{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T09:35:18Z","timestamp":1768901718813,"version":"3.49.0"},"reference-count":17,"publisher":"Association for Computing Machinery (ACM)","issue":"5","license":[{"start":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T00:00:00Z","timestamp":1682035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Commun. ACM"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:p>Seeking consistent means of measure.<\/jats:p>","DOI":"10.1145\/3563035","type":"journal-article","created":{"date-parts":[[2023,4,21]],"date-time":"2023-04-21T14:34:35Z","timestamp":1682087675000},"page":"30-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Please Report Your Compute"],"prefix":"10.1145","volume":"66","author":[{"given":"Jaime","family":"Sevilla","sequence":"first","affiliation":[{"name":"Epoch Research, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anson","family":"Ho","sequence":"additional","affiliation":[{"name":"Epoch Research, San Jose, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tamay","family":"Besiroglu","sequence":"additional","affiliation":[{"name":"Epoch Research, San Jose, CA, USA and MIT CSAIL, Cambridge, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","unstructured":"Ahmed N. and Wahed W. 'The De-Democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research'. (2020) 10.48550\/ARXIV.2010.15581","DOI":"10.48550\/ARXIV.2010.15581"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442188.3445922"},{"key":"e_1_2_1_3_1","unstructured":"Bommasani R. et al. On the Opportunities and Risks of Foundation Models. arXiv. (2022); https:\/\/bit.ly\/40LXLDi"},{"key":"e_1_2_1_4_1","unstructured":"Chowdhery A. et al. PaLM: Scaling Language Modeling with Pathways. (2022); https:\/\/bit.ly\/3mWWv1w"},{"key":"e_1_2_1_5_1","unstructured":"Cottier B. Trends in the dollar training cost of machine learning systems. (2023); https:\/\/bit.ly\/3KmVaKJ"},{"key":"e_1_2_1_6_1","unstructured":"Eloundou T. et al. D. GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv. (2023); https:\/\/arxiv.org\/abs\/2303.10130"},{"key":"e_1_2_1_7_1","unstructured":"Erdil E. and Besiroglu T. Algorithmic progress in computer vision. arXiv. (2022); https:\/\/arxiv.org\/abs\/2212.05153"},{"key":"e_1_2_1_8_1","unstructured":"Gebru T. et al. Datasheets for Datasets (2018); https:\/\/bit.ly\/3leCW4s"},{"key":"e_1_2_1_9_1","unstructured":"Hobbhahn M. How to Measure FLOP\/s for Neural Networks Empirically? (2021); https:\/\/bit.ly\/3FxCBRj"},{"key":"e_1_2_1_10_1","unstructured":"Hoffmann J. et al. Training Compute-Optimal Large Language Models. (2022); https:\/\/bit.ly\/3Tqk4vR"},{"key":"e_1_2_1_11_1","unstructured":"Kaplan J. et al. Scaling Laws for Neural Language Models. (2020); https:\/\/bit.ly\/3n2LnQy"},{"key":"e_1_2_1_12_1","volume-title":"et al","author":"Krakovna V.","year":"2022","unstructured":"Krakovna, V. et al. Specification Gaming: The Flip Side of AI Ingenuity (2022); https:\/\/bit.ly\/3n3h4t210"},{"key":"e_1_2_1_13_1","volume-title":"Proceedings of the Conference on Fairness, Accountability, and Transparency (2019)","author":"Mitchell M.","unstructured":"Mitchell, M. et al. Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (2019); https:\/\/bit.ly\/40aqu4c"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1037\/h0042519"},{"key":"e_1_2_1_16_1","doi-asserted-by":"crossref","unstructured":"Sevilla J. Compute Trends Across Three Eras of Machine Learning. (2022); https:\/\/bit.ly\/3mV2rYR","DOI":"10.1109\/IJCNN55064.2022.9891914"},{"key":"e_1_2_1_17_1","unstructured":"Sevilla J. et al. Estimating Training Compute of Deep Learning Models. (2022); https:\/\/bit.ly\/3Tn0PmG"},{"key":"e_1_2_1_18_1","unstructured":"Steinhardt J. On The Risks of Emergent Behavior in Foundation Models. (2021); https:\/\/bit.ly\/3Zyq6vE"}],"container-title":["Communications of the ACM"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3563035","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3563035","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:38:09Z","timestamp":1750178289000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3563035"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,21]]},"references-count":17,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["10.1145\/3563035"],"URL":"https:\/\/doi.org\/10.1145\/3563035","relation":{},"ISSN":["0001-0782","1557-7317"],"issn-type":[{"value":"0001-0782","type":"print"},{"value":"1557-7317","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,21]]},"assertion":[{"value":"2023-04-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}