Podcasts about data privacy

Japan's first female governing-party leader is an ultra-conservative star in a male-dominated groupSanae Takaichi.In a country that ranks poorly internationally for gender equality, the new president of Japan's long-governing Liberal Democrats, and likely next prime minister, is an ultra-conservative star of a male-dominated party that critics call an obstacle to women's advancement.In a country known for the concept of karoshi, or death from overwork, Japan's likely next prime minister said that people should work like a WHAT?A WORKHORSEBefore entering politics, Japan's likely next prime minister had WHAT artistic hobby?Drummer in a heavy metal bandIntroducing Fortune's first-ever Most Influential Women Asia rankingJust to give you some context:How many athletes? 4How many K-pop stars? 4How many actors? 2How many politicians? 2HOW MANY business leaders, civic leaders, scientists, educators, journalists, healthcare workers, spiritual leaders, or legal scholars?ZERODemocrats demand ‘action' as AI reportedly threatens to replace 100M US jobsA new Senate report warns that artificial intelligence could displace nearly 100 million U.S. jobs within the next decade, spurring Democrats to push for a levy for each human position replaced by machines, tech or algorithms. What is the current nickname for this bill: Terminator tithea "robot tax"Roomba reparationsbot tollRoboCop rebateSilicon sin taxAccording to Bloomberg, This is the leading pick to succeed Tim Cook as CEOCOO Sabih KhanFormer COO Jeff Williams, SVP Design, Watch, and HealthJohn Ternus, SVP of Hardware EngineeringCFO Kevan ParekhCHRO Deirdre O'BrienBoard member Susan Wagner, founding partner and director of BlackRockDeloitte will refund Australian government for WHAT?climate risk model using emissions data from New Zealand and not AustraliaA report that was filled with AI hallucinationsa partial refundConsulting firm quietly admitted to GPT-4o use after fake citations were found in AugustShortly after the report was published, though, Sydney University Deputy Director of Health Law Chris Rudge noticed citations to multiple papers and publications that did not exist. That included multiple references to nonexistent reports by Lisa Burton Crawford, a real professor at the University of Sydney law school.the updated report removed several fake citations and a fabricated quote attributed to an actual ruling from federal justice Jennifer Davies (spelled as "Davis" in the original report).cybersecurity review that relied on completely fabricated case studiesOver 80% of the report found to have copied sections from Wikipediapolicy review found to have been nearly a complete duplicated a previous PwC reportAppLovin stock tanks on report SEC is investigating company over data-collection practicesPOP QUIZ!Adam Foroughi is the CEO of AppLovin:Who is the Founder of AppLovin? Adam ForoughiWho is the Chair of AppLovin? Adam ForoughiWho is the longest-tenured director of AppLovin? Adam ForoughiWho is the largest shareholder at AppLovin? Adam ForoughiWhat percentage of outstanding AppLovin shares does Adam own? 9%What percentage of AppLovin voting power does Adam control? 61%How many votes per share do Adam's Cass B shares give him? 20Did Adam graduate from college? YES! Economics degree from BerkeleyBut what exactly does AppLovin do? The company helps developers market, monetize, analyze and publish their apps through its mobile advertising, marketing, and analytics platformsOn the company's “Director Nominees' Skills and Expertise” matrix in its 2025 proxy statement, which two categories are the least-represented?: Cyber Security (3 of 9) and Data Privacy (4 of 9)What was the value Adam realized on the vesting of stock awards last year? $578MDespite holding $19B in AppLovin stock, how much did Adam get in a work-from-home cash stipend last year? $1,800 Which BlackRock director that Matt spent a lot of time ridiculing in May for being the board's worst performer just lost his job? Hans Vestberg, VerizonWhich Verizon board member that is connected to 64% of the Verizon board–almost entirely through non profit and trade group connections–that Matt recommended a vote against at Verizon's last annual meeting is Verizon's new CEO? Lead Director and former PayPal CEO Dan ShulmanPOP QUIZ! What kind of shoes does Dan wear? Cowboy bootsAnd finally, nepobaby David Ellison's choice to take over CBS News, Bari Weiss, has made a career railing against what?CorruptionMisinformationCorporate malpracticeCensorshipWokenessPOP QUIZ! How many years of experience does Bari have in broadcast television? Zero

In this episode of Next in Media, Mike Shields sits down with Michael Komasinski, CEO of Criteo, to unpack how one of ad tech's best-known companies has reinvented itself for a privacy-first world. Once synonymous with retargeting, Criteo has successfully evolved into a powerhouse in retail media, supporting more than 230 retailers and $160 billion in GMV.Michael shares how the company's early investments in addressability technology and diversification under Megan Clarkin laid the foundation for long-term resilience. He also discusses the industry's next big shifts from the end of “easy money” in retail media to the rise of agentic workflows, AI-powered ad optimization, and Criteo's surprising new partnership with Google. Key Highlights:

We get asked a lot about "Minimum Viable Privacy." What's the least you need to do to in order to get the MOST protection? So, this week on the show, Comedian Rosie Train provides the MUST HAVE apps. You can get additional tips, and learn more over at StupidSexyPrivacy.com

join wall-e for today's tech update on thursday, october 2! california's ai legislation: governor gavin newsom signs sb 53 into law, aiming to enforce ai safety protocols. this bill signifies how regulation may complement technological growth, although concerns exist about its impact on the u.s. competitiveness. instagram's data privacy concerns: adam mosseri clarifies rumors about app microphone data usage, explaining meta's focus on ai-driven user interaction insights for more targeted advertising, raising data privacy debates. openai's sora launch: openai introduces sora, an ai video platform similar to tiktok, highlighting its mission to progress ai within its nonprofit framework, amid internal discussions on company values. synthetic actress in hollywood: particle6's ai unit unveils tilly norwood, sparking conversations about the future of acting and job security among real actors, with inputs from sag-aftra and celebrities like emily blunt. einride's funding success: self-driving truck firm einride secures $100 million to advance its electric and autonomous vehicle tech, supported by eqt ventures and ionq, aiming for market expansion. stay tuned for tomorrow's tech updates!

      Can AI transcend its role from a mere tool to a pivotal player in healthcare transformation? Join us as we engage in a compelling conversation with retired Navy Captain and cardiothoracic surgeon Dr. Hassan Tetteh, a vanguard in clinical informatics and artificial intelligence. We unravel AI's metamorphosis within military and federal healthcare systems, spotlighting its evolution from an unseen assistant to a dynamic co-pilot in the field of medicine. Discover how AI is becoming integral in tasks such as medical note-taking, sepsis detection, and the groundbreaking realm of robotic surgeries, including the historic first fully robotic heart transplant.        Our discussion delves deeper into the profound impact of AI on military and Veterans Affairs medicine. Witness how AI is revolutionizing combat zone triage, telemedicine, and damage control resuscitation, offering unprecedented medical support in austere environments. We explore AI's potential to empower medical professionals with virtual platforms and wearable devices, as well as the innovative deployment of life-saving resources via drones. Dr. Tetteh sheds light on how AI is addressing critical issues in the VA system, tackling challenges like suicide, mental health, and traumatic brain injuries, and reshaping healthcare for military personnel and veterans alike.      Balancing innovation with caution, we also navigate the ethical and regulatory complexities that accompany the integration of AI into healthcare. Highlighting the strategic collaboration between the Warfighter Health Mission and the VA, we explore how AI and electronic health records leverage data to provide crucial health insights. The conversation highlights the transformative potential of AI while emphasizing the importance of data integrity and the need for ongoing learning among healthcare professionals. As we chart the future of AI in healthcare, this episode serves as both a guide and a clarion call for adaptability and leadership in an age of rapid technological advancement. Episode Support: A generous educational grant from HumanCare Technologies Inc. supported the episode. Chapters: (00:05) AI Impact on Healthcare Transformation (13:12) Advancements in Military and VA Medicine (20:42) Data Privacy and AI in Healthcare (30:25) Military Medicine and AI Impact (37:18) Evolution of Healthcare Roles With AI (52:04) Path to AI Adoption in Healthcare (01:00:17) National Security and AI   Chapter Summaries: (00:05) AI Impact on Healthcare Transformation AI is transforming healthcare, aiding in tasks from notes to surgery, and has strategic importance for national security.   (13:12) Advancements in Military and VA Medicine AI has transformative potential in military medicine, enhancing care delivery in combat zones and remote settings, and addressing challenges in the VA system.   (20:42) Data Privacy and AI in Healthcare AI and electronic health records aid in addressing health challenges for military personnel and veterans, including suicide prevention and improving healthcare delivery.   (30:25) Military Medicine and AI Impact AI has transformative potential in healthcare, digitizing and de-identifying pathology slides and utilizing radiologic data for improved patient outcomes.   (37:18) Evolution of Healthcare Roles With AI Nature's evolving healthcare professions adapt to AI, emphasizing continuous learning and the role of human decision commanders.   (52:04) Path to AI Adoption in Healthcare AI in healthcare: FDA's adaptation, adoption curve, data literacy, upskilling, and transformative potential for efficient solutions.   (01:00:17) National Security and AI AI is a transformative technology with global implications, and the US should lead in its development.   Take Home Messages: AI as a Co-Pilot in Healthcare: Artificial intelligence is transitioning from an invisible assistant to a critical co-pilot in both military and VA healthcare settings. Its applications range from assisting in robotic surgeries to providing real-time decision support at the bedside, illustrating its potential to enhance medical practices and patient care significantly. Transformative Impact on Military Medicine: AI is revolutionizing military healthcare by improving combat triage, telemedicine, and remote resuscitation. This technology enables life-saving innovations such as drone deployments and advanced wearables, which are crucial in delivering care in challenging environments. AI's Role in Addressing Veterans' Health Challenges: Within the Veterans Affairs system, AI is being leveraged to tackle pressing issues such as mental health challenges, suicide prevention, and traumatic brain injuries. The integration of AI-driven insights from electronic health records is paving the way for improved healthcare delivery and patient outcomes. The Ethical and Regulatory Landscape of AI in Healthcare: As AI continues to integrate into healthcare, it is essential to address ethical considerations and regulatory challenges. Balancing innovation with regulation is crucial to maintaining leadership in AI technology, ensuring data integrity, and fostering continuous learning among healthcare professionals. Evolving Roles in Healthcare Professions: The integration of AI is prompting a shift in healthcare roles, where professionals are evolving into "decision commanders" equipped with enhanced information to make better decisions. This transformation underscores the importance of adaptability and continuous learning to keep pace with technological advancements in the field.   Episode Keywords: AI in military healthcare, Dr. Hassan Tetteh, robotic heart transplants, autonomous surgeries, combat triage, telemedicine, veteran mental health, suicide prevention, traumatic brain injuries, Warfighter Health Mission, electronic health records, data privacy in healthcare, ethical AI in medicine, medical decision-making, healthcare technology, AI integration, healthcare innovation, national security and AI, AI advancements in medicine   Hashtags: #MilitaryHealthcare #AIRevolution #VeteranWellbeing #AIinMedicine #CombatMedicine #HealthcareInnovation #VAHealthcare #AIFuture #TechInHealthcare #MedicalAdvancements   Honoring the Legacy and Preserving the History of Military Medicine The WarDocs Mission is to honor the legacy, preserve the oral history, and showcase career opportunities, unique expeditionary experiences, and achievements of Military Medicine. We foster patriotism and pride in Who we are, What we do, and, most importantly, How we serve Our Patients, the DoD, and Our Nation.   Find out more and join Team WarDocs at https://www.wardocspodcast.com/ Check our list of previous guest episodes at https://www.wardocspodcast.com/our-guests Subscribe and Like our Videos on our YouTube Channel: https://www.youtube.com/@wardocspodcast Listen to the “What We Are For” Episode 47. https://bit.ly/3r87Afm   WarDocs- The Military Medicine Podcast is a Non-Profit, Tax-exempt-501(c)(3) Veteran Run Organization run by volunteers. All donations are tax-deductible and go to honoring and preserving the history, experiences, successes, and lessons learned in Military Medicine. A tax receipt will be sent to you. WARDOCS documents the experiences, contributions, and innovations of all military medicine Services, ranks, and Corps who are affectionately called "Docs" as a sign of respect, trust, and confidence on and off the battlefield,demonstrating dedication to the medical care of fellow comrades in arms.     Follow Us on Social Media Twitter: @wardocspodcast Facebook: WarDocs Podcast Instagram: @wardocspodcast LinkedIn: WarDocs-The Military Medicine Podcast YouTube Channel: https://www.youtube.com/@wardocspodcast

