Leading From Lived Experience | ft. Vivienne Ming

Machines can now write, create, diagnose disease, generate art, and carry on a conversation. Which raises a question bigger than technology itself: if machines can increasingly do what humans do, what makes us uniquely human? In this episode of B The Way Forward, host Brenda Darden Wilkerson sits down with Dr. Vivienne Ming, a theoretical neuroscientist, serial entrepreneur, and one of the world's leading thinkers on artificial intelligence and human potential. She has built AI that improves healthcare, reduces bias in hiring, predicts mental health crises, and reunites orphaned refugees with their families. But the through line of her career isn't machines. It's people. Brenda pushes on the hardest part: how any of this reaches policy, education, and the systems that keep flattening people into something simpler than they are. Dr. Ming's answer is characteristically unsentimental and characteristically hopeful: tension is the reality of a healthy world, all real problems are messy human problems, and the one thing you actually control is you. Because meaningful innovation begins with internal alignment. Before we reshape industries or build remarkable technology, we decide what kind of people we want to become. Connect with Vivienne on LinkedIn We Learned About 1. Algorithmic bias is a mirror, and that's what makes it useful. 2. Credentials predict far less than we think 3. There is no such thing as a problem-solving skill. 4. The cyborg finding: our advantage is exploring the unknown. 5. Depth is engineerable, and so is a better AI. Chapters: 00:00 Welcome Back: What Makes Us Uniquely Human? 01:47 Meet Dr. Vivienne Ming 03:02 Sci-Fi Nerd to Neuroscientist: Where the Philosophy Began 05:18 Real Smiles, Fake Smiles, and the Tip of the Tongue 07:43 Why Algorithmic Bias Tells Us About Ourselves 08:21 Facial Analysis for Hiring: Absolutely Not 08:52 Intelligence Isn't Fixed 09:35 Two Scientists, an EdTech Company, and 2008 Venture Capital 10:33 Ending High-Stakes Testing with AI 12:59 Foundational Skills, Not "Soft" Skills 14:58 People Can Change — And It's Measurable 17:23 Zero-Sum, Positive-Sum, and Peer Role Models 18:08 When Systems Treat the Most Complex Thing as Fixed 19:42 It's an Ecosystem Problem 20:11 Productization, Not Science, Is the Problem 22:38 Why Politicians Are So Vile Online 23:07 The Human Trust and the French Pluralism Project 24:05 You Aren't Your Shallowest Self 25:31 Allostasis: Beware Anyone Selling a Simple Rule 26:31 Finding the Happy Tension 28:58 There Is No Such Thing as a Problem-Solving Skill 30:33 Everyone's a Mashup — And Everyone's Bad at Something 32:34 When Scale Becomes a Flattening Word 33:41 Flattening Produces Divergence, Not Sameness 34:41 Three Levers: Technology, People, and Policy 37:03 From Thirty Parameters to Eight Billion 37:32 When the Worst AI Beats the Best Human 38:28 The World's Slowest Copy-Paste Function 39:26 Meet the Cyborgs 41:24 Our Advantage Is Exploring the Unknown 42:23 Why Don't We Take Courses on Curiosity? 43:21 Socrates: The AI That Refuses to Answer 43:52 Hybrid Intelligence — and Why the Students Hated It 44:12 Getting This Into Education Policy 46:22 Thirty Years of AI and a Thirteenth Company 46:46 Refugee Reunification, Autism, and Repurposed Tech 49:30 Building AI That Leaves You Better 50:56 Don't Wait Until You Have Wrinkles 51:27 The World Gets Better When Old Men Plant Trees 53:52 Diverse Teams, Flat Hierarchies, and Reading People You Disagree With 56:49 Tension Is at the Heart of Everything 57:46 The One Thing You Control Is You 58:48 Character and the Building of a Citizenry 01:00:12 Kuala Lumpur: The Question She Read Completely Wrong 01:02:40 What She Hopes She's Remembered For 01:02:50 Closing Reflections #BTheWayForward #AnitaB #VivienneMing #RobotProof #HumanPotential #AIandEducation #ResponsibleAI #FutureOfWork