Crazy Wisdom

Stewart Alsop hosts a conversation with Oliver Polzin, a founding team member of Meow Wolf and naturalist, exploring the intersection of creativity, conservation, and architecture. Oliver discusses his current postgraduate work at SCI-Arc in Los Angeles studying synthetic landscapes through an architectural lens, his deep fascination with Pleistocene megafauna and the La Brea Tar Pits, and his vision for creating a "biophilic culture" that reframes humanity's relationship with other species and ecosystems. The discussion ranges from Oliver's early work building mud caves at Meow Wolf to his current explorations of AI-assisted design tools, 3D printing with recycled materials, holistic grazing management systems for the Great Plains, and the ancient Amazonian practice of creating terra preta soil—all part of his broader investigation into how we can design interventions for climate and conservation issues while maintaining what makes us fundamentally human.Timestamps00:00 Stewart introduces Oliver Polzin from Meow Wolf's founding team and discusses how his yoga teaching there inspired the podcast's exploration of creativity and stress relationships.05:00 Oliver describes his architecture graduate program studying climate and conservation through synthetic landscapes, contrasting dark green naturalist ecology with bright green capitalist environmentalism.10:00 Discussion of conservation ethics and AI's potential for monitoring environmental systems, with Oliver explaining his journey from painting to experimental mud construction at early Meow Wolf.15:00 Stewart shares his robotics learning journey with ESP32s in Buenos Aires while Oliver questions humanoid robot design, suggesting functional form factors matter more than human resemblance.20:00 Oliver explores cardboard as material obsession and explains treasure hunt mechanics in Meow Wolf exhibits, creating dopamine-driven discovery experiences through layered storytelling.25:00 Stewart describes creating treasure hunts for Spanish learners in Buenos Aires parks while Oliver validates experiential art's growing importance in an increasingly digital culture.30:00 Conversation shifts to three-d printing flexible filaments for architectural models and Oliver's megafauna book project about La Brea Tar Pits Pleistocene fossils.35:00 Oliver connects Earth consciousness to Pale Blue Dot perspective, arguing humans face developmental threshold understanding planetary responsibility after 300,000 years as anatomically modern species.40:00 Deep dive into end-Pleistocene extinction events and megafauna loss, discussing two-ton capybaras and how predator relationships shaped human psychology and anxiety responses.45:00 Oliver presents speculative Great Plains biopreserve concept with de-extinct megafauna, contrasting holistic rotational grazing with destructive monoculture agriculture systems.50:00 Discussion concludes with Amazonian dark earth technology and indigenous landscape management, emphasizing need for biophilic culture embracing deep time ecological perspective.Key Insights1. Oliver Polzin is part of the founding team of Meow Wolf and is currently studying at SCI-Arc in Downtown LA in a postgraduate program called Synthetic Landscapes, which examines global scale climate and conservation issues through an architectural lens. Architecture exists between art and science, and he believes architectural thinking offers a valuable framework for designing interventions for climate and conservation challenges. This program represents a significant evolution from his earlier work at Meow Wolf, where he created immersive experiential art installations using materials like adobe and cardboard.2. There is an important distinction in ecological thought between what Paul Kingsnorth calls dark green and light green approaches to environmentalism. The dark green strain represents the older naturalist movement from the early twentieth century, focusing on biological systems, ecosystems, and endangered species. Light green emerged in the 1970s after the Earth Day movement and centers on clean energy, solar panels, and wind power as a way to maintain our current lifestyle. Oliver argues that the bright green approach represents a capitalist overlay that has captured the conservation movement, whereas true conservation requires focusing on actual biological systems rather than just technological solutions.3. The experiential art form that Meow Wolf pioneered still has enormous untapped potential, particularly as society becomes increasingly digital. Oliver believes there will be a huge wave of experiential desire in this decade as people crave human connection and real-world excitement. The treasure hunt and scavenger hunt format represents a compelling form of real-life RPG that creates meaningful human interactions. This type of experience design, which Meow Wolf developed through installations like the House of Eternal Return, plays with human dopamine systems by compelling people to open doors, explore spaces, and follow narrative threads through physical environments.4. The architectural model or dollhouse concept represents a crucial rhetorical tool that Oliver is learning to apply to climate and conservation work. Architects have long created physical models to show stakeholders what a building will be like, and this practice of showing a story in compelling ways for different types of brains is essential for getting traction on projects. While architectural models used to be made from foam core, paper, and balsa wood, they are now largely created through 3D printing, which allows for incredibly complex forms and interlocking structures that would have been impossible to construct manually.5. Oliver is obsessed with megafauna and the end Pleistocene extinction event that occurred roughly twelve thousand years ago. For three hundred thousand years, anatomically modern humans existed alongside massive beasts like short faced bears and American lions, and we were the smaller creatures in the ecosystem. The extinction of over one hundred genera of animals over ninety nine pounds, combined with sea level rise of nearly four hundred feet, fundamentally changed human existence and led to the development of agriculture and civilization. Much of our current psychological development, including anxiety responses, is still based on this time period when we lived among these massive animals.6. The current food system in the Great Plains is fundamentally broken compared to the historical managed food system maintained by Plains tribes, who sustained thirty to sixty million bison through 1800. Oliver explored a speculative project about turning the Great Plains into a massive biopreserve of de-extinct megafauna, contrasting the natural system