closing that loop still doesn't guarantee runaway "takeoff," and why measuring real progress requires accounting for both capability gains and the resources required to achieve them.
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The canonical evidence of polysemanticity and superposition that convinced much of the field, including me, that networks pack concepts arbitrarily was misread. What looked like arbitrary packing was mostly a motif I'll call recursive generalization, which is much less concerning. This means we can study networks directly, instead of first untangling them. Neural networks are already clean.
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Powerful AI models continue to cause chaos, but their targets aren't widely seeking justice. Instead, they're being encouraged to adopt those same tools.
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