Holden Karnofsky examines the question of whether advanced AI will pose an existential threat. He argues that while it's difficult to be certain, the evidence suggests a substantial likelihood of catastrophe. This risk stems from the potential for AI systems to dramatically outperform humans in many domains, combined with misaligned goals or values, leading to unintended and harmful consequences. Karnofsky highlights the rapid pace of AI development, the difficulty of aligning complex systems, and the historical precedent of powerful technologies causing unforeseen disruptions as key factors contributing to the risk. He emphasizes the need for serious consideration and proactive mitigation efforts, arguing that the potential consequences are too significant to ignore.
This paper explores the implications of closed timelike curves (CTCs) for the existence of life. It argues against the common assumption that CTCs would prevent life, instead proposing that stable and complex life could exist within them. The authors demonstrate, using a simple model based on Conway's Game of Life, how self-consistent, non-trivial evolution can occur on a spacetime containing CTCs. They suggest that the apparent paradoxes associated with time travel, such as the grandfather paradox, are avoided not by preventing changes to the past, but by the universe's dynamics naturally converging to self-consistent states. This implies that observers on a CTC would not perceive anything unusual, and their experience of causality would remain intact, despite the closed timelike nature of their spacetime.
HN commenters discuss the implications and paradoxes of closed timelike curves (CTCs), referencing Deutsch's approach to resolving the grandfather paradox through quantum mechanics and many-worlds interpretations. Some express skepticism about the practicality of CTCs due to the immense energy requirements, while others debate the philosophical implications of free will and determinism in a universe with time travel. The connection between CTCs and computational complexity is also raised, with the possibility that CTCs could enable the efficient solution of NP-complete problems. Several commenters question the validity of the paper's approach, particularly its reliance on density matrices and the interpretation of results. A few more technically inclined comments delve into the specifics of the physics involved, mentioning the Cauchy problem and the nature of time itself. Finally, some commenters simply find the idea of time travel fascinating, regardless of the theoretical complexities.
Summary of Comments ( 57 )
https://news.ycombinator.com/item?id=43045406
Hacker News users generally praised the article for its thoroughness and nuanced approach to causal inference. Several commenters highlighted the importance of considering confounding variables and the limitations of observational studies, echoing points made in the article. One compelling comment suggested the piece would be particularly valuable for those working in fields where causal claims are frequently made without sufficient evidence, such as nutrition and social sciences. Another insightful comment discussed the practical challenges of applying Hill's criteria for causality, noting that even with strong evidence, definitively proving causation can be difficult. Some users pointed out the article's length, while others appreciated the depth and detailed examples. A few commenters also shared related resources and tools for causal inference.
The Hacker News post titled "Does X cause Y? An in-depth evidence review" has generated a moderate amount of discussion, with a focus on the methodology presented in the article and its broader applications.
Several commenters appreciate the structured approach to analyzing causality. One user praises the article's breakdown of different levels of evidence, highlighting the distinction between merely observing a correlation and establishing a causal link. They find the emphasis on mechanisms, confounders, and experimental data particularly valuable. Another commenter echoes this sentiment, stating that the article provides a practical framework for evaluating claims, which they believe is applicable beyond academic research and useful in everyday life.
Some discussion revolves around the practicality and limitations of the proposed framework. One commenter questions the feasibility of rigorously applying this level of analysis to every decision, suggesting it might be overly demanding for everyday situations. They propose that a simpler heuristic might be sufficient in many cases. Another user points out the inherent subjectivity in weighting different types of evidence, arguing that the framework's effectiveness depends on the user's judgment and potential biases.
A few commenters offer additional resources and perspectives. One user suggests exploring the Bradford Hill criteria, a set of guidelines used in epidemiology to establish causal relationships. Another mentions the book "Good Strategy/Bad Strategy," highlighting its insights into diagnosing cause and effect in complex situations.
A couple of comments touch upon the philosophical implications of causality. One commenter reflects on the inherent difficulty of proving causality definitively, suggesting that the best we can often achieve is a high degree of confidence.
Overall, the comments on the Hacker News post demonstrate a general appreciation for the article's structured approach to causal analysis, while also acknowledging the practical limitations and inherent complexities involved in establishing causality. The discussion extends beyond the specific examples in the article, exploring broader applications and related concepts in various fields.