of rotational grazing where predators keep herds moving with the current monoculture crop agriculture that requires external inputs like fertilizer, pesticides, and herbicides. The natural system builds soil and increases fecundity, while industrial agriculture degrades soil, creates toxic runoff, and produces genetically modified crops that feed animals in toxic concentrated feeding operations.7. The fundamental challenge facing humanity now is creating what Oliver calls a biophilic or ecophilic culture that is loving of other species and our home planet. This requires both psychological shifts and changes in how we design systems at all scales. The Amazon provides a powerful example of this, as recent LiDAR mapping has revealed that what appeared to be pristine wilderness was actually a vast tended garden created by indigenous civilizations who developed technologies like Amazonian dark earth through burning middens with various additives. These cultures understood how to be embedded in a web with other species while playing an important orchestrating role, offering a model for how humans might relate to other forms of life in our current era.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Larry Swanson, creator of the Knowledge Graph Insights Podcast, for their second conversation together. The two cover a wide range of interconnected topics, starting with a correction Larry makes about the true origin of the term "artificial intelligence," tracing it back to the 1956 Dartmouth Conference and its distinction from Norbert Wiener's cybernetics. From there, the conversation moves through the history and structure of knowledge graphs, ontologies, RDF (Resource Description Framework), and the W3C standards process, touching on concepts like the T-box, A-box, and C-box, as well as the 25th anniversary of the Semantic Web paper. Stewart and Larry also dig into the limitations of large language models — particularly around reasoning, confabulation, and what Larry describes as "cognitive surrender" — and why symbolic AI and knowledge engineering may hold answers that the neural network world hasn't fully embraced. The episode also ventures into consciousness, panpsychism, Michael Pollan's ideas, and Stewart's own hands-on experience vibe coding a personal chatbot to replace functionality he feels he's lost with recent changes to Claude. Larry's podcast can be found at kgi.fm.Timestamps00:00 - Stewart introduces Larry Swanson; Larry corrects the record on AI's origin, distinguishing it from Norbert Wiener's cybernetics at the 1956 Dartmouth conference.05:00 - Larry discusses interviewing semantic web paper coauthors on its 25th anniversary; RDF's hidden ubiquity compared to SIM cards powering everything invisibly.10:00 - Knowledge graphs explained through t-box terms, a-box assertions, and Dave McComb's c-box; IKEA's three-layer knowledge graph as a practical example.15:00 - Stewart connects metadata complexity to AI needs; faceted search explained as c-box attributes driving product filtering experiences.20:00 - RDF 1.2 reification standards discussed; W3C's rigorous recommendation process powering governments and enterprises worldwide through collaborative standards.25:00 - Cyc project examined as influential "successful failure"; Pat Hayes bringing description logic into semantic web; LLMs lacking true reasoning capability.30:00 - Epistemological fault lines between human and computer intelligence; cognitive surrender paper reveals no intelligence threshold protects against AI manipulation.35:00 - Stewart's Claude regression problem drives chatbot vibe coding quest; small language models and domain-specific approaches explored as alternatives.40:00 - Consciousness discussion through Michael Pollan's panpsychism lens; language versus cognition disconnect revealing LLMs as pure token-stitching without genuine thought.45:00 - Context graphs as purpose-built knowledge graphs for AI; Stewart's planning agents versus coding agents architecture and ground truth verification problem.50:00 - Docs-as-code versus code-as-docs paradigm shift; knowledge graphs as universal verifiers against validated facts; RDF 1.2 enabling provenance and degrees of certainty.55:00 - Jessica Talisman's Knowledge Graph Academy recommended for onboarding; kgi.fm podcast shared; knowledge representation community needs better abstraction for wider adoption.Key Insights1. The term "artificial intelligence" was not a marketing gimmick but was coined deliberately at the 1956 Dartmouth Conference to distinguish the work of John McCarthy from Norbert Wiener's cybernetics. The two camps represented genuinely different approaches, and the AI label was a form of intentional intellectual branding rather than empty promotion.2. The semantic web, often called the most successful failure in technology history, has quietly embedded itself everywhere despite never achieving its original vision. Technologies like RDF power metadata standards inside every Adobe product and form the invisible backbone of government systems, enterprise data infrastructure, and cultural heritage organizations worldwide.3. Knowledge graphs are best understood as an ontology combined with all the instances that populate it. The distinction between things and strings, popularized by Google in 2012, captures the core idea that knowledge representation is about concepts as distinct from the labels we give them.4. The t-box, a-box, and c-box framework offers a practical model for understanding knowledge architecture. The t-box holds terminology and concepts, the a-box holds assertions about specific instances, and the c-box manages the attributes, taxonomies, and controlled vocabularies that sit between them and enable things like faceted search.5. Large language models produce fluent, convincing output but lack genuine reasoning, epistemological grounding, or judgment. Research on cognitive surrender shows that even people who understand how LLMs work are still susceptible to being misled by their fluency, meaning intelligence and awareness offer no reliable protection against being deceived.6. The gap between language and cognition matters deeply when evaluating AI. Evidence from people with aphasia shows that thinking can occur without language, which suggests LLMs, being purely language-based systems, are missing a fundamental layer of cognition that cannot be recovered through more tokens or better training.7. Knowledge graphs and RDF-based representation are well suited to the problem of verification and grounding in AI systems. Rather than relying on vectorized embeddings of language, a knowledge graph can store validated, provenance-tracked facts with degrees of certainty, making it a natural foundation for building trustworthy AI applications.

