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.
"Anatomy of Oscillation" explores the ubiquitous nature of oscillations in various systems, from physics and engineering to biology and economics. The post argues that these seemingly disparate phenomena share a common underlying structure: a feedback loop where a system's output influences its own input, leading to cyclical behavior. It uses the example of a simple harmonic oscillator (a mass on a spring) to illustrate the core principles of oscillation, including the concepts of equilibrium, displacement, restoring force, and inertia. The author suggests that understanding these basic principles can help us better understand and predict oscillations in more complex systems, ultimately offering a framework for recognizing recurring patterns in seemingly chaotic processes.
Hacker News users discussed the idea of "oscillation" presented in the linked Substack article, primarily focusing on its application in various fields. Some commenters questioned the novelty of the concept, arguing that it simply describes well-known feedback loops. Others found the framing helpful, highlighting its relevance to software development processes, personal productivity, and even biological systems. A few users expressed skepticism about the practical value of the framework, while others offered specific examples of oscillation in their own work, such as product development cycles and the balance between exploration and exploitation in learning. The discussion also touched upon the optimal frequency of oscillations and the importance of recognizing and managing them for improved outcomes.
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.