You can also find my publications on my Google Scholar profile.

Research Papers

Heim, L., Fist, T., Egan, J., Huang, S., Zekany, S., Trager, R., Osborne, M. A., & Zilberman, N. (Mar., 2024). Governing Through the Cloud: The Intermediary Role of Compute Providers in AI Regulation, Oxford Martin School.

Sastry, G., Heim, L., Belfield, H., Anderljung, M., Brundage, M., Hazell, J., O’Keefe, C., Hadfield, G. K., Ngo, R., Pilz, K., Gor, G., Bluemke, E., Shoker, S., Egan, J., Trager, R. F., Avin, S., Weller, A., Bengio, Y., & Coyle, D. (Feb., 2024). Computing Power and the Governance of Artificial Intelligence, White Paper.

Pilz, K., Heim, L., & Brown, N. (Feb., 2024). Increased Compute Efficiency and the Diffusion of AI Capabilities, (under review).

Kulp, G., Puri, P., Gonzales, D., Vermeer, M. J., Smith, E., Heim, L., & Winkelman, Z. (Jan., 2024). Hardware-Enabled Governance Mechanisms: Developing Technical Solutions to Exempt Items Otherwise Classified Under Export Control Classification Numbers 3A090 and 4A090, RAND Working Paper Series.

Pilz, K., & Heim, L. (Nov., 2023). Compute at Scale: A Broad Investigation into the Data Center Industry, White Paper.

Hobbhahn, M., Heim, L., & Aydos, G. (Nov., 2023). Trends in Machine Learning Hardware, Epoch Article.

Egan, J., & Heim, L. (Oct., 2023). Oversight for Frontier AI through a Know-Your-Customer Scheme for Compute Providers, White Paper.

Chan, A., Ezell, C., Kaufmann, M., Wei, K., Hammond, L., Bradley, H., Bluemke, E., Rajkumar, N., Krueger, D., Kolt, N., Heim, L., & Anderljung, M. (Feb., 2024). Visibility into AI Agents, (under review).

Besiroglu, T., Bergerson, S. A., Michael, A., Heim, L., Luo, X., & Thompson, N. (Jan., 2024). The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?, (under review).

Trager, R., Harack, B., Reuel, A., Carnegie, A., Heim, L., Ho, L., Kreps, S., Lall, R., Larter, O., hÉigeartaigh, S. Ó., Staffell, S., & Villalobos, J. J. (Sept., 2023). International Governance of Civilian AI: A Jurisdictional Certification Approach, Oxford Martin School.

Schuett, J., Dreksler, N., Anderljung, M., McCaffary, D., Heim, L., Bluemke, E., & Garfinkel, B. (May, 2023). Towards best practices in AGI safety and governance: A survey of expert opinion, GovAI Report.

OECD.AI. (Feb., 2023). A blueprint for building national compute capacity for artificial intelligence, OECD Digital Economy Papers 350; OECD Digital Economy Papers, Vol. 350.

Rando, J., Paleka, D., Lindner, D., Heim, L., & Tramèr, F. (Nov., 2022). Red-Teaming the Stable Diffusion Safety Filter, ML Safety Workshop NeurIPS 2022.

Villalobos, P., Sevilla, J., Heim, L., Besiroglu, T., Hobbhahn, M., & Ho, A. (Oct., 2022). Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning, Epoch Report.

Villalobos, P., Sevilla, J., Besiroglu, T., Heim, L., Ho, A., & Hobbhahn, M. (Jul., 2022). Machine Learning Model Sizes and the Parameter Gap, Epoch Report.

Sevilla, J., Heim, L., Ho, A., Besiroglu, T., Hobbhahn, M., & Villalobos, P. (Mar., 2022). Compute Trends Across Three Eras of Machine Learning., 2022 IJCNN Conference Paper.

Sevilla, J., Heim, L., Hobbhahn, M., Besiroglu, T., Ho, A., & Villalobos, P. (Jan., 2022). Estimating Training Compute of Deep Learning Models, Epoch Report.

Government Information Request Submissions

Heim, L., & Egan, J. (Dec., 2023). Accessing Controlled AI Chips via Infrastructure-as-a-Service (IaaS): Implications for Export Controls.

Schuett, J., Anderljung, M., Heim, L., & Seger, E. (Jul., 2023). National Priorities for Artificial Intelligence (Response to the OSTP Request for Information).

