Generative AI (or GenAI) refers to AI systems that can create new content like text, images, music, code, or video. These systems learn patterns from training data and generate novel outputs that resemble the original data, often powered by architectures like GANs, transformers, diffusion models, and variational autoencoders. These models power applications including chatbots, code generation, and creative tools. They also raise questions about the potential for misuse including creating misinformation and deepfakes.
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Explore Similar Terms:
Large Language Model (LLM) | Diffusion Models | GANs (Generative Adversarial Networks)

A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world
A diversity of perspectives from Stanford leaders in medicine, science, engineering, humanities, and the social sciences on how generative AI might affect their fields and our world


Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.
Trained on a dataset that includes all known living species – and a few extinct ones – Evo 2 can predict the form and function of proteins in the DNA of all domains of life and run experiments in a fraction of the time it would take a traditional lab.


Synthetic brain MRI technology is supercharging computational neuroscience with massive data.
Synthetic brain MRI technology is supercharging computational neuroscience with massive data.


Experts in technology, law, and human rights debate the unique implications of this technology and how we might best direct its potential to benefit humanity.
Experts in technology, law, and human rights debate the unique implications of this technology and how we might best direct its potential to benefit humanity.
