Cross-posted here on the EA Forum.
This is the summary of the series Transformative AI and Compute - A holistic approach. You can find the sequence here and the links to the posts below:
This series attempts to:
Modern progress in AI systems has been driven and enabled mainly by acquiring more computational resources. AI systems rely on computation-intensive training runs — they require massive amounts of compute.
Learning about the compute requirements for training existing AI systems and their capabilities allows us to get a more nuanced understanding and take appropriate action within the technical and governance domain to enable a safe development of potential transformative AI systems.
To understand the role of compute, I decided to (a) do a literature review, (b) update existing work with new data, (c) investigate the role of compute for timelines, and lastly, (d) explore concepts to enhance our analysis and forecasting efforts.
In this piece, I present a brief analysis of AI systems’ compute requirements and capabilities, explore compute’s role for transformative AI timelines, and lastly, discuss the compute governance domain.
I find that compute, next to data and algorithmic innovation, is a crucial contributor to the recent performance of AI systems. We identify a doubling time of 6.2 months for the compute requirements of the final training run of state-of-the-art AI systems from 2012 to the present.
Next to more powerful hardware components, the spending on AI systems and the algorithmic innovation are other factors that inform the amount of effective compute available — which itself is a component for forecasting models on transformative AI.
Therefore, as compute is a significant component and driver of AI systems’ capabilities, understanding the developments of the past and forecasting future results is essential. Compared to the other components, the quantifiable nature of compute makes it an exciting aspect for forecasting efforts and the safe development of AI systems.
I consequently recommend additional investigations in highlighted components of compute, especially AI hardware. As compute forecasting and regulations require an in-depth understanding of hardware, hardware spending, the semiconductor industry, and much more, we recommend an interdisciplinary effort to inform compute trends interpretations and forecasts. Those insights can then be used to inform policymaking, and potentially regulate access to compute.
This article is Exploratory to My Best Guess. I've spent roughly 300 hours researching this piece and writing it up. I am not claiming completeness for any enumerations. Most lists are the result of things I learned on the way and then tried to categorize.
I have a background in Electrical Engineering with an emphasis on Computer Engineering and have done research in the field of ML optimizations for resource-constrained devices — working on the intersection of ML deployments and hardware optimization. I am more confident in my view on hardware engineering than in the macro interpretation of those trends for AI progress and timelines.
This piece was a research trial to test my prioritization, interest, and fit for this topic. Instead of focusing on a single narrow question, this paper and research trial turned out to be more broad — therefore a holistic approach. In the future, I’m planning to work more focused on a narrow relevant research question within this domain. Please reach out.
Views and mistakes are solely my own.
This work was supported and conducted as a summer fellowship at the Stanford Existential Risks Initiative (SERI). Their support is gratefully acknowledged. I am thankful for joining this program and would like to thank the organizers for enabling this, and the other fellows for the insightful discussions.
I am incredibly grateful to Ashwin Acharya and Michael Andregg for their mentoring throughout the project. Michaels thoughts on AI hardware nudged me to reconsider my current research interest and learn more about AI and compute. Ashwin for bouncing off ideas, the wealth of expertise in the domain, and helping me put things into the proper context. Thanks for the input! I was looking forward to every meeting and the thought-provoking discussions.
Thanks to the Swiss Existential Risk Initiative (CHERI) for providing the social infrastructure during my project. Having the opportunity to organize such an initiative in a fantastic team and being accompanied by motivated young researchers is astonishing.
I would like to express my thanks to Jaime Sevilla, Charlie Giattino, Will Hunt, Markus Anderljung, and Christopher Phenicie for your input and discussing ideas.
Thanks to Jaime Sevilla, Jeffrey Ohl, Christopher Phenicie, Aaron Gertler, and Kwan Yee Ng for providing feedback on this piece.
Ahmed, Nur, and Muntasir Wahed. 2020. “The De-Democratization of AI: Deep Learning and the Compute Divide in Artificial Intelligence Research.” ArXiv:2010.15581 [Cs], October. http://arxiv.org/abs/2010.15581.
Amodei, Dario, and Danny Hernandez. 2018. “AI and Compute.” OpenAI. May 15, 2018. https://openai.com/blog/ai-and-compute/.
Anderljung, Markus, and Alexis Carlier. 2021. “Some AI Governance Research Ideas.” Some AI Governance Research Ideas - EA Forum. March 6, 2021. https://forum.effectivealtruism.org/posts/kvkv6779jk6edygug/some-ai-governance-research-ideas.
Branwen, Gwern. 2020. “The Scaling Hypothesis,” May. https://www.gwern.net/Scaling-hypothesis.
Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. 2020. “Language Models Are Few-Shot Learners.” ArXiv:2005.14165 [Cs], July. http://arxiv.org/abs/2005.14165.
Carey, Ryan. 2018. “Interpreting AI Compute Trends.” AI Impacts. July 10, 2018. https://aiimpacts.org/interpreting-ai-compute-trends/.
Carlsmith, Joseph. 2020. “How Much Computational Power Does It Take to Match the Human Brain?” Open Philanthropy. August 14, 2020. https://www.openphilanthropy.org/brain-computation-report.
Centre for the Governance of AI. 2020. “A Guide to Writing the NeurIPS Impact Statement.” Medium (blog). May 19, 2020. https://medium.com/@GovAI/a-guide-to-writing-the-neurips-impact-statement-4293b723f832.
Cotra, Ajeya. 2020. “Draft Report on AI Timelines.” September 19, 2020. https://www.alignmentforum.org/posts/KrJfoZzpSDpnrv9va/draft-report-on-ai-timelines.