In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Mauro Schilman, CTO and Co-founder of Tuki, the distribution standard for the AI agent era in travel, for a wide-ranging conversation that moves from the joys of international travel and the beauty of mathematics to the fast-evolving world of AI and large language models. Mauro shares his background as a math Olympiad competitor and later a coach, his time training coding models at the AI company Cohere, and his thoughts on how frontier models are progressing — or plateauing — at the foundational level while innovation accelerates at the application layer. The two also get into the mechanics of agentic AI, MCP and agent-to-agent protocols, hierarchical memory systems, red-green test-driven development as a powerful coding workflow, and the philosophical murkiness of open-source AI. They wrap up discussing Tuki Travel's mission to build AI-ready infrastructure for the travel industry, connecting hotels, suppliers, and online travel agencies to prepare for the coming wave of agentic commerce. You can learn more about Tuki Travel and reach out to the team at tukiclub.com.Timestamps00:00 - Stewart welcomes Mauro Schilman, CTO and Co-founder of Tuki Travel, who shares how traveling since age 15 through high school exchanges opened his mind to cultural similarities and differences.05:00 - Mauro explains Math Olympiad coaching culture and mentorship, noting LLMs now solve competition-level problems while Terence Tao explores AI assisting frontier unsolved mathematics.10:00 - Discussion turns to ChatGPT revealing Mauro's birthdate unprompted, exposing opaque application layers, preference tuning, and system prompts hidden within closed models.15:00 - Mauro argues true open source AI requires full training data, annotation protocols, and alignment processes, not just model weights, while scaling laws appear to be slowing.20:00 - Hierarchical memory models replace flat vector databases, using three-level retrieval systems improving context accuracy as knowledge management becomes AI's core challenge.25:00 - Mauro describes travel's fragmented infrastructure of aggregators, bed banks, and intermediaries, explaining Tuki builds agent-ready unification protocols for AI commerce.30:00 - MCP versus API debate clarifies natural language capability descriptions help agents consume services, while agent-to-agent communication embeds negotiating agents inside supplier systems.35:00 - Hallucinations and consumer trust block agentic payments, industries must build mistake-resilience into bookings before autonomous agent transactions become viable.40:00 - Mauro reveals red-green test-driven development methodology where agents write failing tests first then implementations, creating Oracle verification loops dramatically improving code quality.45:00 - Blockchain's potential for transparent distributed AI training discussed, distinguishing democratization from decentralization while stable coins and regulatory momentum build toward agentic commerce infrastructure.Key Insights1. Travel broadens perspective by revealing both universal human similarities and deep cultural differences. Mauro Schilman began traveling at fifteen through math olympiad competitions and found that people across the world share fundamental traits while also being shaped in profoundly different ways by their cultures. This tension between sameness and difference is what makes travel meaningful.2. Mathematics transitions from structured problem-solving in olympiads to genuine uncertainty in graduate school and research. Olympiad problems are carefully designed with elegant solutions meant to encourage creative thinking, but once a mathematician enters academia, the answers are unknown and the work becomes navigating that uncertainty.3. AI is now assisting mathematicians at the frontier, not just solving olympiad-level problems. Terence Tao, one of the greatest living mathematicians, has written publicly about how AI tools can help tackle unsolved problems, though the role of AI remains assistive rather than independent at the research level.4. Large language models are not truly transparent even when described as open source. Releasing model weights alone does not reveal the training data, annotation protocols, alignment tuning, or system prompts that shape model behavior. Real openness would require access to the entire pipeline.5. Memory and retrieval remain core unsolved challenges in AI systems. Researchers are moving from flat vector database approaches toward hierarchical memory structures with roughly three layers, which improves retrieval accuracy and reduces how much context gets consumed with each search.6. The travel industry is structurally unprepared for AI agents. A hidden web of bed banks, aggregators, and aggregators of aggregators sits between hotels and consumers, each taking a fee. Tuki Travel is building infrastructure to unify this distribution layer and make it consumable by AI agents through protocols like MCP and emerging agent-to-agent communication standards.7. Test-driven development using a red-green approach significantly improves AI-generated code quality. By asking the model to write failing tests before writing any implementation, developers create a verification oracle that guides the model toward correct solutions and avoids the bias of writing tests that simply confirm existing flawed code.

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