Crazy Wisdom

Edouard Machery, a Distinguished Professor at the University of Pittsburgh, dives into the intriguing roots of curiosity and question-asking. He explores how ancient Sumerian writing shaped societal norms and the evolution of curiosity from a vice to a celebrated virtue during the Renaissance. The discussion covers the cross-cultural perceptions of AI and how curiosity distinguishes humans from other species. Insightful links between early scientific practices and philosophical inquiry further illuminate our unique drive to ask 'why' and seek understanding in an ever-changing world.

C.T. Lucero, an astrologer and researcher specializing in ancient astrology, joins to discuss the fascinating intersections of astrology, science, and mysticism. They delve into the historical roots of astrology from Hellenistic Greece to Persian advancements. Lucero reveals how AI is shaping contemporary astrological practices and debates Western versus Vedic astrology. They also explore the significance of the 2020 Saturn-Jupiter conjunction and its impact on societal events, offering a fresh perspective on understanding time cycles and astrological influences.

Ryan Estes, a Buddhist entrepreneur and host of AIforFounders, discusses the fascinating intersections of AI and ancient philosophy. He explores the evolution of communication and consciousness, stressing how technologies impact our realities. A deep dive into the illusion of VR reveals parallels to Buddhist insights. Ryan also tackles themes of data ownership and the tension between scientism and spirituality. The conversation touches on historical knowledge suppression and the fragility of democracy, prompting thoughts on the future structures of power in a tech-driven world.

Join Cathal O’Broin, leader of the PoliePals, as he dives into a world where humans, nature, and machines are interconnected through groundbreaking tech. He discusses the fascinating concepts of neural and cryptographic projector-camera systems, known as the 'truth beam,' that redefine data authenticity. The conversation also highlights innovative tinkering, the shifting landscape of decentralized systems, and the vital role of collaboration in creative problem-solving. Get ready for a journey through art, technology, and the future of decentralized communication!

Larry Diamond, co-founder of Healing with the Diamonds, shares his inspiring journey from severe metabolic illness to vibrant health. He discusses heart-brain coherence and the alchemical journey of self-discovery. Insightfully, he critiques modern dietary misconceptions and emphasizes the importance of ancestral eating. Larry introduces concepts like insulin resistance and the metaphorical 'parasites' feeding on fear, linking personal wellness to deeper spiritual truths and the wisdom of the divine feminine. His insights encourage a revolution in our approach to health.

Join Rory Aronson, CEO of FarmBot, as he shares his journey in revolutionizing home gardening with open-source technology. He explains how FarmBot operates like a CNC machine for gardens, detailing its evolution from basic electronics to sophisticated custom boards. Rory discusses the importance of closed-loop feedback systems for accuracy and the intricate nature of integrating hardware and software. They envision a solarpunk future, highlighting the role of local food systems and the ease of growing your own food, making sustainability accessible for everyone.

Thamir Ali Al-Rahedi, host of the From First Principles podcast, dives deep into the art of questioning and its vital role in entrepreneurship. He discusses how effective questions can drive value while answers merely capture it, challenging the traditional views influenced by scientism. Thamir reflects on Steve Jobs' innovative mindset and spiritual practices as key to Apple’s success. The conversation also touches on the complexities of using AI in education and the generalist's struggle in balancing creativity and discernment.

