Scott explores the connection between the Dark Room Problem in neuroscience, meditative jhanas, and theories of beauty, suggesting that intense focus on regular stimuli can produce extreme bliss. Longer summary

Scott Alexander discusses the Dark Room Problem in neuroscience and its connection to meditative states known as jhanas. He explores the idea that sitting quietly in a dark room, or focusing intently on a single stimulus, can lead to extreme bliss. This perspective, shared by Andrés Gómez Emilsson, suggests that regularity, predictability, and symmetry in stimuli can produce intense pleasure when one can concentrate deeply enough. The post compares this to theories of beauty and discusses how it might explain the appeal of music and other forms of art. Shorter summary

Scott Alexander attempts to understand and explain Karl Friston's complex 'free energy' principle in neuroscience, exploring its various interpretations and potential implications. Longer summary

Scott Alexander explores Karl Friston's work on 'free energy', a complex concept in neuroscience that has been described as a unified brain theory. The post delves into various interpretations of free energy, from its mathematical origins in Bayesian equations to its application in explaining perception, cognition, homeostasis, and action. Scott struggles to fully grasp the concept, acknowledging its complexity and the widespread difficulty in understanding Friston's work. He presents different perspectives on free energy, including its role in uncertainty reduction, its connection to predictive processing, and its potential to explain biological systems and the origin of life. The post concludes with a tentative synthesis of these ideas and raises questions about the implications of the free energy principle for machine ethics. Shorter summary

Scott Alexander provides evidence that many prominent AI researchers are concerned about AI risk, contrary to claims in some popular articles. Longer summary

Scott Alexander responds to articles claiming that AI researchers are not concerned about AI risk by providing a list of prominent AI researchers who have expressed concerns about the potential risks of advanced AI. He argues that there isn't a clear divide between 'skeptics' and 'believers', but rather a general consensus that some preliminary work on AI safety is needed. The post highlights that the main differences lie in the timeline for AI development and when preparations should begin, not whether the risks are real. Shorter summary