“Our approach is simple: remove the PII from the data stream, and you don't have to worry about compliance,” said Bill Placke, President, Americas at SecurePII. At WebexOne in San Diego, Doug Green, Publisher of Technology Reseller News, spoke with Jason Thals, COO of BroadSource, and Placke of SecurePII about their finalist recognition in Cisco's Dynamic Duo competition. The joint solution, built on Cisco Webex Contact Center, is designed to unlock AI's potential by enabling enterprises to leverage large language models without exposing sensitive personal data. SecurePII's flagship product, SecureCall, was purpose-built for Webex (and also available on Genesys) to deliver PCI compliance while removing personally identifiable information from voice interactions. This enables organizations to deploy AI and agentic automation confidently, without the regulatory risk tied to data privacy laws across the U.S., GDPR, and beyond. Thals emphasized BroadSource's role in delivering services that complement CCaaS and UCaaS platforms globally, while Placke framed the opportunity for Cisco partners: “This is a super easy bolt-on, available in the Webex App Hub. Customers can be up and running in 30 minutes and compliant.” The collaboration, already proven with a government-regulated client in Australia, is industry-agnostic and scalable from small deployments to 50,000+ users. For Cisco resellers, it represents a powerful, sticky service that integrates seamlessly into channel models while helping enterprises stay compliant as they modernize customer engagement. Learn more at BroadSource and SecurePII.

HIPAA, Data privacy, wearable techs, watches, rings, and mass surveillance: How our eroding data privacy protections are a danger to our collective public health. Also, maybe be more wary of period/fertility tracking apps. -o-www.everythingispublichealth.comBluesky Social: @everythingisPHMastodon: @everythingispublichealth Email: EverythingIsPublicHealth@gmail.com  Photo Credit:  Photo by Musab Al Rawahi on UnsplashSupport the show

Summary In this episode, Andy interviews Matt Mong, a leader at PlanAutomate, a company focused on bringing intelligent automation to the world of project management. With AI dominating headlines, Matt joins the podcast to explore what's real, what's hype, and what project managers and frontline leaders should actually be doing to stay ahead. They discuss common misconceptions about AI in projects, why structured data is critical, and how AI can evolve from productivity booster to strategic game-changer. Matt shares practical advice on balancing innovation with data privacy, how project roles may shift over time, and what leaders can do today to prepare ourselves (and even our kids) for the future of work. If you're looking for insights on how to stay relevant in an AI-transformed workplace, this episode is for you! Sound Bites “The real question isn't just what AI can do faster. It's what it can do that we couldn't do before.” “We tend to overestimate what AI will do in two years and underestimate what it will do in ten.” “If you don't get your data in order, AI won't be useful. It's as simple as that.” “This isn't about eliminating jobs. It's about empowering people to lead and collaborate better.” “Don't future-proof your job. Prepare for the future by experimenting and adapting.” “What if your projects could talk to you and give ongoing guidance? That's where we're heading.” Chapters 00:00 Introduction 02:00 Start of Interview 02:06 What's Something About AI or Projects That Drives You Crazy? 05:00 How Are You Personally Using AI at Work or Home? 07:00 How Are Customers Using AI Today? And Where Is It Heading? 12:06 What About Concerns With Data Privacy and Security? 15:00 What Other Risks or Opportunities Should Leaders Be Thinking About? 17:22 Do You Agree That 'AI Won't Take Your Job. Someone Using AI Will'? 19:40 What Mindsets or Skills Should People Build Now? 23:52 How Can We Prepare Our Kids for the Future of Work? 27:47 Where Can People Learn More About You and PlanAutomate? 29:00 End of Interview 29:20 Andy Comments After the Interview 33:14 Outtakes Learn More You can learn more about Matt and his work at PlanAutomate.com. You can take the survey that Matt mentions here: https://www.planautomate.com/survey-state-of-ai-in-project-management/ For more learning on this topic, check out: Episode 463 with Faisal Hoque. He has an empowering take on how to transcend the fear and hype around AI. It's a great discussion. Episode 415 with leaders from the software company Keto Software. It's a great follow-up to this discussion, with some guys who have been in this space for many years. Episode 384 with PMeLa, an AI persona that I've been curating for a couple of years. She was the first AI to be interviewed on a leadership or project management podcast ever. In fact, YOU can talk with PMeLa here. Level Up Your AI Skills During the interview, I mentioned our AI Made Simple course. Join other listeners from around the world who are taking this course to prepare for an AI-infused future. Just go to ai.PeopleAndProjectsPodcast.com. Thanks! Pass the PMP Exam This Year If you or someone you know is thinking about getting PMP certified, we've put together a helpful guide called The 5 Best Resources to Help You Pass the PMP Exam on Your First Try. We've helped thousands of people earn their certification, and we'd love to help you too. It's totally free, and it's a great way to get a head start. Just go to 5BestResources.PeopleAndProjectsPodcast.com to grab your copy. I'd love to help you get your PMP this year! Join Us for LEAD52 I know you want to be a more confident leader–that's why you listen to this podcast. LEAD52 is a global community of people like you who are committed to transforming their ability to lead and deliver. It's 52 weeks of leadership learning, delivered right to your inbox, taking less than 5 minutes a week. And it's all for free. Learn more and sign up at GetLEAD52.com. Thanks! Thank you for joining me for this episode of The People and Projects Podcast! Talent Triangle: Business Acumen Topics: AI, Automation, Project Management, Future of Work, Innovation, Data Privacy, Intelligent Systems, Team Leadership, Experimentation, Upskilling, Technology Strategy, Digital Transformation The following music was used for this episode: Music: The Fantastical Ferret by Tim Kulig License (CC BY 4.0): https://filmmusic.io/standard-license Music: Tuesday by Sascha Ende License (CC BY 4.0): https://filmmusic.io/standard-license

An in-depth conversation with a ethical hacker about the perils caused by Social Engineering, data harvesting and hacking. Follow Anonymous and learn more about his services on Instagram and X:Instagram: https://www.instagram.com/anon_cysec/X: https://x.com/Code4_CyberSecWebsite: https://code4cybersecurity.com/***** Across The Socials @TheAndresSegovia & Twitter/X @_AndresSegovia https://TheAndresSegovia.com To hear more, visit theandressegovia.substack.com

On this episode of Crazy Wisdom, Stewart Alsop talks with Agustin Ferreira, founder of Neurona, an AI community in Buenos Aires. Their conversation moves through Argentina's history with economic crises and the rise of crypto as an alternative to failing institutions, the importance of Ethereum and smart contracts, the UX challenges that still plague crypto adoption, and how AI and agents could transform the way people interact with decentralized systems. They also explore the tension between TradFi and DeFi, questions of data privacy and surveillance, the shifting role of social networks, and even the cultural and philosophical meaning of decentralization. You can learn more about Agustin's work through Neurona on Twitter at Neurona.Check out this GPT we trained on the conversationTimestamps00:05 Agustin shares how Argentina's economic crises and the Corralito shaped interest in Bitcoin and Ethereum, with smart contracts offering a way out of broken systems.00:10 They compare Bitcoin's simplicity with Ethereum's immutability and programmability, opening new use cases beyond money transfers.00:15 The discussion shifts to crypto's UX problem, from jargon and wallets to agents and AI smoothing the user experience, with projects like Gina Wallet and Gigabrain.00:20 Stewart's frustrations with NFTs and bridging tokens highlight why validators, restaking, and cross-chain complexity still matter for decentralization.00:25 Agustin reflects on TradFi merging with DeFi, the risk of losing core values, and how stablecoins and U.S. interest could spark a spike in crypto markets.00:30 They broaden into Web 2.0's walled gardens, the need for alternatives, and how AI, data privacy, and surveillance raise urgency for decentralized systems.00:35 Social networks, culture, and hypercapitalism come into focus, with Agustin questioning fantasy online lives and imagining more conscious connections.00:40 The conversation turns philosophical, exploring religion-like markets, self-knowledge, and the hope for technology that feels more human.00:45 Stewart and Agustin discuss off-grid living, AI as a tool for autonomy, and space exploration shaping future generations.00:50 Agustin brings in the metaverse, both its potential to connect people more deeply and the risk of centralization, closing with Neurona's mission in Buenos Aires.Key InsightsOne of the strongest themes Agustin brings forward is how Argentina's long history of economic crises and the Corralito in 2001 created a natural openness to crypto. For his generation, trust in the peso was destroyed early, and holding dollars became the norm. This made decentralized alternatives like Bitcoin and later Ethereum feel less like speculation and more like survival tools.Ethereum's introduction of smart contracts represented a decisive leap from Bitcoin's simple ledger into programmable, immutable agreements. For young Argentines, this opened a space to innovate and build projects that weren't dependent on fragile local institutions, and it felt like a path to opportunity in the midst of recurring instability.Agustin emphasizes that crypto still has a major UX problem. From confusing jargon to multiple wallets and bridges, it's far from intuitive. He sees AI agents playing a transformative role in making transactions and investments seamless, removing technical friction so people can use crypto without even realizing the complexity beneath it.Bridging across blockchains reveals both the promise and challenge of decentralization. Tokens must be locked, represented, and validated across chains, and while this creates resilience, it also adds layers of risk. Agustin hopes the future will feel “like magic,” where these processes disappear from the user's view.The rise of TradFi players in DeFi is double-edged. On one hand, it accelerates maturity and scale, but on the other, it risks eroding the original ethos of decentralization. Agustin worries about lost principles yet also anticipates a surge of new DeFi projects and stablecoin adoption driven by U.S. financial interests.Beyond finance, the conversation turns to the politics of data privacy and surveillance. Agustin argues that much of the motivation for decentralized systems is to resist manipulation, polarization, and weaponization of personal information—issues that AI will amplify unless paired with decentralized alternatives.Finally, both Stewart and Agustin reflect on culture, social networks, and even the metaverse. Agustin critiques hypercapitalism's fantasy-driven platforms and envisions technology that enables more authentic human connection. Whether through off-grid living, space exploration, or decentralized metaverse communities, he sees a need to balance innovation with deeper human and philosophical questions about freedom and meaning.

