other places that are working towards this worry-free manufacturing, and to some extent, they have achieved it? Well, elaborate for me a little bit about the future outlook of manufacturing and especially this people issue because you know that I'm engaged...The podcast is called Augmented Podcast. I'm engaged in this debate about automation. Well, is there a discrepancy between automation and augmentation? And to what extent is this about people running the system? Or is it the machines that we should optimize to run all the system? For you, it's all about worry-free. First of all, just answer this question, is worry-free a future ideal, or is it actually here today if you just do the right things? JAY: Well, first of all, worry-free is our mindset where the level of satisfaction should be, right? TROND: Yep. JAY: So to make manufacturing happen is not about how to make good quality, how to make people physically have less worry, how to make customers less worry is what is. But the reason we have a problem with workforce today, I mean, we have a hard time to hire not just highly skilled workers but even regular workforce. Because for some reason, not just U.S., it seems everywhere right now has similar problems. People have more options these days to select other living means. They could be an Uber driver. [laughs] They could be...I don't know. So there are many options. You don't have to just go to the factory to make earnings. They can have a car and drive around Uber and Lyft or whatever. They can deliver the food and whatever. So they can do many other things. And so today, you want to make workforce work environment more attractive. You have to make sure that they understand, oh, this is something they can learn; they can grow. They are fulfilled because the environment gives them a lot of empowerment. The vibe, the environment gives them a wow, especially young people; when you attract them from college, they'd like a wow kind of environment, not just ooh, okay. [laughs] TROND: Yeah. Well, it's interesting you're saying this. I mean, we actually have a lack of workers. So it's not just we want to make factories full of machines; it's actually the machines are actually needed just because there are no workers to fill these jobs. But you're looking into a future where you do think that manufacturing is and will be an attractive place going forward. That seems to be that you have a positive vision of the future we're going into. You think this is attractive. It's interesting for workers. JAY: Yeah. See, I often say that there are some common horizontal we have to use all the day. Vertical is the purpose, quality. I talked about vertical quality first, quality. But what are the horizontal common? I go A, B, C, D, E, F. What's A? AI. B is big data. C is cyber and cloud. D is digital or digital twin, whatever. E is environment ecosystem and emission reduction. What's F? Very important, fun. [laughs] If you miss that piece, who wants to work for a place there's no fun? You tell me would you work for...you and I, we're talking now because it's fun. You talk to people and different perspectives. I talk to you, and I say, wow, you've built some humongous network here in the physical...the future of digital, not just professional space but also social space but also the physical space. So, again, the fun things inspire people, right? TROND: They do. So talking about inspiring people then, Jay, if you were to paint a picture of this future, I guess, we have talked just now about workers and how if you do it right, it's going to be really attractive workplaces in manufacturing. How about for, I guess, one type of worker, these knowledge workers more generally? Or, in fact, is there a possibility that you see that not just is it going to be a fun place to be for great, many workers, but it's actually going to be an exciting knowledge workplace again? Which arguably, industrialization has gone through many stages. And being in a factory wasn't always all that rosy, but it was certainly financially rewarding for many. And it has had an enormous career progression for others who are able to find ways to exploit this system to their benefit. How do you see that going forward? Is there a scope, is there a world in which factory work can or perhaps in an even new way become truly knowledge work where all of these industrial AI factors, the A to the Fs, produce fun, but they produce lasting progression, and career satisfaction, empowerment, all these buzzwords that everybody in the workplace wants and perhaps deserves? JAY: That's how we look at the future workforce is not just about the work but also the knowledge force. So basically, the difference is that people come in, and they become seasoned engineers, experienced engineers. And they retire, and the wisdom carries with them. Sometimes you have documentation, Excel sheet, PPT in the server, but nobody even looks at it. That's what today's worry is. So now what you want is living knowledge, living intelligence. The ownership is very important. For example, I'm a worker. I develop AI, not just the computer software to help the machine but also help me. I can augment the intelligence. I will augment it. When I make the product happen, the inspection station they check and just tell me pass or no pass. They also tell me the quality, 98, 97, but you pass. And then you get your score. You got a 70, 80, 90, but you got an A. 99, you got an A, 91, you got an A, 92. So what exactly does A mean? So, therefore, I give you a reason, oh, this is something. Then I learn. Okay, I can contribute. I can use voice. I can use my opinion to augment that no, labeled. So next time people work, oh, I got 97. And so the reason is the features need to be maintained, to be changed, and the system needs to be whatever. So eventually, you have a human contribute. The whole process could be consisting of 5 experts, 7, 10, 20, eventually owned by 20 people. That legacy continues. And you, as a worker, you feel like you're part of the team, leave a legacy for the next generation. So eventually, it's augmented intelligence. The third level will be actual implementation. So AI is not about artificial intelligence; it is about actual implementation. So people physically can implement things in a way they can make data to decisions. So their decision mean I want to make an adjustment. I want to find out how much I should adjust. Physically, I can see the gap. I can input the adjustment level. The system will tell me physically how could I improve 5%. Wow, that's good. I made a 5% improvement. Your boss also knows. And your paycheck got the $150 increase this month. Why? Because my contribution to the process quality improved, so I got the bonus. That's real-world feedback. TROND: Let me ask you one last question about how this is going to play out; I mean, in terms of how the skilling of workers is going to allow this kind of process. A lot of people are telling me about the ambitions that I'm describing...and some of the guests on the podcasts and also the Tulip software platform, the owner of this podcast, that it is sometimes