Bolina, Vijay

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

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Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

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Secret Collusion among AI Agents: Multi-Agent Deception via Steganography

Motwani, S., Baranchuk, M., Strohmeier, M., Bolina, V., Torr, P., Hammond, L., & Witt, C. D. (2024) · Advances in Neural Information Processing Systems 37

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

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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

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The Ethics of Advanced AI Assistants

Gabriel, I., Manzini, A., Keeling, G., Hendricks, L. A., Rieser, V., Iqbal, H., Tomašev, N., Ktena, I., Kenton, Z., Rodriguez, M., et al. (2024) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

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