In Episode 7, host Jordan L. Fischer, Esq. interviews Representative Stephanie Scialabba, a state representative in the Commonwealth of Pennsylvania, who is using her experience in her prior career as a practicing attorney in data privacy and cybersecurity to advance the privacy rights of Pennsylvanians and create realistic solutions for evolving issues. In this episode, Representative Scialabba discusses her perspective on the legislative process around technology, privacy, and how she is approaching such a fast-paced and highly influential area of the law. For more information on Representative Scialabba, visit: https://www.repscialabba.com/. To contact our host, Jordan L. Fischer, Esq., regarding this podcast or to inquire into becoming a guest, please contact Ms. Fischer at jordan@jordanfischer.me.

This Podcast is sponsored by Team Simmer.Go to TeamSimmer and use the coupon code DEVIATE for 10% on individual course purchases.The Technical Marketing Handbook provides a comprehensive journey through technical marketing principles.Sign up to the Simmer Newsletter for the latest news in Technical Marketing.NEW! - Mastering GA4 With Google BigQuery Course with Johan van de Werken is now out and you can get 15% discount on it if you buy it by the end of the month (September). The 15% discount will be applied automatically at checkout! Doesn't work together with another discount code. Get it here: https://www.teamsimmer.com/all-courses/mastering-ga4-with-google-bigquery/Latest content from Juliana & Simo:Subscribe to Juliana's newsletter: https://julianajackson.substack.com/Latest on the SimoAhava.com blog > #GTMTips: How To Load Google Scripts From A Server Container - https://www.simoahava.com/gtmtips/new-way-load-google-scripts-server-container/Latest from Juliana: https://julianajackson.substack.com/p/how-to-do-data-analysisAlso mentioned in the episode:Loads of goodies on sGTM Pantheon from Gunnar Griese: https://gunnargriese.com/tags/gtm-server-side/GA4 Dataform - https://ga4dataform.com/ (shouts to Jules, Krisztián, Johan, Artem, Simon)Analytics Summit - https://www.analytics-summit.com/Measure Summit - https://measuresummit.com/Measurecamp Helsinki - https://helsinki.measurecamp.org/Google Tag Gateway - https://developers.google.com/tag-platform/tag-manager/gateway/setup-guide?setup=manualsGTM Pantheon - https://github.com/google-marketing-solutions/gps-sgtm-pantheonArben Kqiku - upcoming instructor on Team Simmer for R for Data analysis - https://www.linkedin.com/in/arben-kqiku-301457117/ This podcast is brought to you by Juliana Jackson and Simo Ahava.

DEAR PAO: Harassment received from an online lending app company, violation of Sections 28 and 31 of the Data Privacy Act of 2012 | Sept. 28, 2025Subscribe to The Manila Times Channel - https://tmt.ph/YTSubscribe Visit our website at https://www.manilatimes.net Follow us: Facebook - https://tmt.ph/facebook Instagram - https://tmt.ph/instagram Twitter - https://tmt.ph/twitter DailyMotion - https://tmt.ph/dailymotion Subscribe to our Digital Edition - https://tmt.ph/digital Check out our Podcasts: Spotify - https://tmt.ph/spotify Apple Podcasts - https://tmt.ph/applepodcasts Amazon Music - https://tmt.ph/amazonmusic Deezer: https://tmt.ph/deezer Stitcher: https://tmt.ph/stitcherTune In: https://tmt.ph/tunein #TheManilaTimes#KeepUpWithTheTimes Hosted on Acast. See acast.com/privacy for more information.

Neoborn Caveman, your green-tea-slurping host, invites his Purple Rabbit crew (that's you, not the parasitic overlords) to an open tea-house conversation. Sip along as we explore government overreach, from the 1952 UK ID card abolition to modern digital ID scams like Oracle's TikTok ties threatening sovereignty. Neoborn shares personal health journeys, promoting natural remedies like green tea and rejecting victim-playing culture. He calls out media manipulation—think asteroid fear-mongering and AI truth-twisting—and warns against generalizing groups. From Eurovision boycotts to Canadian policy oversteps, this episode urges preserving stories to counter division, learning from history, and embracing your unique worth to stay free-spirited. Gather for more unfiltered episodes at patreon.com/theneoborncavemanshow . With the special appearance of Sean Martin (only in the Patreon episode)Music guests are Sweet Water, Broken Colors, pMad and many othersKey TakeawaysQuestion digital IDs and government motives; the UK's 1952 ID abolition shows control can be reversed.Data privacy is under threat; Oracle-TikTok deals and Mediterranean data schemes demand resistance.Natural remedies, like green tea, can support health, as shown in Neoborn's personal experiments.Media and AI distort reality; bots and fear-mongering (e.g., Apophis asteroid) undermine truth—rely on logic.Human connections through stories heal division and isolation, fostering real bonds.Storytelling preserves personal and historical truths, countering manipulation and neglect.Generalizing groups (ethnicity, politics) fuels hate—judge actions, not people, to avoid historical traps.Historical lessons (UK IDs, population exchanges) warn against unchecked power—act proactively.Embrace your unique value; growth through trials silences naysayers, inner and outer.Sound Bites“Are we the lost souls or who we are? Are we the victims of the new Project Blue Beam coming?“I don't need drugs to breathe. It's interesting, right?”“Don't generalize. If you say all Chinese are bad, then what about Jackie Chan?”“Only the unloved hate, the immature.”“You are special, you are amazing, you are one of a kind."“Prevent before it happens. You know it's a scheme, a scam and a political maneuver.”Timestamps00:00 Welcome to The Neoborn Caveman Show00:47 Exploring Project Blue Beam and Psyops01:12 Green Tea Rituals and Freedom's Erosion05:15 Personal Challenges and Societal Issues07:40 Social Media and Asteroid Fear-Mongering10:04 Digital IDs and Government Overreach12:24 Data Privacy and Tech Control14:47 Government Lies and Public Deception17:16 Canadian Overreach and Freedom Convoy19:39 Natural Remedies and Big Pharma Critique21:43 Media Manipulation and AI Truth-Twisting29:51 Open Tea House Conversations32:13 Human Connections Over News and Noise34:25 Kids' Punk Rock and Creative Expression36:30 Building Real Human Connections38:54 Storytelling to Preserve Humanity40:48 Excuses vs. Genuine Connection46:07 History's Dark Lessons on Control48:30 Eurovision Boycotts and Political Art50:51 Rejecting Generalizations in Israel-Palestine55:21 Rejecting Generalizations and Division57:14 Historical Context for Unity59:44 Only the Unloved Hate01:00:39 UK's ID Card History Lesson01:04:17 Resisting Digital Control Now01:05:52 Embracing Your Unique GreatnessHumanity centered satirical takes on the world & news + music - with a marble mouthed host.Free speech marinated in comedy.Supporting Purple Rabbits. Hosted on Acast. See acast.com/privacy for more information.

It's no secret that the police and legal system often fail to protect women from creeps and weirdos. So, this week on the show, Comedian Rosie Train provides tips on how to better protect yourself from those creeps, including using a hidden camera detector. You can get additional tips, and learn more over at StupidSexyPrivacy.com

Michael welcomes  Dr. Sandy Rolfe, WEDI's Genomics Workgroup Chair Clinical Content Lead, InterQual, Optum Insight Lesley Berkeyheiser, WEDI's Privacy & Security Workgroup Co-Chair, Assessor with DirectTrust Thanh Thien Nguyen, WEDI's Privacy & Security Workgroup Co-Chair, Executive Director, Kaiser Permanente to discuss their workgroups working together to spotlight the intersection between direct to consumer (DTC) genetic tests and some different privacy and security issues associated with these types of tests. The two workgroups collaborated to develop a Fact Sheet to help consumers take action to protect their data, available here- https://www.wedi.org/2025/07/12/direct-to-consumer-genetic-dna-testing-actions-consumers-can-take-to-protect-their-health-data/  Current regulations and requirements Thanh Thien references in the episode HIPAA Privacy Rule- https://www.hhs.gov/hipaa/for-professionals/privacy/index.html HIPAA Security Rule- https://www.hhs.gov/hipaa/for-professionals/security/index.html Genetic Information Nondiscrimination Act of 2008- https://www.govinfo.gov/content/pkg/PLAW-110publ233/pdf/PLAW-110publ233.pdf Federal Trade Commission Act- https://www.ftc.gov/legal-library/browse/statutes/federal-trade-commission-act California Consumer Privacy Act of 2018- https://oag.ca.gov/privacy/ccpa California Privacy Rights Act- https://thecpra.org/ State Privacy Law Tracker- https://iapp.org/resources/article/us-state-privacy-legislation-tracker/

Season 3 - we're back with a muddle of James's!To kick the season off, hosts James Lawther and Jimmy Barber explore how we can all use artificial intelligence at work with special guest James Crawford. Unless you're living under a rock, you'll be aware of the promise of AI revolutionising the workplace. This tends to focus on how organisations, governments, or public services will utilise AI and automate various jobs. Today, we explore how we as individuals can use AI to help us get ahead and revolutionise how we work. AI expert James Crawford shares his 5P's model and also explains which AI systems work best for specific jobs. He then discusses some of the uses and best practices he has experienced during his travels. Plus, you'll hear 'what we did last summer', but somewhat less dramatic than the films or songs! So if you're worried about getting left behind in the AI revolution, check this episode out!Got a question - get in touch. Click here.

