Crazy Wisdom Stewart Alsop

In this episode of the Crazy Wisdom Podcast, I, Stewart Alsop, am joined by Avetis Muradyan, Chief Technology Officer at Mobile Interactive, to discuss a range of topics from China's economic stability and supply chain innovations in Southeast Asia, to the technological advancements in Chinese manufacturing. We also touch upon the geopolitical dynamics of Indonesia, the impact of economic policies on innovation, and the vibrant entrepreneurial spirit of South America. Avetis shares insights from his extensive experience in Asia and reflects on the global economic landscape.
For more about Avetis's work, you can find him on LinkedIn and read his contributions on the Palladium author's page, or follow him on Twitter @AvetisMuradyan
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Timestamps
00:00 - Introduction and brief overview of Avetis Muradyan's recent visit to China, discussing societal and economic stability contrary to the "collapse narrative."
05:00 - Discussion about supply chain innovations in Southeast Asia, particularly the significant role of mom and pop shops in local economies.
10:00 - Debate on the position and potential of Indonesia within Asian geopolitics, reflecting on historical expectations versus current realities.
15:00 - Shift to technological advancements in China, focusing on the rapid development and improvement of Chinese manufacturing sectors, including automotive and truck design.
20:00 - Exploration of global shifts in manufacturing and deep tech innovation, comparing Western and Chinese approaches to technological development and industrial strategy.
25:00 - Reflections on economic policies, the impact of free capital during economic crises, and societal values influencing national and global economics.
30:00 - Personal anecdotes about experiences in China, comparing past and present manufacturing landscapes, and the broader implications for global economic shifts.
35:00 - Discussion on perceived conflicts between the U.S. and China, arguing against the idea of significant ideological or economic clashes between the two nations.
40:00 - Examination of South America's entrepreneurial spirit and personal freedom, discussing the cultural and economic vibrancy of Brazil and Argentina.
44:00 - Conclusion of the podcast with final thoughts on the abundance and potential of the Western Hemisphere, and information on how to connect with Avetis Muradyan for further discussions.
Key Insights
Economic Stability in China: Avetis Muradyan discusses the misconception of China's economic collapse, emphasizing the country's resilience and growth despite global narratives that suggest otherwise. He points out that, contrary to the collapse narratives, China has emerged as a significant global player, particularly highlighted by its ascent to become the world's largest car exporter.
Supply Chain Innovations in Southeast Asia: Muradyan sheds light on the critical role of small mom and pop shops in Southeast Asia’s economies. These establishments, he notes, are pivotal in the region's retail ecosystem, accounting for a significant portion of retail sales. He also discusses the rapid digital transformation within these small businesses and its impact on local economies.
Indonesia's Geopolitical Position: The discussion delves into Indonesia's potential and its complex internal dynamics, which include a significant military presence that does not align with its geographical identity as an archipelago. Muradyan explores the paradoxical elements of Indonesia’s development and its strategic geopolitical role between major powers like the US and China.
Technological Advancements in China: There is a significant focus on the evolution of Chinese manufacturing, where Muradyan highlights the shift from low-quality production to high-quality, innovative manufacturing processes, particularly in the automotive and tech sectors. This shift illustrates China's broader industrial strategy to climb up

