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 new mathematical framework called "next-level chaos" moves beyond traditional chaos theory by incorporating the inherent uncertainty in our knowledge of a system's initial conditions. Traditional chaos focuses on how small initial uncertainties amplify over time, making long-term predictions impossible. Next-level chaos acknowledges that perfectly measuring initial conditions is fundamentally impossible and quantifies how this intrinsic uncertainty, even at minuscule levels, also contributes to unpredictable outcomes. This new approach provides a more realistic and rigorous way to assess the true limits of predictability in complex systems like weather patterns or financial markets, acknowledging the unavoidable limitations imposed by quantum mechanics and measurement precision.
Hacker News users discuss the implications of the Quanta article on "next-level" chaos. Several commenters express fascination with the concept of "intrinsic unpredictability" even within deterministic systems. Some highlight the difficulty of distinguishing true chaos from complex but ultimately predictable behavior, particularly in systems with limited observational data. The computational challenges of accurately modeling chaotic systems are also noted, along with the philosophical implications for free will and determinism. A few users mention practical applications, like weather forecasting, where improved understanding of chaos could lead to better predictive models, despite the inherent limits. One compelling comment points out the connection between this research and the limits of computability, suggesting the fundamental unknowability of certain systems' future states might be tied to Turing's halting problem.
New research is mapping the chaotic interior of charged black holes, revealing a surprisingly complex structure. Using sophisticated computational techniques, physicists are exploring the turbulent dynamics within, driven by the black hole's electric charge. This inner turmoil generates an infinite number of nested, distorted "horizons," each with its own singularity, creating a fractal-like structure. These findings challenge existing assumptions about black hole interiors and provide new theoretical tools to probe the fundamental nature of spacetime within these extreme environments.
Several commenters on Hacker News expressed excitement about the advancements in understanding black hole interiors, with some highlighting the counterintuitive nature of maximal entropy being linked to chaos. One commenter questioned the visual representation's accuracy, pointing out the difficulty of depicting a 4D spacetime. There was discussion about the computational challenges involved in such simulations and the limitations of current models. A few users also delved into the theoretical physics behind the research, touching upon topics like string theory and the holographic principle. Some comments offered additional resources, including links to relevant papers and talks. Overall, the comments reflected a mix of awe, curiosity, and healthy skepticism about the complexities of black hole physics.
Classical physics is generally considered deterministic, meaning the future state of a system is entirely determined by its present state. However, certain situations appear non-deterministic due to our practical limitations. These include chaotic systems, where tiny uncertainties in initial conditions are amplified exponentially, making long-term predictions impossible, despite the underlying deterministic nature. Other examples involve systems with a vast number of particles, like gases, where tracking individual particles is infeasible, leading to statistical descriptions and probabilistic predictions, even though the individual particle interactions are deterministic. Finally, systems involving measurement with intrinsic limitations also exhibit apparent non-determinism, arising from our inability to perfectly measure the initial state. Therefore, non-determinism in classical physics is often a result of incomplete knowledge or practical limitations rather than a fundamental property of the theory itself.
Hacker News users discuss deterministic chaos and how seemingly simple classical systems can exhibit unpredictable behavior due to sensitivity to initial conditions. They mention examples like the double pendulum, dripping faucets, and billiard balls, highlighting how minute changes in starting conditions lead to vastly different outcomes, making long-term prediction impossible. Some argue that while these systems are technically deterministic, the practical limitations of measurement render them effectively non-deterministic. Others point to the three-body problem and the chaotic nature of weather systems as further illustrations. The role of computational limitations in predicting chaotic systems is also discussed, along with the idea that even if the underlying laws are deterministic, emergent complexity can make systems appear unpredictable. Finally, the philosophical implications of determinism are touched upon, with some suggesting that quantum mechanics introduces true randomness into the universe.
Terence Tao argues against overly simplistic solutions to complex societal problems, using the analogy of a chaotic system. He points out that in such systems, small initial changes can lead to vastly different outcomes, making prediction difficult. Therefore, approaches focusing on a single "root cause" or a "one size fits all" solution are likely to be ineffective. Instead, he advocates for a more nuanced, adaptive approach, acknowledging the inherent complexity and embracing diverse, localized solutions that can be adjusted as the situation evolves. He suggests that relying on rigid, centralized planning is often counterproductive, preferring a more decentralized, experimental approach where local actors can respond to specific circumstances.
Hacker News users discussed Terence Tao's exploration of using complex numbers to simplify differential equations, particularly focusing on the example of a forced damped harmonic oscillator. Several commenters appreciated the elegance and power of using complex exponentials to represent oscillations, highlighting how this approach simplifies calculations and provides a more intuitive understanding of phase shifts and resonance. Some pointed out the broader applicability of complex numbers in physics and engineering, mentioning uses in electrical circuits, quantum mechanics, and signal processing. A few users discussed the pedagogical implications, suggesting that introducing complex numbers earlier in physics education could be beneficial. The thread also touched upon the abstract nature of complex numbers and the initial difficulty some students face in grasping their utility.
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.