This week, comedian Rosie Tran tells you how there's a stalker in your driveway, and it's not a human. She also explains why you want to pick up a Microphone Blocker. Then, we have not one but two book excerpts from BJ Mendelson's first book on Privacy, "Privacy: And How We Get It Back" to share with you. Come get your show notes, and learn more about how creepy your car is, at https://www.stupidsexyprivacy.com

Join CIT's President and CEO, Kyle, as he explores the transformative impact of AI on IT support and help desks. Discover how AI augments support tasks, speeds up resolution times, and brings data-driven insights to enhance customer service. Kyle also addresses data privacy concerns, the human role in AI-driven environments, and the future skillsets required for IT professionals in an AI-enhanced landscape. Join us to explore the present and future of AI in IT support.00:00 Introduction to AI in IT Support01:27 The Role of AI in Help Desk Operations03:03 Advanced AI Capabilities and Benefits05:22 Unexpected Benefits of AI Implementation08:43 Data Privacy and Security Concerns11:17 Human Element and AI Limitations14:57 Future Skills for IT Professionals18:24 Conclusion and Future Prospects

Industry visionary Graham Wilkinson joins the podcast to talk about the industry's adoption of AI, where it's working and where it's not. The team examines the role of AI across generative advertising, data fragmentation, breaking down silos and the genesis of creativity.Thanks for listening! Follow us on Twitter and Instagram or find us on Facebook.

This week, Comedian Rosie Tran explains how your TV has been watching you watch ... Well, everything. And it's not keeping those embarrassing secrets to itself! Find out who your SmartTV is snitching on you with, and we'll also tell you what the best stickers are for covering up your cameras. Author and Podcast Producer BJ Mendelson also returns with part two of his interview with Block Party CEO and Founder, Tracy Chou. Get the show notes and more information at https://www.stupidsexyprivacy.com

Buy AI+Smartphone from the link below :https://www.flipkart.com/ai-pulse-black-64-gb/p/itm42a123557bad4?pid=MOBHDDQXWA6PBGZJ https://www.flipkart.com/ai-nova-5g-purple-128-gb/p/itm6116e1e30cc63?pid=MOBHDDQXJVZXFCWPCheck out BeerBiceps SkillHouse's YouTube 1O1 Course - https://youtube.beerbicepsskillhouse.in/youtube-101Share your guest suggestions hereMail - connect@beerbiceps.comLink - https://forms.gle/aoMHY9EE3Cg3Tqdx9BeerBiceps SkillHouse को Social Media पर Follow करे :-YouTube : https://www.youtube.com/channel/UC2-Y36TqZ5MH6N1cWpmsBRQ Instagram : https://www.instagram.com/beerbiceps_skillhouseWebsite : https://beerbicepsskillhouse.inFor any other queries EMAIL: support@beerbicepsskillhouse.comIn case of any payment-related issues, kindly write to support@tagmango.comLevel Supermind - Mind Performance App को Download करिए यहाँ से

In this episode of Connected With Latham, London partner Gail Crawford and Frankfurt partner Tim Wybitul discuss the EU Data Act, and what this means for data privacy and cybersecurity practices.   This podcast is provided as a service of Latham & Watkins LLP. Listening to this podcast does not create an attorney client relationship between you and Latham & Watkins LLP, and you should not send confidential information to Latham & Watkins LLP. While we make every effort to assure that the content of this podcast is accurate, comprehensive, and current, we do not warrant or guarantee any of those things and you may not rely on this podcast as a substitute for legal research and/or consulting a qualified attorney. Listening to this podcast is not a substitute for engaging a lawyer to advise on your individual needs. Should you require legal advice on the issues covered in this podcast, please consult a qualified attorney. Under New York's Code of Professional Responsibility, portions of this communication contain attorney advertising. Prior results do not guarantee a similar outcome. Results depend upon a variety of factors unique to each representation. Please direct all inquiries regarding the conduct of Latham and Watkins attorneys under New York's Disciplinary Rules to Latham & Watkins LLP, 1271 Avenue of the Americas, New York, NY 10020, Phone: 1.212.906.1200

Send us a textIn this special episode, we speak with Daphne Li, CEO of Common Sense Privacy, alongside leaders from Prodigy Education, AI for Equity, MagicSchool AI, and ClassDojo—recipients of the Privacy Seal. Together, we explore how the edtech sector is tackling one of its biggest challenges: earning trust through responsible AI and data privacy practices.

Nonprofits lean on outside platforms to save time and stretch budgets—but those relationships can quietly expose sensitive donor, client, and payment data. In this episode, Senior Cybersecurity Advisor Parker Brissette of Richey May explains how to recognize and manage third-party software risk before it becomes tomorrow's headline. He starts with a simple lens: follow the data. Where is it stored? Who can touch it—directly or indirectly? Many teams only think about contracted vendors, but Parker widens the aperture to “shadow IT” and consumer tools staff use without formal approval. As he puts it, “Third parties is really anybody that can touch the data at any point in your business, whether you have an agreement with them or maybe not.”From privacy regulations (GDPR, CCPA) to sector-specific rules (HIPAA, PCI), nonprofits carry legal and reputational exposure the moment personal information enters their systems. Parker offers practical steps: inventory paid tools via your accounting system; ask, “If this vendor vanished tomorrow, what would break?”; and press vendors for proof—SOC 2 reports, ISO 27001, or completed security questionnaires. For organizations without a CIO, he recommends clear contracts and one non-negotiable safeguard: “The biggest thing that I recommend in any third-party engagement is setting an expectation of having cyber insurance, because that's a big protection for you financially.”AI enters the picture with both promise and peril. Consumer AI tools can learn from and retain your uploads, potentially exposing proprietary or personal information. Enterprise agreements (e.g., Microsoft Copilot) can offer stronger data protections, but only if configured and used correctly. Parker's guidance is pragmatic: don't ban AI; set guardrails, choose vetted tools, and train teams.Finally, he urges preparation and transparency. Incidents can happen—even with good controls. Donors and corporate funders expect frank communication about what protections exist and what happens if data is exposed. Build trust now by documenting safeguards, validating vendors, and rehearsing your response.You don't have to be a security expert to make smart choices—but you do need a map: know your systems, test your assumptions, ask vendors for evidence, and write risk into your contracts and budgets. That approach turns anxiety into action—and preserves the trust your mission depends on.Find us Live daily on YouTube!Find us Live daily on LinkedIn!Find us Live daily on X: @Nonprofit_ShowOur national co-hosts and amazing guests discuss management, money and missions of nonprofits! 12:30pm ET 11:30am CT 10:30am MT 9:30am PTSend us your ideas for Show Guests or Topics: HelpDesk@AmericanNonprofitAcademy.comVisit us on the web:The Nonprofit Show

In this episode of Bulletproof Your Marketplace, Jeremy is joined by Stephen Fusco, Data Privacy Officer & Sr. Counsel at Danone North America, as they meet to analyze a new chapter of the book Bulletproof Your Marketplace, focusing on Data Privacy and Data Security — Policies and Practices. Stephen shares lessons from real-world breaches and compliance challenges, offering practical guidance on data mapping, minimizing collection, building strong governance structures, and avoiding common mistakes that can lead to costly lawsuits. They also explore the fragmented U.S. regulatory landscape, consumer consent, privacy notices, cookie policies, and the risks of neglecting these essentials. This episode delivers actionable takeaways to help marketplace operators build a safer, more resilient platform while reducing exposure to regulatory penalties, class actions, and reputational damage.

Anthropic agrees to pay 1.5 billion to settle class action, Mark Zuckerberg no relation to Mark Zuckerberg keeps having his Facebook account suspended. UK Govt trial of MS365 copilot shows no clear productivity boost, Microsoft ULA explained by NotebookLM AI thanks to Zig in NC, Credit Card use on internet, New Laptop but I hate Microsoft, Google ordered to pay 425 million for lack of Data Privacy.

This week, comedian Rosie Tran tells you what goes inside your privacy notebook. It's not just for sharing your deepest, darkest secrets. Although you can totally use it for that, too. Privacy expert, BJ Mendelson, then interviews the founder and CEO of Block Party, Tracy Chou. If you're going to continue to use any social media platform, you're going to want to make sure you're also using Block Party. BJ and Tracy explain why. Get the full show notes and additional details at https://www.stupidsexyprivacy.com

We are all aware of issues with data privacy on the internet and on your mobile devices, but how aware are you about the data being collected from your car? We discuss how this data is different from that of your other connected devices and how well automakers are protecting your data. We review a benchmarking report from Privacy4Cars which evaluates how effectively automakers honor your privacy rights.

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

In this episode, I talk to Dough Moquet who left the Air Force after 10 years to return home to Connecticut, where he was successfully leveraged his experiences as an Air Force JAG to land his current gig.

This week, comedian Rosie Tran tries to convince you to buy a physical privacy notebook, and write your password manager's master password inside its cover. In our show notes, found over at StupidSexyPrivacy.com, privacy expert, BJ Mendelson, walk through your choices of BitWarden, 1Password, and the offline option KeePassXC for password managers. And if you stick around at the end of this episode, you can hear an excerpt from BJ Mendelson's first book on privacy, as read by Broadway actor, Roger Wayne. 

In this episode of Bulletproof Your Marketplace, Jeremy Gottschalk sits down with seasoned legal counsel Krishan Thakker to unpack how user contracts and robust privacy practices can act as liability shields for digital platforms. Drawing on over 15 years advising global technology, e-commerce, and social media companies, Krish shares his expertise at the intersection of data privacy, regulatory compliance, AI governance, and trust & safety. Together, they explore why privacy can no longer be an afterthought, the dangers of conflating terms of use with privacy policies, and the financial and reputational fallout from neglecting compliance—illustrated through high-profile failure. Krish also outlines practical steps for platform operators, including conducting a data audit, adopting a “less is more” approach to PII collection, and drafting clear, standalone user agreements. Whether you're a founder, legal counsel, or trust & safety leader, this episode delivers actionable strategies to protect your platform, build user trust, and stay ahead of evolving regulations.