Thornton, E., Schuett, J., Anderljung, M., & Heim, L. (Jun., 2023). Response to the NTIA AI Accountability Policy Request for Comment.

Whittlestone, J., Avin, S., Heim, L., Anderljung, M., & Sastry, G. (Mar., 2023). Response to the UK’s Future of Compute Review.

Heim, L., & Anderljung, M. (Jan., 2023). Comment on October 7 advanced computing and semiconductor manufacturing equipment rule, (not publicly available).

Heim, L., & Anderljung, M. (Aug., 2022). GovAI Response to the Future of Compute Review—Call for Evidence.

Heim, L., & Anderljung, M. (Jun., 2022). Submission to the RFI on Implementing Initial Findings and Recommendations of the NAIRR Task Force.

Commentary & Op-eds

Heim, L., Anderljung, M., & Belfield, H. (Mar., 2024). To Govern AI, We Must Govern Compute, Lawfare.

Fist, T., Heim, L., & Schneider, J. (Jun., 2023). Chinese Firms Are Evading Chip Controls, Foreign Policy.

GovAI Blog Posts

Garfinkel, B., Anderljung, M., Heim, L., Trager, R., Clifford, B., & Seger, E. (Mar., 2024). Goals for the Second AI Safety Summit.

Heim, L., & Pilz, K. (Feb., 2024). What Increasing Compute Efficiency Means for the Proliferation of Dangerous Capabilities.

Heim, L., Anderljung, M., Bluemke, E., & Trager, R. (Feb., 2024). Computing Power and the Governance of AI..

Garfinkel, B., & Heim, L. (Jul., 2023). What Should the Global Summit on AI Safety Try to Accomplish?.

Anderljung, M., Heim, L., & Shevlane, T. (Apr., 2022). Compute Funds and Pre-trained Models.

Personal Blog Posts

Heim, L. (Apr., 2024). (Training) Compute Thresholds - Features and Functions in AI Governance.

Heim, L. (Mar., 2024). Considerations and Limitations for AI Hardware-Enabled Mechanisms.

Heim, L. (Feb., 2024). Crucial Considerations for Compute Governance.

Heim, L., & Pilz, K. (Feb., 2024). What share of all chips are high-end data center AI chips?.

Heim, L. (Feb., 2024). Technical AI Governance.

Heim, L. (Jun., 2023). The Case for Pre-emptive Authorizations for AI Training.

Heim, L. (Jun., 2023). This can’t go on (?)—AI Training Compute Costs.

Heim, L. (Apr., 2023). FLOP for Quantity, FLOP/s for Performance..

Heim, L. (May, 2022). Information security considerations for AI and the long term future.

Heim, L. (Apr., 2022). Estimating 🌴PaLM’s training cost..

Heim, L. (Sep., 2021). Transformative AI and Compute.

Datasets

Hobbhahn, M., Heim, L., & Aydos, G. (2023). Trends in Machine Learning Hardware.

Sevilla, J., Villalobos, P., Cerón, J. F., Burtell, M., Heim, L., Nanjajjar, A. B., Ho, A., Besiroglu, T., Hobbhahn, M., & Denain, J.-S. (2022). Parameter, compute and data trends in machine learning.

Previous Non AI Governance Work

Schadll, S., Rodriguez-Raecke, R., Heim, L., & Freiherr, J. (Jul., 2021). Playing Tetris Lets You Rate Odors as Less Intense, Frontiers in Psychology.

Heim, L., Biri, A., Qu, Z., & Thiele, L. (Apr., 2021). Measuring what Really Matters: Optimizing Neural Networks for TinyML.

Müschenich, F. S., Sichtermann, T., Di Francesco, M. E., Rodriguez-Raecke, R., Heim, L., Singer, M., Wiesmann, M., & Freiherr, J. (Dec., 2020). Some like it, some do not: Behavioral responses and central processing of olfactory–trigeminal mixture perception, Brain Structure and Function, 226(1), 247–261.

Heim, L. (Sept., 2020). Evaluation and Deployment of Resource-Constrained Machine Learning on Embedded Devices, Master Thesis, ETH Zurich & RWTH Aachen University.

Heim, L. (Sept., 2016). Network Virtualization for Automatic Deployment of SDR-Based Wireless Experiments, Bachelor Thesis, RWTH Aachen University.