Best of last 30 days intelligence

AI updates June -Vj1? ?VJ2? ? ?YJ1? ?YJ2? ?XM1 -

mo(o)re Friends20.com - special thks Z: King (English LLM) Charles AI world series

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My original article was intentionally short - a new start of week series if you only want to check in with very best of ai leaos - but i have wanted to post this list of refences from AI chapter 2 of a recent oecd report somewhere; and whil not directly related to the top od this article I admire hoiw many of its references are pretty new and suitably 4 hemisphere

References

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[131] Ahmed, N. and M. Wahed (2020), “The de-democratization of AI: Deep learning and the compute divide in artificial intelligence research”, arXiv, 2010.15581,?https://arxiv.org/abs/2010.15581.

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[58] Bryant, M. (2019), “How AI and machine learning are changing prosthetics”, 29 March, MedTech Dive,?https://www.medtechdive.com/news/how-ai-and-machine-learning-are-changing-prosthetics/550788.

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[102] Dung, L. (2023), “Current cases of AI misalignment and their implications for future risks”,?Synthese, Vol. 202/138,?https://doi.org/10.1007/s11229-023-04367-0.

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[147] European Commission (2021),?Regulation of the European Parliament and of the Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts, COM(2021) 206 final, European Commission, Brussels,?https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=celex%3A52021PC0206.

[7] EU-US Trade and Technology Council (2023),?EU-U.S. Terminology and Taxonomy for Artificial Intelligence, First Edition, EU-US Trade and Technology Council,?https://digital-strategy.ec.europa.eu/en/library/eu-us-terminology-and-taxonomy-artificial-intelligence.

[75] Fariani, R., K. Junus and H. Santoso (2023), “A systematic literature review on personalised learning in the higher education context”,?Technology, Knowledge and Learning, Vol. 28/2, pp. 449–476,?https://link.springer.com/article/10.1007/s10758-022-09628-4.

[146] G7 Hiroshima Summit (2023),?Hiroshima AI Process G7 Digital & Tech Ministers’ Statement, G7 Hiroshima Summit,?https://www.soumu.go.jp/hiroshimaaiprocess/pdf/document02.pdf.

[132] Ganguli, D. et al. (2022), “Predictability and surprise in large generative models”, arXiv, 2202.07785 [cs],?https://arxiv.org/abs/2202.07785.

[71] Ghosh, A. (2023), “How can artificial intelligence help scientists? A (non-exhaustive) overview”,?in?Artificial Intelligence in Science: Challenges, Opportunities and the Future of Research, OECD Publishing, Paris,?https://doi.org/10.1787/a8e6c3b6-en.

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[69] Goldman Sachs (2023), “Generative AI could raise global GDP by 7%”, 5 April, Goldman Sachs,?https://www.goldmansachs.com/intelligence/pages/generative-ai-could-raise-global-gdp-by-7-percent.html.

[141] Government of Canada (2019),?Directive on Automated Decision-Making, Treasury Board Secretariat, Government of Canada,?https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592.

[155] GPAI (2023), “About”, webpage,?https://gpai.ai/about?(accessed on 8 January 2024).

[105] Grallet, G. and H. Pons (2023), “Yuval Noah Harari (Sapiens) versus Yann Le Cun (Meta) on artificial intelligence”, 5 November, Le Point,?https://www.lepoint.fr/sciences-nature/yuval-harari-sapiens-versus-yann-le-cun-meta-on-artificial-intelligence-11-05-2023-2519782_1924.php.

[127] Green, A. and L. Lamby (2023), “The supply, demand and characteristics of the AI workforce across OECD countries”, OECD Social, Employment and Migration Working Papers, No. 287, OECD Publishing, Paris,?https://doi.org/10.1787/bb17314a-en.

[17] Heikkil?, M. (2022), “The hype around DeepMind’s new AI model misses what’s actually cool about it”, 23 May, MIT Technology Review,?https://www.technologyreview.com/2022/05/23/1052627/deepmind-gato-ai-model-hype.

