A discussion with the founders of Waddle Labs and RoboCurve on how general-purpose language models are beginning to control robots. The conversation covers the lineage from RT-2 and code-as-policies to in-context learning, harness design, skill consolidation, Platonic representations, and the path toward general-purpose robots.
Vasant Kearney presents a nuanced perspective on implementing AI in healthcare, particularly in insurance. He highlights that while AI is evolving rapidly, real-world application requires understanding that solving isolated problems doesn't equate to complex solutions. His core argument is the utility of X12 as a 'harness' for agentic AI, providing a structured, confining framework for LLMs in healthcare. This approach acknowledges that X12 defines the contract between providers and payers, and even phone calls can be seen as X12 transactions. However, he cautions that X12, or any payer data, isn't ground truth due to disparate system origins, necessitating an internal 'semi-correct' representation. Kearney advocates for a balanced "AI pilled and AI skeptical" stance, embracing AI's potential while remaining cautious about its inherent limitations, cost, and the necessity for robust system design and evaluation.