The author argues that large language models (LLMs) like ChatGPT are the most interpretable form of machine learning that has come into broad usage. They believe that LLMs are the first AI/ML technology to truly realize what it means to give a human-centric explanation for what they produce. The author also discusses the limitations of explainable AI and interpretable models, stating that they often don't fit how people think. They suggest that LLMs are the answer to explainable AI, as they allow users to probe them like they would a fellow person, and help facilitate learning in unplanned ways. However, they also acknowledge that LLMs need to improve in areas such as self-awareness, tone adjustment, and referential transparency.
Key takeaways:
The author argues that Language Learning Models (LLMs) are the most interpretable form of machine learning that has come into broad usage.
LLMs are considered uninterpretable by traditional definitions due to their billions of parameters, but the author argues that they are the first AI/ML technology to truly realize what it means to give a human-centric explanation for what they produce.
The author believes that LLMs are the answer to explainable AI, as they allow users to probe and ask questions, much like interacting with a fellow person.
However, improvements are needed in areas such as self-awareness, tone adjustment, mind melding, and referential transparency for LLMs to be fully trusted and effective.
