Bridgers, Sophie

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

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

Cited by 306

When children are better (or at least more open-minded) learners than adults: Developmental differences in learning the forms of causal relationships

Lucas, C. G., Bridgers, S., Griffiths, T. L., & Gopnik, A. (2014) · Cognition

Child and Animal Learning DevelopmentBayesian Modeling and Causal Inference

Cited by 240

The power of possibility: causal learning, counterfactual reasoning, and pretend play

Buchsbaum, D., Bridgers, S., Skolnick Weisberg, D., & Gopnik, A. (2012) · Philosophical Transactions of the Royal Society B: Biological Sciences

Child and Animal Learning DevelopmentBayesian Modeling and Causal Inference

Cited by 200

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

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

Cited by 10