CLIP: Connecting Text and Images
We’re introducing a neural network called CLIP which efficiently learns visual concepts from natural language supervision.
Research
Learning to Summarize with Human Feedback
We’ve applied reinforcement learning from human feedback to train language models that are better at summarization. Our models generate summaries that are better than summaries from 10x larger models trained only with supervised learning. Even though we train our models on the Reddit TL;DR dataset, the same
Image GPT
We find that, just as a large transformer model trained on language can generate coherent text, the same exact model trained on pixel sequences can generate coherent image completions and samples.
Causal Bayesian Networks: A flexible tool to enable fairer machine learning
Decisions based on machine learning (ML) are potentially advantageous over human decisions, but the data used to train them often contains human and societal biases that can lead to harmful decisions.
Replay in biological and artificial neural networks
Our waking and sleeping lives are punctuated by fragments of recalled memories: a sudden connection in the shower between seemingly disparate thoughts, or an ill-fated choice decades ago that haunts us as we struggle to fall asleep.
Using machine learning to accelerate ecological research
The Serengeti is one of the last remaining sites in the world that hosts an intact community of large mammals. These animals roam over vast swaths of land, some migrating thousands of miles across multiple countries following seasonal rainfall. As huma…
Using AI to give doctors a 48-hour head start on life-threatening illness
Artificial intelligence can now predict one of the leading causes of avoidable patient harm up to two days before it happens, as demonstrated byour latest research published in Nature.