Statistical Methods in Generative AI

Artificial Intelligence, and more specifically, Generative AI, is emerging as an important technology. Over the past few years a number of prominent generative AI technologies have been developed and have received widespread attention; ranging from text generation via large language models (ChatGPT, Claude, Llama, Gemini, DeepSeek, Qwen, etc), image generation via diffusion models (Dall-E, Stable Diffusion, etc), to scientific generative AI techniques used for protein generation (e.g., Watson et al. 2023, etc), DNA sequence editing (e.g., Ruffolo et al. 2025, etc), among others. Such methods have been quickly adopted by end users and institutions, both via direct usage, as well as integrated in other tools such as code assistants and web search agents. The scientific community has shown significant interest in using generative AI models, achieving a number of breakthrough results (see e.g., Davies et al. 2021, Hayes et al. 2025, etc), culminating in a 2024 Nobel Prize in Chemistry awarded in part for work with a significant component in protein structure design and generation (The Royal Swedish Academy of Sciences 2024). Yet, the adoption of generative AI (GenAI) methods more generally is hindered by their lack of reliability (see e.g., Farquhar et al. 2024, Strauss et al. 2025, Manduchi et al. 2025, etc).