Pith. sign in

REVIEW 4 cited by

Artificial Intelligence Index Report 2023

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
show 6 more authors
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

Signed reviews

No signed human review yet.

0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

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. Dissecting the NVIDIA Hopper Architecture through Microbenchmarking and Multiple Level Analysis

    cs.DC 2025-01 conditional novelty 6.0 of 10

    A multi-level benchmark study characterizes Hopper GPU memory, tensor cores, TMA, DSM, and DPX, reporting speedups for FP8, TMA, and 16-bit DPX instructions.

  2. 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.

  3. The Global AI Vibrancy Tool

    cs.CY 2024-11 conditional novelty 4.0 of 10

    The Global AI Vibrancy Tool ranks 36 countries on AI activity from 2017 to 2023, with the US leading, and adds Innovation, Economic Competitiveness, and Policy Governance sub-indices.

  4. Deciphering the AI Economy: A Mathematical Model Perspective

    econ.GN 2025-05 reject novelty 2.0 of 10

    A 12-year, single-country regression between a constructed AI-vector index and Georgia's GDP per capita yields a 23.9% elasticity, but the index is opaque, partly constant, and not causally identified.

Pith tools