On this episode of Crazy Wisdom, I, Stewart Alsop, talk with Sarah Boisvert, founder of New Collar AI, about the future of work in manufacturing, the rise of “new collar” jobs, and how technologies like 3D printing and AI are transforming skills training. We cover her experience with Fab Labs, creating a closed-loop AI tutor for workforce development, and the challenges of capturing implicit knowledge from retiring experts. Sarah also shares insights from her books The New Collar Workforce and People of the New Collar Workforce, which feature augmented reality to bring stories to life. You can connect with Sarah through LinkedIn.Check out this GPT we trained on the conversationTimestamps00:00 Sarah introduces New Collar jobs and how digital skills are transforming blue collar roles, discussing FedEx robotics and augmented workers.05:00 Stewart asks about 3D printing challenges; Sarah explains advances in printer automation and the ongoing difficulty of CAD design.10:00 They discuss Generation Z as digital natives, instant gratification, and workforce engagement, highlighting Lean manufacturing principles.15:00 Sarah reflects on how technology speeds life up, her experiences with management training, and the importance of communication on factory floors.20:00 They explore text-to-CAD possibilities, Sarah’s closed-loop AI tutor for manufacturing, and the creation of a proprietary technical database.25:00 Sarah describes the scale of open jobs in 3D printing, challenges of filling them, and shifting perceptions of manufacturing work.30:00 Discussion of robotics safety, small business adoption barriers, and the need for human oversight in automation.35:00 Sarah talks about capturing implicit knowledge from retiring experts, using LLMs for factory floor solutions, and military applications.40:00 Knowledge management, boutique data sets, and AI’s role in preserving technical expertise are explored.45:00 Sarah shares insights on product design, her AR-enabled book, and empowering workers through accessible technical training.Key InsightsSarah Boisvert introduces the concept of “new collar” jobs, emphasizing that modern manufacturing roles now require digital skills traditionally associated with white-collar work. She highlights how roles like CNC machinists and 3D printing operators blend hands-on work with advanced tech, making them both in-demand and engaging for a younger, tech-savvy workforce.The conversation explores the rise of Fab Labs worldwide and their role in democratizing access to manufacturing tools. Boisvert shares her experience founding a Fab Lab in Santa Fe, enabling students and adults to gain practical, project-based experience in CAD design, 3D printing, and repair skills critical for today’s manufacturing environment.Boisvert underscores the persistent skills gap in manufacturing, noting that 600,000 U.S. manufacturing jobs remain unfilled. She attributes part of this to outdated perceptions of manufacturing as “dirty and unsafe,” a narrative she’s actively working to change through her books and training programs that show how modern factories are highly technical and collaborative.She reveals her team’s development of a closed-loop large language model for workforce training. Unlike ChatGPT, this system draws from a proprietary database of technical manuals and expert knowledge, offering precise, context-specific answers for students and workers without relying on the open internet.The episode dives into generational differences in the workplace. Boisvert describes how Gen Z workers are motivated by purpose and efficiency, often asking “why” to understand the impact of their work. She sees Lean principles as a key to managing and empowering this generation to innovate and stay engaged.On automation, Boisvert stresses that robots are not replacing humans in manufacturing but filling labor shortages. She notes that while robots improve efficiency, they require humans to program, monitor, and repair them—skills that new collar workers are being trained to master.Finally, she shares her innovative approach to storytelling in her book People of the New Collar Workforce, which uses augmented reality to bring worker stories to life. Readers can scan photos to hear directly from individuals about their experiences transitioning into high-tech manufacturing careers.

In this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, sit down with returning guest Brian Ahuja to explore a thought-provoking idea he’s been stewing on—could we one day build a robot capable of true partner dancing? From the biomechanics of salsa to the possibilities of AI embodiment, we unpack what it would take to engineer fluid, responsive movement and how that intersects with everything from artificial muscles to the intimacy of tactile feedback. We also touch on Brian’s long-term vision for a potential lab or foundation to tackle this challenge. You can follow Brian and future developments on Twitter @brianahuja.Check out this GPT we trained on the conversationTimestamps00:00 – Brian Ahuja returns to discuss AI embodiment, sparked by his experience in ballroom dance and curiosity about translating physical intelligence into robotics.05:00 – They explore robotics in partner dancing, touching on the difference between choreographed motion and improvisational, responsive movement.10:00 – Brian breaks down human biomechanics, emphasizing that hip motion in dances like salsa originates from knees and feet—not the hips directly.15:00 – The conversation shifts to balance, proprioception, and ocular reflexes, linking them to movement stability in dance.20:00 – They compare robot vs. human movement, noting robots’ jerky motions and the absence of muscle-based initiation.25:00 – The need for haptic feedback is discussed, with Brian detailing how partner dancing depends on tactile signals and real-time response.30:00 – They touch on robotic form factors, questioning whether humanoid robots are the best approach and pondering the design of artificial muscles.35:00 – Brian proposes the idea of the Ahuja Test, gauging if a robot can move so fluidly it's indistinguishable from a human, using dance as the standard.Key InsightsPartner Dancing as a Frontier for Robotics: Brian Ahuja proposes that partner dancing could be a benchmark for robotic embodiment, where success would indicate a robot’s ability to replicate fluid, responsive human movement. This task is far more complex than solo choreography—it requires real-time tactile feedback, improvisation, and nuanced physical communication.Movement Origin in Humans vs. Robots: A critical difference lies in how movement is generated. Human motion begins with muscle contraction, not at the joints. Robots, however, typically initiate movement at joint points, missing the layered interplay of muscles, tendons, and fascia that create smooth, lifelike motion.Haptic Feedback and Improvisation: Real partner dancing involves subtle cues, like pressure through fingertips, to signal direction and timing. For a robot to follow or lead a dance, it would need a highly sensitive haptic feedback system capable of interpreting and responding to these nonverbal signals in real time.The Limits of Current Robotics: Even with advanced robots like the Tesla bot, current movement still appears jerky and lacks the fluidity needed for partner dancing. The mechanical design—especially the lack of artificial musculature—may impose fundamental limits on how closely robots can mimic human motion.Applications Beyond Dance: The implications of this inquiry stretch beyond dance into fields like physical therapy, elder care, and domestic robotics. A robot that could move like a human could handle tasks requiring adaptability, precision, and physical sensitivity.Vision and Systems Thinking: Brian frames the challenge as a systems problem that might start with a lab or foundation. He emphasizes not needing to do everything alone, recognizing the value of building knowledge iteratively through conversations, research, and community.The Ahuja Test: Inspired by the Turing Test, Brian coins the idea of the “Ahuja Test”—a way to measure if a robot can move indistinguishably from a human. He suggests partner dancing could serve as the ultimate proving ground for such a test, given its demand for embodied intelligence and nuanced coordination.