Ad fraud and data privacy are changing the way advertisers and consumers interact. In this episode of Women of Influence, Nina Talcott, Director of Research Communications and Media Engagement at Pixalate, explains how fraud drains billions from the industry, what new privacy laws mean for consumers, and why transparency is critical. She also shares her perspective on influence, the value of mentorship, and the advice she would give her younger self.Episode Highlights:How Pixalate combats ad fraud and protects consumer data.Billions lost to bots, spoofing, and fake apps on connected TV.Why consumer privacy and new data laws matter.Influence built through one-on-one connections.Advice to her younger self: be authentic and embrace individuality.Links and ResourcesConnect with Nina Talcott on LinkedInConnect with Pixalate on LinkedInLearn More about PixalateWant more from SheSpeaks?* Sign up for our podcast newsletter HERE! * Connect with us on Instagram, FB & Twitter @shespeaksup Contact us at podcast@shespeaks.com WATCH our podcast on YouTube @SheSpeaksTV

Register for #WISE12 - Join us for the world's premier education innovation summit: https://wise-qatar.org/wise-12/ In this episode of WISE On Air, we explore the human side of #artificialintelligence with Professor Munther Dahleh, founding director of MIT's Institute for Data Systems and Society. As machines become increasingly capable, Munther argues that understanding people, not just #algorithms, will determine whether AI truly serves humanity. From his early work designing control systems for spacecraft to his current focus on societal applications of #AI, Munther shares insights on: - The "trilingual student" concept - why future innovators need technical skills, social science understanding, and domain expertise - Why he uses ChatGPT for reports but never for recommendation letters - The hidden dangers of "mediocre systems at scale" - How self-driving cars force us to confront moral decisions we never ask human drivers to make - Why he wishes he had studied more philosophy and anthropology - The future of healthcare, education, and work in an AI-driven world Munther challenges the notion that technological advancement alone will solve society's problems, emphasizing that the most critical breakthroughs may come from understanding human behavior, ethics, and social systems. About the Guest: Professor Munther Dahleh is a Professor at MIT and founding director of the Institute for Data Systems and Society. He is the author of "Data Systems and Society: Harnessing AI for Societal Good" and has spent over three decades working at the intersection of #technology and #society. 00:00 Introduction and Personal Experience with ChatGPT 00:22 Welcome to Wise On Air 00:30 Introducing Professor Munther Dahleh 02:02 The Journey to AI and Control Systems 04:13 From Machines to Social Networks 13:45 The Concept of Trilingual Students 15:28 AI's Impact on Society and Education 25:53 The Future of Jobs and Continuous Learning 29:10 Open Access and Vocational Education 30:31 Ethics and Data Privacy in AI 33:29 Self-Driving Cars and Moral Dilemmas 37:39 Global AI Collaboration and Geopolitics 42:36 AI's Role in Healthcare and Research 49:59 The Future of AI and Human Expertise 52:45 Final Thoughts and Advice for the Next Generation

In this episode, we talk with Will Grewal, CEO of HomeStack, about how real estate tech is changing. He shares insights from his trip to Japan about a "12-star service experience" and how that can be applied to real estate. We discuss the shift from websites to mobile apps, the impact of AI on the industry, and why leverage, not money, is the ultimate key to business growth. Will explains why the future of real estate is not about more data, but about hyper-personalization and empathy. Connect with Will on LinkedIn. Learn more about HomeStack on - Facebook - LinkedIn - Instagram or online at homestack.com. You asked for it. We delivered. Check out our new merch! https://merch.realestateinsidersunfiltered.com/   Follow Real Estate Insiders Unfiltered Podcast on Instagram - YouTube - Facebook - TikTok. Visit us online at realestateinsidersunfiltered.com.   Link to Facebook Page: https://www.facebook.com/RealEstateInsidersUnfiltered Link to Instagram Page: https://www.instagram.com/realestateinsiderspod/ Link to YouTube Page: https://www.youtube.com/@RealEstateInsidersUnfiltered Link to TikTok Page: https://www.tiktok.com/@realestateinsiderspod Link to website: https://realestateinsidersunfiltered.com This podcast is produced by Two Brothers Creative. https://twobrotherscreative.com/contact/  

Hustle & Flowchart is BACK!!! Joe Fier returns to the mic with special guest (and business partner) Scott Duffy. The duo dive deep into their break from the podcast, their ambitious work with the Delphi AI platform, and how they're helping entrepreneurs create scalable “digital twins” (or AI clones) to power up their businesses in the age of artificial intelligence. This is a jam-packed conversation covering AI adoption, opportunities for trainers and creators, practical business models, and the step-by-step process for building your own AI-powered digital persona. Whether you're an AI newbie or eager to scale your expertise, you'll walk away inspired and ready for the future.Topics DiscussedJoe's Hiatus & Podcast Reinvention: Why Joe took a break, lessons learned, and the importance of conscious creation.The AI Opportunity: How Joe & Scott are riding the AI wave, building digital twins and seeing firsthand how few businesses are harnessing AI effectively.Breaking Down “Clones”: What is an AI clone/digital twin? Scripted vs. non-scripted clones, use cases, and debunking common fears/misconceptions.AI in the Training Industry: Real examples of how trainers, authors, and content creators are using Delphi AI to scale their expertise and deepen community engagement.Business Model Deep Dive: Inside their Managed Service Provider (MSP) model—how it differs from a traditional agency and how they support clients long-term.Step-by-Step: Building a Clone: Their proprietary onboarding, content organization, and prompt-design process for building powerful, accurate digital twins.Monetization & Use Cases: Lead gen, premium memberships, knowledge bases, internal team training, legacy archiving, and even unique personal journaling/cloning applications.Data Privacy and Security: Clear explanation of how Delphi secures your data versus large language models like ChatGPT.Legacy, Human Connection, and the Future of AI: Why “leaning in” to AI is the best way to stay ahead, and how human connection and wisdom can scale in a positive way with the right tools.AIBuildTeam's Client Experience: How their team supports clients from vision to launch, ongoing training, and continuous optimization.Call to Action for Early Adopters: Encouragement to “run toward AI”—and how listeners can get involved.Resources MentionedDelphi AI: https://hustleandflowchart.com/delphi AI Build Team: https://aibuildteam.ai/Explore Page on Delphi: https://www.delphi.ai/explorePodcast Episode with Dara Ladjevardian on Hustle & Flowchart: https://hustleandflowchart.com/the-future-of-mentorship-how-delphis-cloning-technology-is-democratizing-access-to-expertise-dara-ladjevardian/ Connect with UsWebsite: Hustle & Flowchart PodcastAI Build Team: aibuildteam.aiInstagram: @hustleandflowchartYouTube Channel: Subscribe...

