The post "Limits of Smart: Molecules and Chaos" argues that relying solely on "smart" systems, particularly AI, for complex problem-solving has inherent limitations. It uses the analogy of protein folding to illustrate how brute-force computational approaches, even with advanced algorithms, struggle with the sheer combinatorial explosion of possibilities in systems governed by physical laws. While AI excels at specific tasks within defined boundaries, it falters when faced with the chaotic, unpredictable nature of reality at the molecular level. The post suggests that a more effective approach involves embracing the inherent randomness and exploring "dumb" methods, like directed evolution in biology, which leverage natural processes to navigate complex landscapes and discover solutions that purely computational methods might miss.
The blog post explores the potential of applying "quantitative mereology," the study of parts and wholes with numerical measures, to complex systems. It argues that traditional physics, focusing on fundamental particles and forces, struggles to capture the emergent properties of complex systems. Instead, a mereological approach could offer a complementary perspective by quantifying relationships between parts and wholes across different scales, providing insights into how these systems function and evolve. This involves defining measures of "wholeness" based on concepts like integration, differentiation, and organization, potentially leading to new mathematical tools and models for understanding emergent phenomena in areas like biology, economics, and social systems. The author uses the example of entropy to illustrate how a mereological view might reinterpret existing physical concepts, suggesting entropy as a measure of the distribution of energy across a system's parts rather than purely as disorder.
HN users discussed the practicality and philosophical implications of applying mereology (the study of parts and wholes) to complex systems. Some expressed skepticism about quantifying mereology, questioning the usefulness of assigning numerical values to part-whole relationships, especially in fields like biology. Others were more receptive, suggesting potential applications in areas like network analysis and systems engineering. The debate touched on the inherent complexity of defining "parts" and "wholes" in different contexts, and whether a purely reductionist approach using mereology could capture emergent properties. Some commenters also drew parallels to other frameworks like category theory and information theory as potentially more suitable tools for understanding complex systems. Finally, there was discussion of the challenge of reconciling discrete, measurable components with the continuous nature of many real-world phenomena.
Summary of Comments ( 2 )
https://news.ycombinator.com/item?id=43495476
HN commenters largely agree with the premise of the article, pointing out that intelligence and planning often fail in complex, chaotic systems like biology and markets. Some argue that "smart" interventions can exacerbate problems by creating unintended consequences and disrupting natural feedback loops. Several commenters suggest that focusing on robustness and resilience, rather than optimization for a specific outcome, is a more effective approach in such systems. Others discuss the importance of understanding limitations and accepting that some degree of chaos is inevitable. The idea of "tinkering" and iterative experimentation, rather than grand plans, is also presented as a more realistic and adaptable strategy. A few comments offer specific examples of where "smart" interventions have failed, like the use of pesticides leading to resistant insects or financial engineering contributing to market instability.
The Hacker News post "Limits of Smart: Molecules and Chaos" discussing the Dynomight Substack article of the same name sparked a moderately active discussion with 17 comments. Several commenters engaged with the core ideas presented in the article, focusing on the inherent unpredictability of complex systems and the limitations of reductionist approaches.
One compelling thread explored the implications for large language models (LLMs). A commenter argued that LLMs, while impressive, are ultimately statistical machines limited by their training data and incapable of true understanding or generalization beyond that data. This limitation, they argued, ties back to the article's point about the inherent chaos and complexity of the world. Another commenter built upon this idea, suggesting that LLMs may be effective within specific niches but struggle with broader, more nuanced contexts where unforeseen variables and emergent behaviors can dominate.
Another commenter focused on the practical implications of the article's thesis for fields like medicine and engineering. They highlighted the challenges of predicting outcomes in complex biological systems and the limitations of current modeling techniques. They posited that a more holistic, systems-based approach might be necessary to overcome these challenges.
Several commenters offered personal anecdotes or examples to illustrate the article's points. One shared an experience from the semiconductor industry, highlighting the unexpected and often counterintuitive behavior of materials at the nanoscale. Another discussed the limitations of weather forecasting, drawing a parallel to the article's discussion of chaos and unpredictability.
Some commenters offered critiques or alternative perspectives. One commenter questioned the article's framing of "smart" and suggested that the real issue lies in our limited understanding of complex systems rather than any inherent limitation of intelligence. Another commenter pushed back against the idea that reductionism is inherently flawed, arguing that it remains a valuable tool for scientific inquiry, even in the face of complex phenomena.
A few comments offered tangential observations or links to related resources. One commenter shared a link to a paper discussing the concept of "emergence" in complex systems. Another commented on the writing style of the original article, praising its clarity and accessibility.
Overall, the comments on Hacker News reflect a thoughtful engagement with the ideas presented in the "Limits of Smart" article. The discussion covered a range of topics, from the implications for artificial intelligence to the challenges of predicting outcomes in complex systems. While there wasn't a single dominant narrative, the comments collectively explored the inherent limitations of reductionist approaches and the need for more nuanced understanding of complex phenomena.