In this episode of the Crazy Wisdom podcast, host Stewart Alsop sits down with Aaron Lowry, an experienced consultant and returning guest. They discuss a wide range of topics, including Lowry's work in rebuilding custom vehicles, the value of blending aesthetics with engineering, and the challenges of balancing principles and propositions in problem-solving. They also explore the evolving world of artificial intelligence, contrasting its limitations with human intelligence, and consider its impact on creative expression. Connect with Aaron on Twitter at @Aaron_Lowry for more insights into his projects and ideas.
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Timestamps
00:00 - Stewart Alsop introduces Aaron Lowry, discussing their previous conversations and current interests. They mention the makerspace and complexities in physical and software creation, while Lowry shares insights on sheet metal work and its principles.
00:05 - Stewart talks about challenges in crafting and how quick access to information on computers may impact patience. He appreciates Lowry's language of attunement and asks for Lowry's views on AI, given that he hasn't been directly involved in building it.
00:10 - Lowry discusses intelligence, consciousness, and the reciprocal relationship between agent and environment. He explores challenges in defining intelligence, noting the mirror-like effect of AI reflecting our own limitations.
00:15 - Stewart discusses how filtering AI models reduces their utility. Lowry describes prompt injection as a way to navigate AI limitations while emphasizing the importance of understanding the parameters that bound the data set.
00:20 - Lowry acknowledges the energy required to maintain AI models, comparing it to the efficiency of the human brain. He stresses the probabilistic nature of human intelligence versus the deterministic nature of machine learning.
00:25 - Lowry distinguishes between the infinite potential of probabilistic intelligence and deterministic frameworks. He compares real-world interaction to a video game, noting how deterministic thinking can make people behave like NPCs.
00:30 - They discuss navigating principles versus propositions, likening it to piloting a sailboat. Maintaining direction requires continuous feedback and adaptation.
00:35 - Stewart differentiates between propositional and participatory knowing, noting AI's strong grasp of the former. Lowry argues that perspective is assigned in AI models but participation remains absent.
00:40 - Lowry describes the truck he is restoring, noting the blend of modern engineering and aesthetic choices. He shares his process of acquiring knowledge from books and the internet.
00:45 - They discuss Brian Rommel's approach to training language models with high-quality data from the past, emphasizing the importance of data quality.
00:50 - They discuss how AI models can synthesize a broader spectrum of perspectives than any individual. Lowry advocates for plurality in models, warning against a single authoritative perspective.
00:55 - They delve into AI's impact on art. Despite the democratization of creative tools, Lowry asserts that authentic artistic inspiration is still necessary. He highlights the empty appeal of AI-generated perfection lacking the soul of human art.
Key insights
Principles vs. Propositions in Problem-Solving: Aaron Lowry emphasizes the importance of working with first principles rather than rigid propositions. He compares this to piloting a sailboat, where adaptability and constant course correction are crucial, and stresses that a principle-based approach allows for dynamic navigation of complex challenges.
Sheet Metal Work as a Metaphor: Lowry draws parallels between his experience working with sheet metal and broader life lessons. He finds that patience, precision, and an understanding of thermodynamics are essential when shaping materials and that these skills have broader applications, like aligning wit

Welcome to the Crazy Wisdom podcast, where I, Stewart Alsop, had the pleasure of hosting Bob Upham. In this episode, we explore a variety of intriguing topics, including the roots of personal computing intertwined with the 1960s consciousness revolution, Bob’s fascinating journey from music to mastering programming, and his profound insights on the evolution of software development. We also touched on his experiences at major tech hubs and his stint at companies like IBM and Yahoo, diving into the intricacies of business development within tech. Bob shared his perspectives on the ongoing transformation of technology, emphasizing the significant shift towards more accessible programming tools and the implications of AI in software development. For those interested in connecting with Bob or attending his startup workshops, check out his LinkedIn profile 
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Timestamps
00:00 - Introduction to Bob Upham and discussion on the early personal computing industry influenced by the consciousness revolution of the 1960s.
05:00 - Bob discusses his transition from music to programming, blending artistic creativity with the logical structure of software development.
10:00 - Bob recounts his early career experiences in New York during the late 70s, transitioning from mainframes to PCs, and the significance of geographical tech hubs.
15:00 - Exploration of the evolution of tech companies in the 80s, with a focus on the impact of IBM and other major corporations in shaping the technology landscape.
20:00 - Bob reflects on the bureaucratic and hierarchical nature of working at IBM, and the creativity involved in programming.
25:00 - Discussion on the business of software, the complexities of navigating corporate structures, and how the landscape of technology employment has changed with the advent of personal computing.
30:00 - Bob talks about his role at Yahoo, his work with behavioral and geo-targeting, and the early days of internet mapping services.
35:00 - The conversation shifts to the changes in software development over the years, from data entry and the manual processes of early computing to the more creative and efficient methods available today.
40:00 - Bob discusses the current state of technology, the ubiquity of programming skills, and the democratization of software development through accessible tools.
45:00 - The episode wraps up with a look at the future of technology, including AI and its potential impacts on creativity and efficiency in software development.
Key Insights
The Cultural Roots of Computing: Bob Upham discussed how the early personal computing industry was significantly influenced by the consciousness revolution of the 1960s. This era brought about a fusion of technology with creative and social movements, illustrating how cultural shifts can propel technological advancements.
Music Meets Machine: Upham's personal journey from a musician to a programmer highlighted an intriguing crossover between the arts and technology. He shared how the structured creativity of music provided a solid foundation for software engineering, underscoring the interconnectedness of logical and creative disciplines.
Tech Hub Evolution: The episode touched on the geographical shifts in the technology sector from New York and Boston to Silicon Valley. This transition marked a significant shift in the epicenters of innovation, driven by changes in technological focus and corporate culture.
Corporate Culture and Bureaucracy: Bob reminisced about his time at IBM, describing it as a period dominated by bureaucracy and hierarchical structures. This insight into corporate culture provides a contrasting backdrop to the more flexible and dynamic environment prevalent in tech companies today.
Impact of Personal Computing: The conversation explored the transformational impact of the personal computer, moving from the domain of mainframes and centralized systems to more a

