Navigating Probabilistic Realities: Principles, Sheet Metal, and AI Reflections with Aaron Lowry

Crazy Wisdom

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

1. 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.

2. 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 with fundamental principles in all aspects of life.

3. AI and Human Intelligence Contrasts: Despite not being directly involved in building AI, Lowry offers a thoughtful analysis of its relationship to human intelligence. He argues that AI can mirror our limitations and reflects how intelligence is both probabilistic and deterministic, giving us powerful tools but also raising ethical and practical challenges.

4. Guardrails and Filtering in AI Models: The conversation explores how filtering in AI reduces its utility. While Lowry acknowledges that filters are essential for contextualizing data sets, he also notes that prompt injection helps circumvent these limitations, revealing the inherent challenges in fully controlling AI output.

5. Plurality of Perspectives in AI: Both Alsop and Lowry agree that multiple AI models are necessary to capture a range of perspectives, and relying on a single authoritative model could be dangerous. They highlight that AI models should maintain diversity to better reflect the broad spectrum of human experience.

6. AI's Role in Creative Expression: They touch upon the potential of AI to create art, noting how it can democratize creative tools. However, Lowry points out that even with high technical proficiency, AI-generated art often lacks the emotional resonance that comes from genuine human inspiration and participation.

7. Blending Aesthetics with Engineering: Lowry shares his approach to rebuilding classic vehicles, which blends modern engineering with aesthetic considerations. His goal is to maintain the beauty of the original designs while ensuring functionality, illustrating the delicate balance between creativity and technical precision.