Crox, John. 2019. “On AI and Compute - EA Forum.” EA Forum. March 4, 2019. https://forum.effectivealtruism.org/posts/8wEDjvpcdACvYGQTq/on-ai-and-compute.
Dafoe, Allan. 2018. “AI Governance: A Research Agenda.” Centre for the Governance of AI, Future of Humanity Institute, University of Oxford. https://www.fhi.ox.ac.uk/wp-content/uploads/GovAI-Agenda.pdf.
Davidson, Tom. 2021. “Report on Semi-Informative Priors.” Open Philanthropy. March 25, 2021. https://www.openphilanthropy.org/blog/report-semi-informative-priors.
Finnveden, Lukas. 2020. “Extrapolating GPT-N Performance - AI Alignment Forum.” December 18, 2020. https://www.alignmentforum.org/posts/k2SNji3jXaLGhBeYP/extrapolating-gpt-n-performance.
Garfinkel, Ben. 2018. “Reinterpreting ‘AI and Compute.’” AI Impacts. December 18, 2018. https://aiimpacts.org/reinterpreting-ai-and-compute/.
Grace, Katja, John Salvatier, Allan Dafoe, Baobao Zhang, and Owain Evans. 2016. “2016 Expert Survey on Progress in AI.” AI Impacts (blog). December 14, 2016. https://aiimpacts.org/2016-expert-survey-on-progress-in-ai/.
Hernandez, Danny, and Tom B. Brown. 2020. “Measuring the Algorithmic Efficiency of Neural Networks.” ArXiv:2005.04305 [Cs, Stat], May. http://arxiv.org/abs/2005.04305.
Hestness, Joel, Sharan Narang, Newsha Ardalani, Gregory Diamos, Heewoo Jun, Hassan Kianinejad, Md Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. 2017. “Deep Learning Scaling Is Predictable, Empirically.” ArXiv:1712.00409 [Cs, Stat], December. http://arxiv.org/abs/1712.00409.
Kaplan, Jared, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. “Scaling Laws for Neural Language Models.” ArXiv:2001.08361 [Cs, Stat], January. http://arxiv.org/abs/2001.08361.
Khan, Saif M. 2020. “AI Chips: What They Are and Why They Matter.” Center for Security and Emerging Technology (blog). April 2020. https://cset.georgetown.edu/research/ai-chips-what-they-are-and-why-they-matter/.
———. 2021. “The Semiconductor Supply Chain.” Center for Security and Emerging Technology (blog). January 2021. https://cset.georgetown.edu/publication/the-semiconductor-supply-chain/.
Los Alamos National Laboratory. 2013. “Massive Infrastructures Are Needed to Support Supercomputers.” March 25, 2013. https://www.lanl.gov/discover/publications/national-security-science/2013-april/what-is-under the floor-of-a-supercomputer.php.
Lyzhov, Alex. 2021. “‘AI and Compute’ Trend Isn’t Predictive of What Is Happening.” “AI and Compute” Trend Isn’t Predictive of What Is Happening - AI Alignment Forum. February 4, 2021. https://www.alignmentforum.org/posts/wfpdejMWog4vEDLDg/ai-and-compute-trend-isn-t-predictive-of-what-is-happening.
Microsoft Documentation. 2020. “Deploy ML Models to FPGAs - Azure Machine Learning.” September 24, 2020. https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-fpga-web-service.
Mirhoseini, Azalia, Anna Goldie, Mustafa Yazgan, Joe Wenjie Jiang, Ebrahim Songhori, Shen Wang, Young-Joon Lee, et al. 2021. “A Graph Placement Methodology for Fast Chip Design.” Nature 594 (7862): 207–12. https://doi.org/10.1038/s41586-021-03544-w.
MLCommons. 2021. “MLCommons<sup>TM</sup> Releases MLPerf<sup>TM</sup> Training v1.0 Results.” MLCommons. June 30, 2021. https://mlcommons.org/.
Radford, Alec, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019. “Language Models Are Unsupervised Multitask Learners,” February, 24.
Sevilla, Jaime, Pablo Villalobos, Juan Felipe Cerón, Matthew Burtell, and Lennart Heim. 2021. “Parameter, Compute and Data Trends in Machine Learning.” Google Sheets (blog). June 19, 2021. https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/.
Shalf, John. 2020a. “The Future of Computing beyond Moores Law.” Philosophical Transactions of the Royal Society A, March. https://doi.org/10.1098/rsta.2019.0061.
———. 2020b. “Computing Beyond Moore’s Law.” July 14. https://cs.lbl.gov/assets/CSSSP-Slides/20200714-Shalf.pdf.
Sutton, Rich. 2019. “The Bitter Lesson.” March 13, 2019. http://www.incompleteideas.net/IncIdeas/BitterLesson.html.
Thompson, Neil C., Kristjan Greenewald, Keeheon Lee, and Gabriel F. Manso. 2020. “The Computational Limits of Deep Learning.” ArXiv:2007.05558 [Cs, Stat], July. http://arxiv.org/abs/2007.05558.
Thompson, Neil C., and Svenja Spanuth. 2021. “The Decline of Computers as a General Purpose Technology.” Communications of the ACM 64 (3): 64–72. https://doi.org/10.1145/3430936.
Transformative AI, as defined by Open Philanthropy in this blogpost: “Roughly and conceptually, transformative AI is AI that precipitates a transition comparable to (or more significant than) the agricultural or industrial revolution.” ↩︎