In this episode of the Crazy Wisdom podcast, I, Stewart Alsop, sit down with Fabian Schonholz, a seasoned technology and operations executive, to explore the intriguing world of synthetic data. We discuss its pivotal role in training AI models, particularly large language models (LLMs), and delve into the nuances of data behavior, the challenges of ensuring realism without real-world ties, and the potential of synthetic data to mitigate biases in AI training. For those interested in learning more about Fabian or reaching out for consultations, visit his LinkedIn profile linked here or check out his consulting services at FESSEXconsulting.com.
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Timestamps
05:00 - Challenges of modeling nuanced behaviors in synthetic data and its implications for AI model training. 10:00 - Applications of synthetic data in different types of models (e.g., churn models, conversion models) before the emergence of LLMs. 15:00 - The role of synthetic data in accelerating AI model production and enhancing data density. 20:00 - Discussion on the influence of nuanced behaviors on AI models, specifically within the context of LLMs and their ability to capture the subtleties of human language. 25:00 - Exploration of the improvement in model performance when retrained with real data after initial training with synthetic data. 30:00 - Considerations on bias in model training, the impact of synthetic data on reducing bias, and the broader implications for AI accuracy and fairness. 35:00 - The process of creating synthetic data, including the use of data from real-world scenarios as a base for generating synthetic datasets. 40:00 - The utility of synthetic data in operational contexts, specifically in AI model training, and the feedback loops involved in improving these models over time. 45:00 - Final thoughts on the potential risks and philosophical aspects of synthetic data usage, particularly in relation to its impact on the quality of AI models and the ethical considerations involved. Key Insights
Definition and Importance of Synthetic Data: Fabian Schonholz defines synthetic data as data that mimics real-world data but has no direct link to it, ensuring privacy and confidentiality. This type of data is crucial for training AI models where real data can be sensitive or scarce.
Challenges of Synthetic Data: Despite its benefits, synthetic data comes with challenges, particularly in accurately replicating the nuanced behaviors of real data. This can affect the realism and effectiveness of AI models trained with synthetic data, especially in complex applications.
Applications Before LLMs: Synthetic data has been utilized in various models such as churn models, conversion models, and predictive lifetime value models. These applications demonstrate the versatility and impact of synthetic data across different domains prior to the emergence of large language models.
Impact on AI Model Training: Synthetic data accelerates the production of AI models by providing a robust way to simulate real-world data. This can significantly reduce the time and resources needed to bring AI technologies to production, especially in early stages of development.
Mitigating Bias in AI: One of the profound benefits of synthetic data is its potential to reduce bias in AI training. By carefully crafting datasets, developers can ensure a more balanced representation that avoids perpetuating existing biases found in real-world data.
Nuanced Behaviors and AI Accuracy: The conversation highlights the importance of nuanced behaviors in data, which synthetic data might overlook. Capturing these subtle aspects is critical for the accuracy and functionality of AI models, particularly in fields like natural language processing and predictive analytics.
Future of Synthetic Data in AI: Looking forward, the integration of synthetic data in AI development holds promise for more ethical, efficient, and effective model