optimistic to think that a lot of the training can just be embedded in the work process. That is obviously an ideal. But in America, for example, there is this idea that, well, you are either a trained worker or an educated worker, or you are an uneducated worker. And then yes, you can learn some things on the job. But there are limits to how much you can learn directly on the job. You have to be pulled out, and you have to do training and get competencies. As you're looking into the future, are there these two tracks? So you either get yourself a short or long college degree, and then you move in, and then you move faster. Or you are in the factory, and then if you then start to want to learn things, you have to pull yourself out and take courses, courses, courses and then go in? Or is it possible through these AI-enabled training systems to get so much real-time feedback that a reasonably intelligent person actually never has to be pulled out of work and actually they can learn on the job truly advanced things? So because there are two really, really different futures here, one, you have to scale up an educational system. And, two, you have to scale up more of a real-time learning system. And it seems to me that they're actually discrepant paths. JAY: Sure. To me, I have a framework in my book. I call it the four P structure, four P. First P is principle-based. For example, in Six Sigma, in lean manufacturing, there's some basic stuff you have to study, basic stuff like very simple fishbone diagram. You have to understand those things. You can learn by yourself what that is. You can take a very basic introduction course. So we can learn and give you a module. You can learn yourself or by a group, principle-based. The second thing is practice-based. Basically, we will prepare data for you. We will teach you how to use a tool, and you will do it together as a team or as individual, and you present results by using data I give to you, the tool I give to you. And it's all, yeah, my team A presented. Oh, they look interesting. And group B presented, so we are learning from each other. Then after the group learning is finished, you go back to your team in the real world. You create a project called project-based learning. You take a tool you learn. You take the knowledge you learn and to find a project like a Six Sigma project you do by yourself. You formulate. And then you come back to the class maybe a few weeks later, present with a real-world project based on the boss' approval. So after that, you've got maybe a black belt but with the last piece professional. Then you start teaching other people to repeat the first 3ps. You become master black belt. So we're not reinventing a new term. It really is about a similar concept like lean but more digital space. Lean is about personal experience, and digital is about the data experience is what's the big difference. TROND: But either way, it is a big difference whether you have to rely on technological experts, or you can do a lot of these things through training and can get to a level of aptitude that you can read the signals at least from the system and implement small changes, perhaps not the big changes but you can at least read the system. And whether they're low-code or no-code, you can at least then through learning frameworks, you can advance, and you can improve in not just your own work day, but you can probably in groups, and feedbacks, and stuff you can bring the whole team and the factory forward perhaps without relying only on these external types of expertise that are actually so costly because they take you away. So per definition, you run into this; I mean, certainly isn't worry-free because there is an interruption in the process. Well, look, this is fascinating. Any last thoughts? It seems to me that there are so many more ways we can dig deeper on your experience in any of these industrial contexts or even going deeper in each of the frameworks. Is there a short way to encapsulate industrial AI that you can leave us with just so people can really understand? JAY: Sure. TROND: It's such a fundamental thing, AI, and people have different ideas about that, and industry people have something in their head. And now you have combined them in a unique way. Just give us one sentence: what is industrial AI? What should people leave this podcast with? JAY: AI is a cognitive science, but industrial AI is a systematic discipline is one sentence. So that means people have domain knowledge. Now we have to create data to represent our domain then have the discipline to solve the domain problems. Usually, with domain knowledge, we try with our experience, and you and I know; that's it. But we have no data coming out. But if I have domain become data and data become discipline, then other people can repeat our success even our mistake; they understand why. So eventually, domain, data, discipline, 3 Ds together, you can make a good decision, sustainable and long-lasting. TROND: Jay, this has been so instructive. I thank you for spending this time with me. And it's a little bit of a never-ending process. JAY: [laughs] TROND: Industry is not something that you can learn it and then...because also the domain changes and what you're doing and what you're producing changes as well. So it's a lifelong -- JAY: It's rewarding. TROND: Rewarding but lifelong quest. JAY: Yeah. Well, thank you for the opportunity to share, to discuss. Thank you. TROND: It's a great pleasure. You have just listened to another episode of the Augmented Podcast with host Trond Arne Undheim. The topic was Industrial AI. And our guest was Professor Jay Lee from University of Cincinnati. In this conversation, we talked about how AI in industry needs to work every time and what that means. My takeaway is that industrial AI is a breakthrough that will take a while to mature. It implies discipline, not just algorithms. In fact, it entails a systems architecture consisting of data, algorithm, platform, and operation. Thanks for listening. If you liked the show, subscribe at augmentedpodcast.co or in your preferred podcast player, and rate us with five stars. If you liked this episode, you might also like Episode 81: From Predictive to Diagnostic Manufacturing Augmentation. Hopefully, you'll find something awesome in these or in other episodes, and if so, do let us know by messaging us. We would love to share your thoughts with other listeners. The Augmented Podcast is created in association with Tulip, the frontline operation platform that connects the people, machines, devices, and systems used in a production or logistics process in a physical location. Tulip is democratizing technology and is empowering those closest to operations to solve problems. Tulip is also hiring. You can find Tulip at tulip.co. Please share this show with colleagues who care about where industry and especially where industrial tech is heading. To find us on social media is easy; we are Augmented Pod on LinkedIn and Twitter and Augmented Podcast on Facebook and YouTube. Augmented — industrial conversations that matter. See you next time. Special Guest: Jay Lee.