Blogger Alexander Rebuts Pinker: AI Intelligence Can Scale Continuously
Alexander uses g-factor evidence from animals and LLMs to refute Pinker's claim that AI intelligence cannot be extrapolated, arguing for continuous capability growth.
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In an open letter, Scott Alexander challenges Steven Pinker's view that AI intelligence is merely a set of specific problem-solving mechanisms that cannot be scaled.
Pinker argues that intelligence is inherently limited by observation and experimentation, rejecting the idea of a linear path to superintelligence. Alexander counters this by citing psychological research showing a general factor of cognition ('g') exists across species, including rats and birds.
This pattern holds for artificial systems. Research by Ruan, Maddison, and Hashimoto finds that a single general intelligence factor explains about 80% of variance in LLM performance across diverse tasks like translation and coding. Epoch's Capabilities Index further demonstrates that model performance scales predictably with parameters and data.
Alexander asserts that the default hypothesis should be that each generation of AI is more intelligent than the last, with no compelling argument for an asymptote before human-level capability. This provides an empirical basis for assessing long-term AI risks.