Zero Shot Capabilities at Scale
One lofty goal of artificial intelligence research is developing systems with the ability to perform well on a variety of tasks without demonstrations, which is called "zero shot learning". By leveraging natural language signals and a deep understanding of how compute, data, and models interact, we've built systems with such capabilities across a variety of modalities. I will present three historic models which had unprecedented zero shot capabilities: GPT-3, CLIP, and Codex.
Nick Ryder is a research scientist at OpenAI focusing on both the engineering and science of scaling of large language models. He's worked on projects such as GPT-3 and Codex. His primary research interests include scaling laws of generative modeling and distributed computing for large transformers.