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss AI data privacy and how AI companies use your data, especially with free versions. You will learn how to approach terms of service agreements. You will understand the real risks to your privacy when inputting sensitive information. You will discover how AI models train on your data and what true data privacy solutions exist. Watch this episode to protect your information! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-data-privacy-review.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In Ear Insights, let’s address a question and give as close to a definitive answer as we can—one of the most common questions asked during our keynotes, our workshops, in our Slack Group, on LinkedIn, everywhere: how do AI companies use your data, particularly if using the free version of a product? A lot of people say, “Be careful what you put in AI. It can learn from your data. You could be leaking confidential data. What’s going on?” So, Katie, before I launch into a tirade which could take hours long, let me ask you, as someone who is the less technical of the two of us, what do you think happens when AI companies are using your data? Katie Robbert – 00:43 Well, here’s the bottom line for me: AI is any other piece of software that you have to read the terms in use and sign their agreement for. Great examples are all the different social media platforms. And we’ve talked about this before, I often get a chuckle—probably in a more sinister way than it should be—of people who will copy and paste this post of something along the lines of, “I do not give Facebook permission to use my data. I do not give Facebook permission to use my images.” And it goes on and on, and it says copy and paste so that Facebook can’t use your information. And bless their hearts, the fact that you’re on the platform means that you have agreed to let them do so. Katie Robbert – 01:37 If not, then you need to have read the terms, the terms of use that explicitly says, “By signing up for this platform, you agree to let us use your information.” Then it sort of lists out what it’s going to use, how it’s going to use it, because legally they have to do that. When I was a product manager and we were converting our clinical trial outputs into commercial products, we had to spend a lot of time with the legal teams writing up those terms of use: “This is how we’re going to use only marketing data. This is how we’re going to use only your registration form data.” When I hear people getting nervous about, “Is AI using my data?” My first thought is, “Yeah, no kidding.” Katie Robbert – 02:27 It’s a piece of software that you’re putting information into, and if you didn’t want that to happen, don’t use it. It’s literally, this is why people build these pieces of software and then give them away for free to the public, hoping that people will put information into them. In the case of AI, it’s to train the models or whatever the situation is. At the end of the day, there is someone at that company sitting at a desk hoping you’re going to give them information that they can do data mining on. That is the bottom line. I hate to be the one to break it to you. We at Trust Insights are very transparent. We have forms; we collect your data that goes into our CRM. Katie Robbert – 03:15 Unless you opt out, you’re going to get an email from us. That is how business works. So I guess it was my turn to go on a very long rant about this. At the end of the day, yes, the answer is yes, period. These companies are using your data. It is on you to read the terms of use to see how. So, Chris, my friend, what do we actually—what’s useful? What do we need to know about how these models are using data in the publicly available versions? Christopher S. Penn – 03:51 I feel like we should have busted out this animation. Katie Robbert – 03:56 Oh. I don’t know why it yells at the end like that, but yes, that was a “Ranty Pants” rant. I don’t know. I guess it’s just I get frustrated. I get that there’s an education component. I do. I totally understand that new technology—there needs to be education. At the end of the day, it’s no different from any other piece of software that has terms of use. If you sign up with an email address, you’re likely going to get all of their promotional emails. If you have to put in a password, then that means that you are probably creating some kind of a profile that they’re going to use that information to create personas and different segments. If you are then putting information into their system, guess what? Katie Robbert – 04:44 They have to store that somewhere so that they can give it back to you. It’s likely on a database that’s on their servers. And guess who owns those servers? They do. Therefore, they own that data. So unless they’re doing something allowing you to build a local model—which Chris has covered in previous podcasts and livestreams, which you can go to Trust Insights.AI YouTube, go to our “So What” playlist, and you can find how to build a local model—that is one of the only ways that you can fully protect your data against going into their models because it’s all hosted locally. But it’s not easy to do. So needless to say, Ranty Pants engaged. Use your brains, people. Christopher S. Penn – 05:29 Use your brains. We have a GPT. In fact, let’s put it in this week’s Trust Insights newsletter. If you’re not subscribed to it, just go to Trust Insights.AI/newsletter. We have a GPT—just copy and paste the terms of service. Copy paste the whole page, paste in the GPT, and we’ll tell you how likely it is that you have given permission to a company to train on your data. With that, there are two different vulnerabilities when you’re using any AI tool. The first prerequisite golden rule: if you ain’t paying, you’re the product. We warn people about this all the time. Second, the prompts that you give and their responses are the things that AI companies are going to use to train on. Christopher S. Penn – 06:21 This has different implications for privacy depending on who you are. The prompts themselves, including all the files and things you upload, are stored verbatim in every AI system, no matter what it is, for the average user. So when you go to ChatGPT or Gemini or Claude, they will store what you’ve prompted, documents you’ve uploaded, and that can be seen by another human. Depending on the terms of service, every platform has a carve out saying, “Hey, if you ask it to do something stupid, like ‘How do I build this very dangerous thing?’ and it triggers a warning, that prompt is now eligible for human review.” That’s just basic common sense. That’s one side. Christopher S. Penn – 07:08 So if you’re putting something there so sensitive that you cannot risk having another human being look at it, you can’t use any AI system other than one that’s running on your own hardware. The second side, which is to the general public, is what happens with that data once it’s been incorporated into model training. If you’re using a tool that allows model training—and here’s what this means—the verbatim documents and the verbatim prompts are not going to appear in a GPT-5. What a company like OpenAI or Google or whoever will do is they will add those documents to their library and then train a model on the prompt and the response to say, “Did this user, when they prompted this thing, get a good response?” Christopher S. Penn – 07:52 If so, good. Let’s then take that document, digest it down into the statistics that it makes up, and that gets incorporated into the rest of the model. The way I explain it to people in a non-technical fashion is: imagine you had a glass full of colored sand—it’s a little rainbow glass of colored sand. And you went out to the desert, like the main desert or whatever, and you just poured the glass out on the ground. That’s the equivalent of putting a prompt into someone’s trained data set. Can you go and scoop up some of the colored sand that was your sand out of the glass from the desert? Yes, you can. Is it in the order that it was in when you first had it in the glass? It is not. Christopher S. Penn – 08:35 So the ability for someone to reconstruct your original prompts and the original data you uploaded from a public model, GPT-5, is extremely low. Extremely low. They would need to know what the original prompt was, effectively, to do that, which then if they know that, then you’ve got different privacy problems. But is your data in there? Yes. Can it be used against you by the general public? Almost certainly not. Can the originals be seen by an employee of OpenAI? Yes. Katie Robbert – 09:08 And I think that’s the key: so you’re saying, will the general public see it? No. But will a human see it? Yes. So if the answer is yes to any of those questions, that’s the way that you need to proceed. We’ve talked about protected health information and personally identifiable information and sensitive financial information, and just go ahead and not put that information into a large language model. But there are systems built specifically to handle that data. And just like a large language model, there is a human on the other side of it seeing it. Katie Robbert – 09:48 So since we’re on the topic of data privacy, I want to ask your opinion on systems like WhatsApp, because they tend to pride themselves, and they have their commercials. Everything you see on TV is clearly the truth. There’s no lies there. They have their commercials saying that the data is fully encrypted in such a way that you can pass messages back and forth, and nobody on their team can see it. They can’t understand what it is. So you could be saying totally heinous things—that’s sort of what they’re implying—and nobody is going to call you out on it. How true do you think that is? Christopher S. Penn – 10:35 There are two different angles to this. One is the liability angle. If you make a commercial claim and then you violate that claim, you are liable for a very large lawsuit. On the one hand is the risk management side. On the other hand, as reported in Reuters last week, Meta has a very different set of ethics internally than the rest of us do. For the most part, there’s a whole big exposé on what they consider acceptable use for their own language models. And some of the examples are quite disturbing. So I can’t say without looking at the codebase or seeing if they have been audited by a trustworthy external party how trustworthy they actually are. There are other companies and applications—Signal comes to mind—that have done very rigorous third-party audits. Christopher S. Penn – 11:24 There are other platforms that actually do the encryption in the hardware—Apple, for example, in its Secure Enclave and its iOS devices. They have also submitted to third-party auditing firms to audit. I don’t know. So my first stop would be: has WhatsApp been audited by a trusted impartial third-party? Katie Robbert – 11:45 So I think you’re hitting on something important. That brings us back to the point of the podcast, which is, how much are these open models using my data? The thing that you said that strikes me is Meta, for example—they have an AI model. Their view on what’s ethical and what’s trustworthy is subjective. It’s not something that I would necessarily agree with, that you would necessarily agree with. And that’s true of any software company because, once again, at the end of the day, the software is built by humans making human judgments. And what I see as something that should be protected and private is not necessarily what the makers of this model see as what should be protected and private because it doesn’t serve their agenda. We have different agendas. Katie Robbert – 12:46 My agenda: get some quick answers and don’t dig too deep into my personal life; you stay out of it. They’re like, “No, we’re going to dig deeper because it’s going to help us give you more tailored and personalized answers.” So we have different agendas. That’s just a very simple example. Christopher S. Penn – 13:04 It’s a simple example, but it’s a very clear example because it goes back to aligning incentives. What are the incentives that they’re offering in exchange for your data? What do you get? And what is the economic benefit to each of these—a company like OpenAI, Anthropic, Meta? They all have economic incentives, and part of responsible use of AI for us as end users is to figure out what are they incentivizing? And is that something that is, frankly, fair? Are you willing to trade off all of your medical privacy for slightly better ads? I think most people say probably no. Katie Robbert – 13:46 Right. Christopher S. Penn – 13:46 That sounds like a good deal to us. Would you trade your private medical data for better medical diagnosis? Maybe so, if we don’t know what the incentives are. That’s our first stop: to figure out what any company is doing with its technology and what their incentives are. It’s the old-fashioned thing we used to do with politicians back when we cared about ethics. We follow the money. What is this politician getting paid? Who’s lobbying them? What outcomes are they likely to generate based on who they’re getting money from? We have to ask the same thing of our AI systems. Katie Robbert – 14:26 Okay, so, and I know the answer to this question, but I’m curious to hear your ranty perspective on it. How much can someone claim, “I didn’t know it was using my data,” and call up, for lack of a better term, call up the company and say, “Hey, I put my data in there and you used it for something else. What the heck? I didn’t know that you were going to do that.” How much water does that hold? Christopher S. Penn – 14:57 About the same as that Facebook warning—a copy and paste. Katie Robbert – 15:01 That’s what I thought you were going to say. But I think that it’s important to talk about it because, again, with any new technology, there is a learning curve of what you can and can’t do safely. You can do whatever you want with it. You just have to be able to understand what the consequences are of doing whatever you want with it. So if you want to tell someone on your team, “Hey, we need to put together some financial forecasting. Can you go ahead and get that done? Here’s our P&L. Here’s our marketing strategy for the year. Here’s our business goals. Can you go ahead and start to figure out what that looks like?” Katie Robbert – 15:39 A lot of people today—2025, late August—are, “it’s probably faster if I use generative AI to do all these things.” So let me upload my documents and let me have generative AI put a plan together because I’ve gotten really good at prompting, which is fine. However, financial documents, company strategy, company business goals—to your point, Chris—the general public may never see that information. They may get flavors of it, but not be able to reconstruct it. But someone, a human, will be able to see the entire thing. And that is the maker of the model. And that may be, they’d be, “Trust Insights just uploaded all of their financial information, and guess what? They’re one of our biggest competitors.” Katie Robbert – 16:34 So they did that knowingly, and now we can see it. So we can use that information for our own gain. Is that a likely scenario? Not in terms of Trust Insights. We are not a competitor to these large language models, but somebody is. Somebody out there is. Christopher S. Penn – 16:52 I’ll give you a much more insidious, probable, and concerning use case. Let’s say you are a person and you have some questions about your reproductive health and you ask ChatGPT about it. ChatGPT is run by OpenAI. OpenAI is an American company. Let’s say an official from the US government says, “I want a list of users who have had conversations about reproductive health,” and the Department of Justice issues this as a warranted request. OpenAI is required by law to comply with the federal government. They don’t get a choice. So the question then becomes, “Could that information be handed to the US government?” The answer is yes. The answer is yes. Christopher S. Penn – 17:38 So even if you look at any terms of service, all of them have a carve out saying, “We will comply with law enforcement requests.” They have to. They have to. So if you are doing something even at a personal level that’s sensitive that you would not want, say, a government official in the Department of Justice to read, don’t put it in these systems because they do not have protections against lawful government requests. Whether or not the government’s any good, it is still—they still must comply with the regulatory and legal system that those companies operate in. Things like that. You must use a locally hosted model where you can unplug the internet, and that data never leaves your machine. Christopher S. Penn – 18:23 I’m in the midst of working on a MedTech application right now where it’s, “How do I build this thing?” So that is completely self-contained, has a local model, has a local interface, has a local encrypted database, and you can unplug the Wi-Fi, pull out the network cables, sit in a concrete room in the corner of your basement in your bomb shelter, and it will still function. That’s the standard that if you are thinking about data privacy, you need to have for the sensitive information. And that begins with regulatory stuff. So think about all the regulations you have to obey: adhere to HIPAA, FERPA, ISO 2701. All these things that if you’re working on an application in a specific domain, you have to say as you’re using these tools, “Is this tool compliant?” Christopher S. Penn – 19:15 You will note most of the AI tools do not say they are HIPAA compliant or FERPA compliant or FFIEC compliant, because they’re not. Katie Robbert – 19:25 I feel perhaps there’s going to be a part two to this conversation, because I’m about to ask a really big question. Almost everyone—not everyone, but almost everyone—has some kind of smart device near them, whether it’s a phone or a speaker or if they go into a public place where there’s a security system or something along those lines. A lot of those devices, depending on the manufacturer, have some kind of AI model built in. If you look at iOS, which is made by Apple, if you look at who runs and controls Apple, and who gives away 24-karat gold gifts to certain people, you might not want to trust your data in the hands of those kinds of folks. Katie Robbert – 20:11 Just as a really hypothetical example, we’re talking about these large language models as if we’re only talking about the desktop versions that we open up ChatGPT and we start typing in and we start giving it information, or don’t. But what we have to also be aware of is if you have a smartphone, which a lot of us do, that even if you disable listening, guess what? It’s still listening. This is a conversation I have with my husband a lot because his tinfoil hat is bigger than mine. We both have them, but his is a little bit thicker. We have some smart speakers in the house. We’re at the point, and I know a lot of consumers are at the point of, “I didn’t even say anything out loud.” Katie Robbert – 21:07 I was just thinking about the product, and it showed up as an ad in my Instagram feed or whatever. The amount of data that you don’t realize you’re giving away for free is, for lack of a better term, disgusting. It’s huge. It’s a lot. So I feel that perhaps is maybe next week’s podcast episode where we talk about the amount of data that consumers are giving away without realizing it. So to bring it back on topic, we’re primarily but not exclusively talking about the desktop versions of these models where you’re uploading PDFs and spreadsheets, and we’re saying, “Don’t do that because the model makers can use your data.” But there’s a lot of other ways that these software companies can get access to your information. Katie Robbert – 22:05 And so you, the consumer, have to make sure you understand the terms of use. Christopher S. Penn – 22:10 Yes. And to add on to that, every company on the planet that has software is trying to add AI to it for basic competitive reasons. However, not all APIs are created the same. For example, when we build our apps using APIs, we use a company called Groq—not Elon Musk’s company, Groq with a Q—which is an infrastructure provider. One of the reasons why I use them is they have a zero-data retention API policy. They do not retain data at all on their APIs. So the moment the request is done, they send the data back, it’s gone. They have no logs, so they can’t. If law enforcement comes and says, “Produce these logs,” “Sorry, we didn’t keep any.” That’s a big consideration. Christopher S. Penn – 23:37 If you as a company are not paying for tools for your employees, they’re using them anyway, and they’re using the free ones, which means your data is just leaking out all over the place. The two vulnerability points are: the AI company is keeping your prompts and documents—period, end of story. It’s unlikely to show up in the public models, but someone could look at that. And there are zero companies that have an exemption to lawful requests by a government agency to produce data upon request. Those are the big headlines. Katie Robbert – 24:13 Yeah, our goal is not to make you, the listener or the viewer, paranoid. We really just want to make sure you understand what you’re dealing with when using these tools. And the same is true. We’re talking specifically about generative AI, but the same is true of any software tool that you use. So take generative AI out of it and just think about general software. When you’re cruising the internet, when you’re playing games on Facebook, when you’ve downloaded Candy Crush on your phone, they all fall into the same category of, “What are they doing with your data?” And so you may say, “I’m not giving it any data.” And guess what? You are. So we can cover that in a different podcast episode. Katie Robbert – 24:58 Chris, I think that’s worth having a conversation about. Christopher S. Penn – 25:01 Absolutely. If you’ve got some thoughts about AI and data privacy and you want to share them, pop by our free Slack group. Go to Trust Insights.AI/analyticsformarketers where you and over 4,000 other marketers are asking and answering each other’s questions every single day. And wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on, go to Trust Insights.AI/TIPodcast. You can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Katie Robbert – 25:30 Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Katie Robbert – 26:23 Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientist to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the “In-Ear Insights” podcast, the “Inbox Insights” newsletter, the “So What” livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Katie Robbert – 27:28 Data storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

