The post examines the relationship between neuron count and intelligence across species, challenging traditional brain size measures and exploring implications for AI development. Longer summary

This post discusses the relationship between brain size, neuron count, and intelligence across different species. It challenges traditional measures like absolute brain size and encephalization quotient, focusing instead on the number of cortical neurons as a key factor in intelligence. The post highlights birds as an example, explaining how their dense neuron packing allows them to achieve primate-level intelligence with much smaller brains. The author then explores the implications of this for understanding intelligence and its potential impact on AI development, suggesting that AI capabilities might scale linearly with computing power. The post ends with a humorous reference to pilot whales, which have more cortical neurons than humans but aren't known for higher intelligence. Shorter summary

Scott Alexander explores theories to reconcile contradictory views on AI progress rates, considering the implications for AI development timelines and intelligence scaling. Longer summary

Scott Alexander discusses the apparent contradiction between Eliezer Yudkowsky's argument that AI progress will be rapid once it reaches human level, and Katja Grace's data showing gradual AI improvement across human-level tasks. He explores several theories to reconcile this, including mutational load, purpose-built hardware, varying sub-abilities, and the possibility that human intelligence variation is actually vast compared to other animals. The post ends by considering implications for AI development timelines and potential rapid scaling of intelligence. Shorter summary