[92] Heikkil?, M. (2022), “The viral AI avatar app Lensa undressed me—without my consent”, 12 December, MIT Technology Review,?https://www.technologyreview.com/2022/12/12/1064751/the-viral-ai-avatar-app-lensa-undressed-me-without-my-consent.

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[95] Horowitz, M. (2023), “Bending the automation bias curve: A study of human and AI-based decision making in national security contexts”, arXiv, 2306.16507,?https://arxiv.org/abs/2306.16507.

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[67] IDC (2022), “IDC forecasts 18.6% compound annual growth for the artificial intelligence market in 2022-2026”, 29 July, International Data Corporation,?https://www.idc.com/getdoc.jsp?containerId=prEUR249536522.

[149] IMDA (2022), “About artificial intelligence”, webpage,?https://www.imda.gov.sg/How-We-Can-Help/AI-Verify?(accessed on 8 January 2024).

[114] INRIA (2023), “French national artificial intelligence research program”, webpage,?https://www.inria.fr/en/french-national-artificial-intelligence-research-program?(accessed on 8 January 2024).

[13] Islam, K. (2022), “Recent advances in vision transformer: A survey and outlook of recent work”, arXiv, 2203.01536,?https://arxiv.org/abs/2203.01536.

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[23] Jones, E. (2023), “Explainer: What is a foundation model?”, 17 July, Ada Lovelace Institute,?https://www.adalovelaceinstitute.org/resource/foundation-models-explainer.

[60] Kaplan, J. et al. (2020), “Scaling laws for neural language models”, arXiv, 2001.98361,?https://arxiv.org/pdf/2001.08361.pdf.

[26] Khan, S., A. Mann and D. Peterson (2021),?The Semiconductor Supply Chain: Assessing National Competititveness, Center for Security and Emerging Technology,?https://cset.georgetown.edu/wp-content/uploads/The-Semiconductor-Supply-Chain-Issue-Brief.pdf.

[79] Kreps, S., R. Mccain and M. Brundage (2022), “All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation”,?Journal of Experimental Political Science, Vol. 9/1, pp. 104–17,?https://doi.org/10.7910/DVN/1XVYU3.

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[61] LeCun, Y. (2022), “A path towards autonomous machine intelligence”,?Openreview.net,?https://openreview.net/pdf?id=BZ5a1r-kVsf.

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[10] Lorenz, P., K. Perset and J. Berryhill (2023), “Initial policy considerations for generative artificial intelligence”, OECD Artificial Intelligence Papers, No. 1, OECD Publishing, Paris,?https://doi.org/10.1787/fae2d1e6-en.

[62] Marcus, G. (2023), “Babbage: is GPT-4 the dawn of true artificial intelligence”, The Economist Podcasts,?https://podcasts.apple.com/ca/podcast/economist-radio/id151230264?i=1000605454954.

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[16] Merritt, R. (25 March 2022), “What Is a transformer model?”, NVIDIA blog,?https://blogs.nvidia.com/blog/2022/03/25/what-is-a-transformer-model.

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[84] Metz, C. (2023), “What exactly are the dangers posed by A.I.?”, 7 May,?The New York Times,?https://www.nytimes.com/2023/05/01/technology/ai-problems-danger-chatgpt.html.

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[21] Morris, M. et al. (2023), “Levels of AGI: Operationalizing progress on the path to AGI”, arXiv, 2311.02462,?https://arxiv.org/abs/2311.02462.

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[91] NIST (2022),?Towards a Standard for Identifying and Managing Bias in Artificial Intelligence, Special Publication (NIST SP), National Institute of Standards and Technology, Gaithersburg, MD,?https://doi.org/10.6028/NIST.SP.1270,?https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=934464.

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[64] OECD (forthcoming), “Exploring artificial intelligence futures: Prospective milestones, benefits and risks”, OECD Artificial Intelligence Papers, OECD Publishing, Paris,?https://doi.org/10.1787/dee339a8-en.