The industry is experiencing the ‘Wild West ‘in terms of AI implementation and associate legislation. Leading patent attorney Gene Quinn of IP Watchdog joins the podcast to discuss the complexity and swirl of issues and potential resolutions in both the US and globally, smart modularization approaches for marketers and ultimately adding value for consumers. Thanks for listening! Follow us on Twitter and Instagram or find us on Facebook.

Shawn Tuma has been fighting cyber villains since before “cybersecurity” was even a thing. As Co-Chair of Spencer Fane's Data Privacy & Cybersecurity Practice, he's the guy companies call when the unthinkable happens—whether it's ransomware, data theft, or an IT guy whose credentials include “brother of a cousin.”In this fantastic episode of Counsel Brew, we sit down with Shawn and explore the intricate world of managing critical cybersecurity incidents. Shawn's path to badassery proves that you never know what may lie ahead. Shawn shares his extensive experience in guiding companies through the complexities of cyber attacks, ransomware, and data breaches, a field that didn't even exist when he started his legal career. His unique insights offer valuable guidance for businesses looking to safeguard their operations and maintain resilience in the face of cyber threats.In this episode, we sip French roast and swap stories on: ☕ Why breach response is part adrenaline rush, part group therapy 

News and Updates: “Vibe coding” is redefining software development by letting AI handle most coding tasks — even architecture — through natural-language prompts. Tools like Windsurf, Replit, and Microsoft Copilot let pros and hobbyists alike generate entire apps, but the code still needs close review. Google recently acquired Windsurf's CEO for $2.4B, highlighting AI's growing role in dev workflows. The UK and 24 U.S. states now require age verification (via government ID or face scans) for access to adult content. Critics argue these laws do little to protect children and instead threaten user privacy by collecting sensitive personal data — data often stored insecurely and vulnerable to leaks. Users can mitigate risks by using burner accounts, VPNs, and privacy tools — though laws are evolving, and enforcement may increase. Experts say the laws are easy to bypass and are more likely to harm privacy than improve online safety for kids. Delta is phasing out fixed ticket prices in favor of AI-driven personalized fares, which could eventually target individuals based on their data. Only 3% of fares currently use the system, but Delta expects that to rise to 20% by year's end. Privacy experts warn this “surveillance pricing” could lead to unfair, exploitative practices — especially for lower-income travelers. Music fans were stunned to find AI-generated songs appearing on Spotify under deceased artists' names like Blaze Foley and Guy Clark. The platform removed the tracks after backlash, admitting they violated deceptive content policies — but there's still no label to indicate whether music is AI-generated.

In this episode, Mark Ledlow explores the dynamics of family offices, wealth management, and protective intelligence with Jennifer Laurence, an Estate Management Consultant in Private UHNW Service, and a Founder and President of Luxury Lifestyle Logistics, as they delve into the complexities of servicing high-net-worth individuals. They discuss the evolution of protective intelligence, the necessity of precise communication between executive assistants and security teams, and the operational challenges within high-net-worth estate management. Jennifer also shares her insights on creating a cohesive service culture tailored to individual family needs and the importance of professionalizing estate management practices. As the episode concludes, Jennifer shares information about her consultancy, Luxury Lifestyle Logistics, which specializes in estate management consulting.Learn about all this and more in this episode of The Fearless Mindset Podcast.KEY TAKEAWAYSEstate management for high-net-worth individuals requires personalized, holistic solutions beyond standard hospitality. Security is a spectrum: risk assessments and tailored strategies are essential for each client's unique profile. Privacy and confidentiality are major concerns for both clients and staff in luxury estate management. Every data point—utility bills, car titles, reservations—can be a vulnerability for high-profile individuals. The “circle of trust” must be carefully managed to balance service and privacy.QUOTES“We're serving individuals in their homes, and it's a very close, personable working relationship.” “Security risk is a spectrum. You have to understand what you can do internally, externally, and when it's right to bring in somebody full-time.” “Every single data point that you put your name on is ultimately a weak point for threat mitigation.” “You don't get to make your dinner reservations in your last name anymore. The world is too rich in data.” “Protective intelligence—that's the new era of security.”Get to know more about Jennifer Laurence through the links below.https://www.linkedin.com/in/jenniferlaurence/To hear more episodes of The Fearless Mindset podcast, you can go to https://the-fearless-mindset.simplecast.com/ or listen to major podcasting platforms such as Apple, Google Podcasts, Spotify, etc. You can also subscribe to the Fearless Mindset YouTube Channel to watch episodes on video.

Listen in with host Paul Spain and tech journalist Bill Bennett for a thought-provoking discussion on the government's proposed road user charges and what data tracking in vehicles really means for privacy and everyday Kiwis. Motorola's One NZ partnership and new Phone lineup, Privacy Commissioner's new rules on biometric data, the challenges of cloud and data centre infrastructure, Spark's recent business shifts, and why satellite broadband is shaking up the telco market. If you care about privacy, tech innovation, or how global giants and local players are shaping the future, this episode is packed with insights you won't want to miss. Thanks to our Partners One NZ, Workday, 2degrees, HP, Spark and Gorilla Technology

What is the right way, if there is one at all, to integrate artificial intelligence (AI) technology into our education system? For Sal Khan, CEO of one of the world's largest nonprofit education technology platforms, the answer is to take a step back and ask: Where can AI best complement current pedagogy? If a problem can be solved by pencil and paper, should we really be using AI instead?Khan joins Bethany and Luigi to discuss his recent book, “Brave New Words: How AI Will Revolutionize Education (And Why That's a Good Thing),” in which he makes the case for why the education sector will not only survive but thrive in the age of AI. He shares his 17-year journey to build and grow his organization, which now provides over 10,000 videos on everything from integral calculus to art history, reaching more than 170 million registered users in over 20 languages, mostly for free. Together, the three talk about how and where AI can enhance the learning process: how AI has shifted Khan's philosophy and approach to pedagogy, how it could democratize educational and economic opportunity, and what this all means for traditional modes of learning and instruction in schools and universities. They also discuss concerns about data ownership, Khan's partnerships with tech companies, and the guardrails he proposes to protect education against the monetization of students' data and the concentration of benefits to privileged children. Ultimately, he makes the case for why teachers aren't going anywhere—and leaves aspiring nonprofit and civic leaders with advice on how to build a successful, mission-driven organization.Read a review of Sal Khan's book on ProMarket, written by Capitalisn't team member Matt Lucky.

In this episode, Jaeden discusses Cohere's new AI agent platform, North, which focuses on automating workflows while ensuring data privacy and security for enterprises. He highlights the platform's capabilities, its competitive positioning in the market, and the importance of deploying AI models on private infrastructure. Jaeden also reflects on the future of AI agents and their role in enhancing business operations.Try AI Box: ⁠⁠https://aibox.aiAI Chat YouTube Channel: https://www.youtube.com/@JaedenSchaferJoin my AI Hustle Community: https://www.skool.com/aihustle/aboutYouTube Video: https://youtu.be/jV0M0COxV_EChapters00:00 Introduction to Cohere's New AI Agent Platform02:41 Security and Data Privacy in AI Agents05:55 Cohere's Competitive Edge and Market Positioning08:39 Conclusion and Future Outlook for Cohere

As consumer wearables track everything from our heartbeats to nightly sleep cycles, a new industry has emerged—one focused on making sense of this avalanche of personal health data. In an era when prevention is prized over cure, questions abound: How accurate is the data captured on our wrists and fingers? Can these metrics truly guide us to longer, healthier lives, or are we just generating noise? For Marco Benitez, a biomedical engineer and co-founder of the health data platform Rook, the challenge isn't just about collecting the numbers but turning them into actionable insights—for both individuals and the broader healthcare world. As the medical establishment wrestles with integrating this torrent of information—and as users grapple with privacy and security—a revolution in health is quietly unfolding. Peter Bowes sat down with Benitez to discuss the promise and pitfalls of wearable data, the evolving relationship between Big Pharma and digital health, and what it will take to shift the system from sick care to prevention.___If you value our video and audio content, we would appreciate your support.Any contribution - large or small - is hugely appreciated and helps us to continue sharing insights that could help us all live longer and healthier lives. With gratitude. https://www.buzzsprout.com/87724/support PartiQlar supplementsEnhance your wellness journey with PartiQlar supplements. No magic formulas, just pure single ingredients, like NMN, L-Glutathione, Spermidine, Resveratrol, TMG and Quercetin. Get a 15% discount with the code MASTERAGING15 at PartiQlarSupport the showThe Live Long and Master Aging (LLAMA) podcast, a HealthSpan Media LLC production, shares ideas but does not offer medical advice. If you have health concerns of any kind, or you are considering adopting a new diet or exercise regime, you should consult your doctor.

