Powerful new large-scale AI models like GPT-4 are showing dramatic improvements in reasoning, problem-solving, and language capabilities. This marks a phase change for artificial intelligence—and a signal of accelerating progress to come.
In this Microsoft Research Podcast series, AI scientist and engineer Ashley Llorens hosts conversations with his collaborators and colleagues about what these new models—and the models that will come next—mean for our approach to creating, understanding, and deploying AI, its applications in areas such as health care and education, and its potential to benefit humanity.
This episode features Senior Principal Researcher Emre Kiciman and Principal Researcher Amit Sharma, whose paper “Causal Reasoning and Large Language Models: Opening a New Frontier for Causality” examines the causal capabilities of large language models (LLMs) and their implications. Kiciman and Sharma break down the study of cause and effect; recount their respective ongoing journeys with GPT-3.5 and GPT-4—from their preconceptions to where they are now—and share their views of a future in which LLMs help bring together different modes of reasoning in the practice of causal inference and make causal methods easier to adopt.
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