This post is a transcript of a discussion between Carl Shulman and Eliezer Yudkowsky, following up on a conversation with Paul Christiano and Ajeya Cotra. Color key: Chat by Carl and Eliezer Other chat 9.14. Carl Shulman's predictions [Shulman][20:30] I'll interject some points re the earlier discussion about how animal data relates to the 'AI scaling to AGI' thesis. 1. In humans it's claimed the IQ-job success correlation varies by job, For a scientist or doctor it might be 0.6+, for a low complexity job more like 0.4, or more like 0.2 for simple repetitive manual labor. That presumably goes down a lot with less in the way of hands, or focused on low density foods like baleen whales or grazers. If it's 0.1 for animals like orcas or elephants, or 0.05, then there's 4-10x less fitness return to smarts. 2. But they outmass humans by more than 4-10x. Elephants 40x, orca 60x+. Metabolically (20 watts divided by BMR of the animal) the gap is somewhat smaller though, because of metabolic scaling laws (energy scales with 3/4 or maybe 2/3 power, so ). https://en.wikipedia.org/wiki/Kleiber%27s_law If dinosaurs were poikilotherms, that's a 10x difference in energy budget vs a mammal of the same size, although there is debate about their metabolism. 3. If we're looking for an innovation in birds and primates, there's some evidence of 'hardware' innovation rather than 'software.' Herculano-Houzel reports in The Human Advantage (summarizing much prior work neuron counting) different observational scaling laws for neuron number with brain mass for different animal lineages. > We were particularly interested in cellular scaling differences that might have arisen in primates. If the same rules relating numbers of neurons to brain size in rodents (6) > > The brain of the capuchin monkey, for instance, weighing 52 g, contains >3× more neurons in the cerebral cortex and ≈2× more neurons in the cerebellum than the larger brain of the capybara, weighing 76 g. [