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.
A cell's metabolic state, meaning the chemical reactions happening within it, significantly influences its fate, including whether it divides, differentiates into a specialized cell type, or dies. Rather than simply fueling cellular processes, metabolism actively shapes cell behavior by altering gene expression and protein function. Specific metabolites, the intermediate products of metabolism, can directly modify proteins, impacting their activity and guiding cellular decisions. This understanding opens up possibilities for manipulating metabolism to control cell fate, offering potential therapeutic interventions for diseases like cancer.
HN commenters generally expressed fascination with the article's findings on how metabolism influences cell fate. Several highlighted the counterintuitive nature of the discovery, noting that it shifts the traditional understanding of DNA as the primary driver of cellular differentiation. Some discussed the implications for cancer research, regenerative medicine, and aging. One commenter pointed out the potential connection to the Warburg effect, where cancer cells favor glycolysis even in the presence of oxygen. Another questioned the generalizability of the findings, given the focus on yeast and mouse embryonic stem cells. A few expressed excitement about the future research directions this opens up, particularly regarding metabolic interventions for disease.
Researchers have identified a naturally occurring molecule called BAM15 that acts as a mitochondrial uncoupler, increasing fat metabolism without affecting appetite or body temperature. In preclinical studies, BAM15 effectively reduced body fat in obese mice without causing changes in food intake or activity levels, suggesting it could be a potential therapeutic for obesity and related metabolic disorders. Further research is needed to determine its safety and efficacy in humans.
HN commenters are generally skeptical of the article's claims. Several point out that the study was performed in mice, not humans, and that many promising results in mice fail to translate to human benefit. Others express concern about potential side effects, noting that tampering with metabolism is complex and can have unintended consequences. Some question the article's framing of "natural" boosting, highlighting that the molecule itself might not be readily available or safe to consume without further research. A few commenters discuss the potential for abuse as a performance-enhancing drug. Overall, the prevailing sentiment is one of cautious pessimism tempered by hope for further research and development.
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.