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 Substack post, "Limits of Smart: Molecules and Chaos," by Dynomight, delves into the inherent limitations of predictive modeling, particularly within the realm of complex systems characterized by numerous interacting components. The author meticulously constructs an argument against the naive application of "smart" technologies and overly optimistic expectations of predictive capabilities, focusing on the fundamental divide between macroscopic, statistically predictable behaviors and the underlying microscopic world governed by chaotic dynamics.
The central thesis revolves around the contrast between the seemingly predictable behavior of bulk materials and the chaotic motion of individual molecules within those materials. While we can confidently predict the overall temperature or pressure of a gas, for instance, the trajectory of any single molecule within that gas is highly sensitive to initial conditions and thus practically unpredictable. This principle is extrapolated to more complex systems, arguing that even with immensely powerful computational resources, the butterfly effect renders long-term, precise predictions impossible due to the accumulation of minute errors and unforeseen perturbations.
The author uses the analogy of a pool table to illustrate this point. While predicting the general dispersal of billiard balls after a break might be feasible, precisely predicting the final resting position of each ball is an exercise in futility due to the subtle nuances of each collision and the imperceptible imperfections of the table surface. This analogy underscores the inherent limits of prediction in systems dominated by chaotic interactions.
Furthermore, the post elucidates the computational intractability of simulating even relatively simple systems at a molecular level. The sheer number of particles and interactions involved quickly overwhelms even the most powerful computers, making exhaustive calculations impossible. The author highlights that while statistical mechanics can provide valuable insights into macroscopic properties, it doesn't offer a pathway to circumvent the fundamental limitations imposed by chaotic dynamics at the microscopic scale.
The author also touches upon the implications for fields like artificial intelligence and machine learning. While these technologies have demonstrated remarkable capabilities in certain domains, the post cautions against overestimating their potential for predicting complex systems. The inherent limitations of computation and the pervasive nature of chaos pose significant challenges to achieving perfect predictability in the real world, especially when dealing with intricate and dynamically evolving systems. In essence, the post advocates for a more nuanced understanding of the capabilities and limitations of predictive models, recognizing that “smart” technologies are not a panacea for the inherent uncertainties of complex systems. It calls for a tempered optimism grounded in the fundamental principles of physics and computation.
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.