Shevlane, Toby

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

Model evaluation for extreme risks

Shevlane, T., Farquhar, S., Garfinkel, B., Phuong, M., Whittlestone, J., Leung, J., Kokotajlo, D., Marchal, N., Anderljung, M., Kolt, N., et al. (2023) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 66

The Offense-Defense Balance of Scientific Knowledge: Does Publishing AI Research Reduce Misuse?

Shevlane, T., & Dafoe, A. (2020) · Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 28

Evaluating Frontier Models for Dangerous Capabilities

Phuong, M., Aitchison, M., Catt, E., Cogan, S., Kaskasoli, A., Krakovna, V., Lindner, D., Rahtz, M., Assael, Y., Hodkinson, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIAdversarial Robustness in Machine Learning

Cited by 11

A Mechanism-Based Approach to Mitigating Harms from Persuasive Generative AI

El-Sayed, S., Akbulut, C., McCroskery, A., Keeling, G., Kenton, Z., Jalan, Z., Marchal, N., Manzini, A., Shevlane, T., Vallor, S., et al. (2024) · arXiv

Ethics and Social Impacts of AIMisinformation and Its Impacts

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