PaLM 2 Technical Report

Researcher at Anthropic working on machine learning, language models, and reinforcement learning. Previously a postdoctoral researcher at Meta FAIR, and he earned a PhD in computer science from Harvard University.

Research scientist at Google Research in New York City. He earned a PhD in computer science from NYU Tandon and works on language models, multimodal learning, NLP, and deep learning.

Resident at xAI and long-term visitor at the Center for AI Safety. He recently completed a PhD in electrical and computer engineering at the University of Texas at Austin, and his work is grounded in mathematical and statistical machine learning plus control theory.

Assistant professor in the University of Pennsylvania Department of Computer and Information Science whose research spans natural language processing, speech, and multimodal learning; previously a research scientist at Facebook AI Research.

Aakanksha Chowdhery is a machine learning researcher based in New York City. She works on large-scale machine learning across pre-training, post-training, inference, and system efficiency, and is known for contributions such as PaLM, Pathways, and Gemini.

AI researcher focused on reasoning and agents who worked on GPT-4, o1, and Deep Research; previously at Google Brain and earned a PhD at MIT.

Researcher at Thinking Machines, formerly at OpenAI, working on real-time multimodal systems across language, vision, and voice.

Research leader at Google DeepMind and Google Research focused on natural language processing and machine learning, including large language models such as PaLM and Gemini.

Brian Lester is a Senior Research Engineer at Google working on natural language processing and efficient ways to adapt large pre-trained language models. His public work includes prompt tuning, SPoT, and FLAN-related research.

Postdoctoral fellow at Johns Hopkins University and incoming assistant professor at UNC Chapel Hill whose work focuses on multilingual NLP, grounded language understanding, and societal impacts of language technologies.

Research software engineer at Google DeepMind working on machine learning for code and formal reasoning; completed a PhD at TU Darmstadt on uncertainty quantification in deep learning.

Jiahui Yu is a research scientist at Google DeepMind working on multimodal learning and large language models.

Xinyun Chen is a machine learning researcher at Meta FAIR. Her work spans large language models, code generation, mathematical reasoning, and efficient inference.

Ali Payani is a Research Scientist at Google DeepMind working on natural language generation and natural language reasoning. His public research interests include reasoning, planning, and grounding with language models.

Research scientist and engineer at OpenAI; previously worked at Google DeepMind on language models, reinforcement learning, and robotics, and is known for neural network interpretability and visualization.

Chief Technology Officer at Google DeepMind, with work spanning machine learning and reinforcement learning.

Google researcher whose public profile says he joined Google in September 2008 and has been with the Google Brain team since January 2015, with interests spanning information retrieval, learning to rank, machine learning, machine translation, and natural language processing.

Vice president at Google DeepMind in London working on machine learning and reinforcement learning. His public profile notes work spanning policy-gradient methods, distributed and generative models, and robotics.

Research director at Google working on music AI, multimodal generation, and human-AI interaction. He co-founded the Magenta project and has led widely used work on music generation with neural networks.

Carlos Riquelme is a Senior Research Scientist at Google Research in New York City. His public work uses reinforcement learning and meta-learning to improve large language models.

Xuezhi Wang is a Research Scientist at Google DeepMind whose public work focuses on reasoning and systematic generalization in language models, along with robustness and fairness in natural language processing. She received her PhD from Carnegie Mellon University in 2016.

Research scientist at Google working on multilingual generation, machine translation, and large language model systems. His public work spans translation quality, evaluation, and scalable language technology.

Research scientist at Google DeepMind.

Inventor, author, and futurist serving as Principal Researcher and AI Visionary at Google, known for long-running work on AI, language technology, and human-computer systems.