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Artificial Intelligence Index Report 2023

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arxiv 2310.03715 v1 pith:BIRKAFSB submitted 2023-10-05 cs.AI cs.CY

Nestor Maslej , Loredana Fattorini , Erik Brynjolfsson , John Etchemendy , Katrina Ligett , Terah Lyons , James Manyika , Helen Ngo
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Juan Carlos Niebles Vanessa Parli Yoav Shoham Russell Wald Jack Clark Raymond Perrault
This is my paper · ORCID
classification cs.AIcs.CY
keywords reportdataindexartificialchaptereditionintelligenceoriginal
verification ladder T0 review T1 audit T2 compute T3 formal
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Welcome to the sixth edition of the AI Index Report. This year, the report introduces more original data than any previous edition, including a new chapter on AI public opinion, a more thorough technical performance chapter, original analysis about large language and multimodal models, detailed trends in global AI legislation records, a study of the environmental impact of AI systems, and more. The AI Index Report tracks, collates, distills, and visualizes data related to artificial intelligence. Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI. The report aims to be the world's most credible and authoritative source for data and insights about AI.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 188 citations worldwide. Full citation record

  1. Energy Considerations for Large Pretrained Neural Networks

    cs.LG 2025-06 reject novelty 4.0 of 10

    Fine-tuning pretrained CNNs on ImageNet used far less electricity when weights were quantized to about 8 to 13 bits, while pruning and low-rank factorization did not reliably reduce energy.

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