Steptoe Cyberlaw Podcast

Maury Shenk opens this episode with an exploration of three efforts to overcome notable gaps in the performance of large language AI models. OpenAI has developed a tool meant to address the models’ lack of explainability. It uses, naturally, another large language model to identify what makes individual neurons fire the way they do. Maury is skeptical that this is a path forward, but it’s nice to see someone trying. The other effort, Anthropic’s creation of an explicit “constitution” of rules for its models, is more familiar and perhaps more likely to succeed. We also look at the use of “open source” principles to overcome the massive cost of developing new models and then training them. That has proved to be a surprisingly successful fast-follower strategy thanks to a few publicly available models and datasets. The question is whether those resources will continue to be available as competition heats up. The European Union has to hope that open source will succeed, because the entire continent is a desert