Yannic Kilcher, a YouTube AI savant, and Connor Shorten, a machine learning contributor, dive into the revolutionary GPT-3 language model. They discuss its jaw-dropping 175 billion parameters and how it performs various NLP tasks with zero fine-tuning. The duo unpacks the differences between autoregressive models like GPT-3 and BERT, as well as the complexities of reasoning versus memorization in language models. Additionally, they tackle the implications of AI bias, the significance of transformer architecture, and the future of generative AI.