[6] OECD (2024), “Explanatory memorandum on the updated OECD definition of an AI system”,?OECD Artificial Intelligence Papers, No. 8, OECD Publishing, Paris,?https://doi.org/10.1787/623da898-en.

[8] OECD (2023), “A blueprint for building national compute capacity for artificial intelligence”, OECD Digital Economy Papers, No. 350, OECD Publishing, Paris,?https://doi.org/10.1787/876367e3-en.

[4] OECD (2023), “AI language models: Technological, socio-economic and policy considerations”, OECD Digital Economy Papers, No. 352, OECD Publishing, Paris,?https://doi.org/10.1787/13d38f92-en.

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[3] OECD (2019),?Artificial Intelligence in Society, OECD Publishing, Paris,?https://doi.org/10.1787/eedfee77-en.

[27] OECD (2019), “Measuring distortions in international markets: The semiconductor value chain”, OECD Trade Policy Papers, No. 234, OECD Publishing, Paris,?https://doi.org/10.1787/8fe4491d-en.

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[124] OECD.AI (2024), “Live data: Investments in AI and data”, OECD.AI Policy Observatory?(database),?https://oecd.ai/en/data?selectedArea=investments-in-ai-and-data,?https://oecd.ai/en/data?selectedArea=investments-in-ai-and-data?(accessed on 14 February 2024).

[118] OECD.AI (2024), “National AI policies & strategies”,?OECD. AI Policy Observatory?(database),?https://oecd.ai/en/dashboards/overview?(accessed on 10 March 2024).

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Notes

← 1.?“Synthetic data is generated from data/processes and a model that is trained to reproduce the characteristics and structure of the original data aiming for similar distribution. The degree to which synthetic data is an accurate proxy for the original data is a measure of the utility of the method and the model.” (EU-US Trade and Technology Council, 2023[7]).

← 2.?The initial set of potential future AI solutions can be found at:?https://easyretro.io/publicboard/Lg97hwaJe8MJWJTe5uKGfjjtInh1/f63b59a0-0531-456e-82f2-d9bea1463fb9.

← 3.?At the time of writing, top research institutions in 2023 according to data from OpenAlex available on OECD.AI include Chinese Academy of Sciences (China), French National Centre for Scientific Research (France), Tsinghua University (China), Shanghai Jiao Tong University (China), Zhejiang University (China), Harbin Institute of Technology (China), Beihang University (China), Huazhong University of Science (China), Stanford University (United States) and the Max Planck Society (Germany). Publications are in multiple languages, not just in English. Data available at?https://oecd.ai/en/data?selectedArea=ai-research&selectedVisualization=ai-publication-time-series-by-institution.

← 4.?AI keywords used include: generic AI keywords, such as “artificial intelligence” and “machine learning”; keywords pertaining to AI techniques, such as “neural network”, “deep learning”, “reinforcement learning”; and keywords referring to fields of AI applications, such as “computer vision”, “predictive analytics”, “natural language processing”, “autonomous vehicles”.

← 5.?In recent years, supercomputer systems have been increasingly updated to also run AI-specific workloads. However, the list does not distinguish supercomputers according to workload capacity specialised for AI. Drawing conclusions from the list should thus be made cautiously: the list does not define “supercomputers” for AI but uses a benchmark methodology (Linpack). This means any supercomputer can make it into the Top500 list if it can solve a set of linear equations using floating point arithmetic. As submitting to the list is voluntary, some countries have reportedly slowed or stopped submissions of top supercomputers to the list in recent years. Thus, country comparisons using this data are limited and should be considered with this caveat. Analysis of the Top500 list can serve as a proxy measure to observe emerging or deepening compute divides between economies. However, such figures should be supplemented by AI-specific analysis as countries reflect on their specific national AI compute needs.

← 6.?Maximum achieved performance measured by Rmax in tera floating-point operations per second (TFLOPS).

← 7.?Members include the Council of Europe, the European Commission, the European Union Agency for Fundamental Rights, the Inter-American Development Bank, the OECD, the United Nations, UNESCO and the World Bank.

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