This podcast episode explores the world of large language models, focusing on the LLAMA270B model released by Meta.ai. The speaker discusses the accessibility and self-contained nature of the model, as well as the process of acquiring and training the parameters. The episode delves into the workings of neural networks, particularly in the context of next word prediction, and explains the transition from a document generator to an assistant model. It also explains the two major stages of developing language models: pre-training and fine-tuning. The podcast highlights the concept of calculating ELO scores for language models and discusses the evolving capabilities of these models, including tool use. The valuation of AI and the limitations of current large language models are explored, as well as the analogy between language models and operating systems. The vulnerabilities and potential attacks on language models, such as jailbreak attacks and prompt injection, are discussed, emphasizing the need for improved safety measures and robust defenses.
