The $1 Trillion Question
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A new paper argues for analyzing AI systems like GPT through a "teleological" lens focused on the specific problem they were optimized to solve during training. 7 min read
Key Points:
Clark's nutcrackers cache food like no other. These birds hide thousands of seeds in the soil every winter and depend on recalling cache locations months later to survive. Their extraordinary spatial memory outperforms that of related species like scrub jays.
This difference makes sense through a teleological lens: the ecological need to find cached food shaped the evolution of enhanced spatial abilities in nutcrackers. Their cognitive specialization arose from the particular problems their lifestyle requires solving.
Similar principles extend to artificial intelligence. Analyzing what objective a system was optimized for reveals a lot about resulting capabilities. Just as naturally selected skills reflect challenges faced, so too do trained AI abilities mirror prescribed training goals.
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