In this conversation, Erik Taurence discusses the mission and offerings of Data Shopper, a marketing technology platform designed to help small businesses effectively market themselves. He emphasizes the importance of audience targeting, affordability, and the transition to a SaaS model. Erik also addresses concerns about data privacy and the significance of building strong client relationships. The discussion highlights the platform's capabilities in audience identification and marketing campaign management, as well as the company's future goals and commitment to community building. Professional Real Estate Investors - How we can help you: Investor Fuel Mastermind:  Learn more about the Investor Fuel Mastermind, including 100% deal financing, massive discounts from vendors and sponsors you're already using, our world class community of over 150 members, and SO much more here: http://www.investorfuel.com/apply   Investor Machine Marketing Partnership:  Are you looking for consistent, high quality lead generation? Investor Machine is America's #1 lead generation service professional investors. Investor Machine provides true ‘white glove' support to help you build the perfect marketing plan, then we'll execute it for you…talking and working together on an ongoing basis to help you hit YOUR goals! Learn more here: http://www.investormachine.com   Coaching with Mike Hambright:  Interested in 1 on 1 coaching with Mike Hambright? Mike coaches entrepreneurs looking to level up, build coaching or service based businesses (Mike runs multiple 7 and 8 figure a year businesses), building a coaching program and more. Learn more here: https://investorfuel.com/coachingwithmike   Attend a Vacation/Mastermind Retreat with Mike Hambright: Interested in joining a “mini-mastermind” with Mike and his private clients on an upcoming “Retreat”, either at locations like Cabo San Lucas, Napa, Park City ski trip, Yellowstone, or even at Mike's East Texas “Big H Ranch”? Learn more here: http://www.investorfuel.com/retreat   Property Insurance: Join the largest and most investor friendly property insurance provider in 2 minutes. Free to join, and insure all your flips and rentals within minutes! There is NO easier insurance provider on the planet (turn insurance on or off in 1 minute without talking to anyone!), and there's no 15-30% agent mark up through this platform!  Register here: https://myinvestorinsurance.com/   New Real Estate Investors - How we can work together: Investor Fuel Club (Coaching and Deal Partner Community): Looking to kickstart your real estate investing career? Join our one of a kind Coaching Community, Investor Fuel Club, where you'll get trained by some of the best real estate investors in America, and partner with them on deals! You don't need $ for deals…we'll partner with you and hold your hand along the way! Learn More here: http://www.investorfuel.com/club   —--------------------

Yasir Ali, CEO of Polymer, discusses the shortcomings of current security frameworks, emphasizing their outdated nature and lack of real verification. He argues that many organizations still rely on signed statements without providing tangible evidence of compliance with security controls. This disconnect has become increasingly problematic, especially with the rise of AI and the need for more robust data protection measures. Ali highlights that the traditional approach of simply stating compliance is no longer sufficient in a landscape where data privacy and security are paramount.Ali suggests that the solution lies in a more evidence-based approach to security frameworks. He advocates for organizations to provide real-time evidence of their security practices, particularly in relation to how customer data is handled and used. This includes demonstrating that customer data is not being misused for AI training purposes. He believes that the industry needs to move beyond mere documentation and focus on actual compliance and security practices that can be verified.The conversation also touches on the role of customers in demanding better security practices from service providers. While Ali acknowledges that end customers often feel overlooked, he points out that larger organizations, such as banks, have begun to push back against vendors to ensure their data is protected. He emphasizes that smaller businesses must take responsibility for their own data security, as the regulatory environment in the U.S. is not providing sufficient oversight.As AI adoption accelerates through SaaS products, Ali expresses concern about the new vulnerabilities that may arise. He notes that many organizations are integrating AI tools into their existing systems without fully understanding the implications for data security. Polymer aims to address these challenges by providing tools that enhance data security and compliance within SaaS environments, ensuring that organizations can protect their sensitive information while leveraging the benefits of AI. All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

Guest: Dr. Tom Coughlin, President, Coughlin Associates, IEEE Past President (2025) Website: https://tomcoughlin.com FMS Conference: https://futurememorystorage.com/ Episode Summary: Join us for an enlightening conversation with Dr. Tom Coughlin, a seasoned digital storage analyst and consultant with over 40 years in the industry. Tom, the President of Coughlin Associates and former IEEE President, shares unparalleled insights into the foundational technologies shaping our digital world. We delve into the crucial role of memory in AI's development, the surprising realities of storage demand, and the fascinating world of breakthrough memory technologies. Discover why memory often gets overlooked in AI discussions, critical considerations for data privacy, and the global impact of the IEEE. Tom also previews the upcoming Future of Memory and Storage (FMS) conference and offers invaluable career advice for tech entrepreneurs. Key Discussion Points: Behind-the-Scenes of Storage Innovation: Tom shares a surprising story about the 25-year research journey behind HAMR technology now rolling out in HDDs. Evolving Storage Demands: Learn how SSDs have become primary data center storage and replaced HDDs in personal computers and consumer applications. Understand HDDs' shift to colder storage in data centers—this is their growth market, and much of the world's data lives on HDDs. Discover magnetic tape's vital role in archiving and backing up cloud data. Explore new archive storage technologies being developed, such as optical recording and DNA storage. Memory's Critical Role in AI: Memory, particularly DRAM, is playing a big role in training AI models. Approaches are emerging that reduce the need for expensive DRAM (especially in HBM) for inference applications, using storage technologies like SSDs (e.g., Kioxia's AiSAQ for tuning LLMs). er optical storage or DNA for long-term data storage and preservation. Why Memory is Overlooked in AI: Insights into why people tend to focus more on processing (GPUs) than on the data itself, despite memory and storage advances being as impressive as those in GPUs. Data Privacy & Security in Storage: Essential considerations include having copies of data on immutable storage for ransomware recovery, using AI for anomaly detection on networked systems to prevent malware, and proper encryption use in storage systems for data security. The Global Impact of IEEE: Learn about IEEE as the world's largest technical professional organization with nearly half a million members in over 190 countries. IEEE puts on over 2,000 conferences and events each year and publishes a good percentage of the world's technical literature. IEEE standards enable interoperability and industries, with a recent focus on sustainability and ethical AI practices to solve global problems and benefit humanity. Future of Memory and Storage (FMS) Conference: Dr. Coughlin, the general chair, provides details on the 2025 FMS (August 4-7, 2025, at the Santa Clara Convention Center). The conference will feature keynotes by major players in the digital storage and memory industry and sessions covering all major technologies and applications. FMS is the largest independent event focused on digital storage and memory. Highlight Speakers at FMS: Keynote talks include representatives from Kioxia, Fadu, Micron, Silicon Motion, SK hynix, Samsung, Neo, Sandisk, Max Linear, VergeIO, and Kove. There will also be a special session on AI, memory, and storage organized by NVIDIA, and Dr. Coughlin will give a talk on his experiences as IEEE President in 2024. Many parallel sessions will feature speakers from important industry players. Major Disruption in Digital Storage: Dr. Coughlin predicts that just managing the massive amounts of data generated by AI and IoT will be a huge challenge. He also foresees a growing need for technology to ensure data provenance, to identify false information and curate data for AI training. Career Advice for Tech Professionals: Dr. Coughlin advises aspiring tech professionals to be part of their industry and join technical professional organizations like the IEEE. This provides opportunities to develop professional networks and learn important skills like working with others and communicating through volunteer leadership. Learn More About Dr. Tom Coughlin and FMS: Future of Memory and Storage (FMS) Conference: https://futurememorystorage.com/ Tom Coughlin's Work: https://tomcoughlin.com Disclaimer: The information provided in these show notes is for informational purposes only and does not constitute financial, investment, or technical advice. Views expressed by the guest are their own and do not necessarily reflect the views of the podcast host or its affiliates..do not necessarily reflect the views of Finalis Inc. or Finalis Securities LLC, Member FINRA/SIPC.. Listeners should conduct their own research and consult with qualified professionals before making any decisions.  

Join us for a compelling conversation with Geoff Woods, the visionary founder of AI Leadership and the bestselling author of The Ai-Driven Leader, as we discuss how to unleash the power of artificial intelligence in your leadership and your life. This episode promises to transform your understanding of AI, not as a magic bullet but as a thought partner that enhances your strategic thinking and decision-making. Geoff shares invaluable insights from his tenure at Jindal Steel and Power, emphasizing the pivotal role leaders play in driving AI's potential for real enterprise value. Learn how to embrace AI strategically to develop skills and achieve long-term goals, shattering the myth that technology alone can solve your business challenges.Leaders face the dual challenge of empowering growth-oriented team members while supporting those wary of AI's impact. Throughout the episode, we delve into practical strategies for fostering a growth mindset, using AI tools to unlock potential and drive innovation. Discover the CRIT prompt framework—Context, Role, Interview, Task—as a tool to turn AI into a strategic advisor.We also explore ethical leadership in the AI era and navigate the delicate balance between technology and humanity. Geoff and I delve into the importance of empathy, self-awareness, and maintaining data privacy amidst AI advancements. This episode offers a roadmap for leaders eager to redefine their organization's success through AI while keeping humans at the forefront of technological progress.What You'll Learn- Discover innovative ways to integrate AI for enhanced decision-making.- Overcome apprehensions about AI and harness its full potential.- Champion ethical AI practices that align with human-centric values.- Transform AI into a dynamic partner for accelerating growth.- Ignite groundbreaking innovations through strategic AI collaboration.- Equip yourself with futuristic leadership skills in the age of AI.Podcast Timestamps(00:00) - The Ai-Driven Leader(09:06) - Empowering Growth and Overcoming Fear(17:30) - Strategic Planning Acceleration with AI(26:43) - Shaping the Future Workforce With AI(40:18) - Navigating Ethical Leadership in the AI Landscape(49:19) – Considerations for AI Usage, Data Privacy, etc.KEYWORDSPositive Leadership, AI, AI Leadership, Strategic Growth, Decision-making and artificial intelligence, Human-centric, Growth Mindset, Innovation, CRIT prompt, Increasing Collaboration, AI as Competitive Advantage, Virtual Personas, Adaptability, Ethical Considerations with AI, Data Privacy, ChatGPT, Resistance to AI, AI Transformation, CEO Success