In this episode of Crazy Wisdom, I, Stewart Alsop, speak with Andrew Einhorn, CEO and founder of Level Fields, a platform using AI to help people navigate financial markets through the lens of repeatable, data-driven events. We explore how structured patterns in market news—like CEO departures or earnings surprises—can inform trading strategies, how Level Fields filters noise from financial data, and the emotional nuance of user experience design in fintech. Andrew also shares insights on knowledge graphs, machine learning in finance, and the evolving role of narrative in markets. Stock tips from Level Fields are available on their YouTube channel at Level Fields AI and their website levelfields.ai.Check out this GPT we trained on the conversationTimestamps00:00 – Andrew introduces Level Fields and explains how it identifies event-driven stock movements using AI.05:00 – Discussion of LLMs vs. custom models, and how Level Fields prioritized financial specificity over general AI.10:00 – Stewart asks about ontologies and knowledge graphs; Andrew describes early experiences building rule-based systems.15:00 – They explore the founder’s role in translating problems, UX challenges, and how user expectations shape product design.20:00 – Insight into feedback collection, including a unique refund policy aimed at improving user understanding.25:00 – Andrew breaks down the complexities of user segmentation, churn, and adapting the product for different investor types.30:00 – A look into event types in the market, especially crypto-related announcements and their impact on equities.35:00 – Philosophical turn on narrative vs. fundamentals in finance; how news and groupthink drive large-scale moves.40:00 – Reflection on crypto parallels to dot-com era, and the long-term potential of blockchain infrastructure.45:00 – Deep dive into machine persuasion, LLM training risks, and the influence of opinionated data in financial AI.50:00 – Final thoughts on momentum algos, market manipulation, and the need for transparent, structured data.Key InsightsEvent-Based Investing as Market Forecasting: Andrew Einhorn describes Level Fields as a system for interpreting the market’s weather—detecting recurring events like CEO departures or earnings beats to predict price movements. This approach reframes volatility as something intelligible, giving investors a clearer sense of timing and direction.Building Custom AI for Finance: Rejecting generic large language models, Einhorn’s team developed proprietary AI trained exclusively on financial documents. By narrowing the scope, they increased precision and reduced noise, enabling the platform to focus only on events that truly impact share price behavior.Teaching Through Signals, Not Just Showing: Stewart Alsop notes how Level Fields does more than surface opportunities—it educates. By linking cause and effect in financial movements, the platform helps users build intuition, transforming confusion into understanding through repeated exposure to clear, data-backed patterns.User Expectation vs. Product Vision: Initially, Level Fields emphasized an event-centric UX, but users sought more familiar tools like ticker searches and watchlists. This tension revealed that even innovative technologies must accommodate habitual user flows before inviting them into new ways of thinking.Friction as a Path to Clarity: To elicit meaningful feedback, Level Fields implemented a refund policy that required users to explain what didn’t work. The result wasn’t just better UX insights—it also surfaced emotional blockages around investing and design, sharpening the team’s understanding of what users truly needed.Narrative as a Volatile Market Force: Einhorn points out that groupthink in finance stems from shared academic training, creating reflexive investment patterns tied to economic narratives. These surface-level cycles obscure the deeper, steadier signals that Level Fields seeks to highlight through its data model.AI’s Risk of Amplifying Noise: Alsop and Einhorn explore the darker corners of machine persuasion and LLM-generated content. Since models are trained on public data, including biased and speculative sources, they risk reinforcing distortions. In response, Level Fields emphasizes curated, high-integrity inputs grounded in financial fact.