Goldman finds ‘no meaningful relationship between AI and productivity at the economy-wide level,’ but a 30% boost for 2 specific use cases

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-- Nominal: Meters ≠ Seconds (different names)

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At first glance, the benchmarks and their construction looked good (i.e. no cheating) and are much faster than working with UMAP in Python. To further test, I asked the agents to implement additional different useful machine learning algorithms such as HDBSCAN as individual projects, with each repo starting with this 8 prompt plan in sequence:

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