Welcome to the Crazy Wisdom podcast. I'm Stewart Alsop, and in this episode, I'm joined by Stephen Wolfram, a distinguished scientist and the founder of Wolfram Research. Throughout our conversation, we explore a range of topics including Stephen's revitalized work on a fundamental theory of physics, the profound implications of this work for understanding the universe and its laws, and the intersection of these theories with computational technology and AI. We also discuss how these advanced concepts might shape future technological developments and philosophical inquiries into AI's role in society and personal identity.
Timestamps
[00:00:00] - Stephen Wolfram discusses revisiting his physics theory from the 1990s and the rapid advancements made since 2019. [00:05:00] - Explores implications of his physics project on AI, machine learning, and philosophical questions about the universe's existence. [00:10:00] - Describes the universe's construction from a network of discrete points, explaining emergent phenomena like black holes. [00:15:00] - Discusses the interface between the discrete network model of the universe and concepts of quantum mechanics. [00:20:00] - Shifts focus to computational theory, introducing the "Ruliad" that encompasses all possible computational rules. [00:25:00] - Talks about AI's generative capabilities and its evolving relationship with human cognition and creativity. [00:30:00] - Discusses practical applications of his theoretical work in technology development and complex systems understanding. [00:35:00] - Considers the societal and ethical implications of AI, and the concept of co-evolving with AI in a symbiotic relationship. [00:40:00] - Explores philosophical impacts of AI on human identity, decision-making, and future societal norms. [00:45:00] - Reflects on personal impacts of his work, the continuity of identity through AI, and digital legacies. [00:50:00] - Concludes with thoughts on AI's potential role in human exploration and understanding of the universe. [00:55:00] - Discusses computational thinking and the integration of computational methods in modern problem-solving. Key Insights
Revival of Fundamental Physics Theories: Stephen Wolfram discussed reigniting his work on the fundamental theory of physics, an endeavor he first conceptualized in the early 1990s. He returned to these theories with fresh perspectives and the help of young physicists, leading to rapid advancements that surpassed his initial expectations, demonstrating that foundational theories in physics can evolve significantly with the incorporation of new data and technologies.
Interconnection Between Physics and Computation: Wolfram elucidated how his latest work in physics unexpectedly bears significant implications for fields like AI, machine learning, and distributed computing. The formalism developed for explaining the universe has versatile applications, suggesting a profound connectivity between physical laws and computational processes.
Philosophical Questions on Existence and Regularity: The conversation delved into deep philosophical questions such as why the universe exists and whether the laws of physics are inevitable. Wolfram’s approach suggests that these existential queries are not only theoretical but can be addressed in a scientific framework, proposing that our observations of the universe are deeply influenced by human perspectives and limitations.
Discrete Network Model of the Universe: Wolfram proposes that the universe is constructed from a network of discrete points, which he likens to atoms of space. This model suggests that all phenomena in the universe, including particles and black holes, emerge from this underlying network, offering a novel way of understanding the fabric of space and time beyond the continuous models traditionally used in physics.
Concept of the Ruliad: A significant portion of the discussion focused on the "Ruliad," a conceptual framework that encompa

In this episode of the Crazy Wisdom podcast, I, Stewart Alsop, had the pleasure of hosting Albert Chun, Director of Operations at Invisible Technologies. We explored the latest advancements in large language models, particularly focusing on their implications for content creation, coding, and video generation. Albert provided insights into the evolution of software engineering towards prompt engineering, the philosophical debates surrounding AI and humanity, and the impact of AI on creativity and discipline. We also discussed the role of AI in education across different socioeconomic contexts and pondered over the future of AI in decentralized systems. Albert shared some valuable resources and thoughts, including those from his experiences in educational settings. For those looking to connect with Albert or explore more about his work, you can find him on LinkedIn at Albert S. Chun's LinkedIn.

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Timestamps
[00:00:00] Introduction to the podcast with Stewart Alsop and Albert Chun. Discussion on the remarkable capabilities and implications of a recent LLM update named SORA, its impact on content generation, and the significant human effort required behind improving LLMs.
[00:05:00] Focus shifts to the evolving role of software engineering towards prompt engineering and the increasing complexity of tasks that LLMs can handle.
[00:10:00] Albert reflects on the philosophical aspects of interacting with AI, touching on human endeavor, the essence of being human, and the potential for AI to reshape personal reprogramming.
[00:15:00] Conversation moves to the impact of AI on creativity and the challenges of maintaining discipline amidst increasingly accessible creative tools.
[00:20:00] Discussion on the social implications of AI in different socio-economic settings, especially concerning technology use in education.
[00:25:00] Albert talks about his experiences teaching in diverse educational settings and using technology to enhance learning outcomes in lower-income neighborhoods.
[00:30:00] The podcast explores potential changes in education policy and technology, including the benefits and limitations of current educational frameworks in adapting to technological advancements.
[00:35:00] Focus on financial literacy in education, exploring the slow adoption of practical skills teaching in school curriculums and the broader societal implications.
[00:40:00] The conversation wraps up with thoughts on the future impact of decentralized systems and technologies on societal structures and norms.
Key Insights
Advancements in LLMs: Albert Chun highlighted recent advancements in Large Language Models (LLMs) such as SORA, discussing its capabilities in content generation and the significant human labor involved in refining these models. This reflects a broader trend in AI development, where human input remains crucial despite increasing automation.
Role of Software Engineering: The conversation shifted to how the role of software engineers is evolving towards prompt engineers and annotators. As AI grows more complex, the skills required to train and manage these models are becoming more specialized, suggesting a paradigm shift in the tech industry's labor needs.
Philosophical Implications of AI: Albert and Stewart delved into the philosophical implications of AI, pondering what it means to be human in an age increasingly dominated by artificial intelligence. They discussed how AI challenges our conventional views on creativity, knowledge, and the essence of human endeavor.
AI and Creativity: The discussion explored how AI might impact creativity, particularly through tools that simplify the creative process. While AI can democratize creativity by making tools more accessible, there is concern about whether this ease of use might undermine the discipline required for profound creative work.
Educational Disparities and AI: Albert shared insights from