Forbes 7/27/2023

One of the limitations of large language models is that their training is frozen in time. If you ask OpenAI’s viral chatbot ChatGPT if Covid vaccines work against the most common variant circulating in 2023, it responds: “As an AI language model, I don’t have access to real-time data or information beyond my last update in September 2021.”

A tremendous amount has changed since then – there are new Covid strains, new vaccine and drug approvals, and tens of thousands of new scientific studies. In order for chatbots to be useful in a medical setting, they are going to need access to the latest research. Armed with $32 million in capital, nearly a dozen employees with PhDs (or PhD candidates) and a supercomputer in the Nevada desert, Daniel Nadler has been working to solve this knowledge cutoff problem with his new startup OpenEvidence.

Constantly retraining machine learning models requires huge amounts of costly computing power, but there is another option. It’s a technical and engineering challenge that involves “marrying these language models with a real-time firehose of clinical documents,” says OpenEvidence founder Nadler, 40. Essentially, granting the AI access to a new pool of data right before it goes to answer the question – a process computer scientists call “retrieval augmented generation.” If you ask OpenEvidence’s chatbot the question about vaccines and the new Covid variant, it responds that “specific studies on this variant are limited” and includes information from studies published in February and May 2023 with citations. The main difference, says Nadler, is that his model “can answer with an open book, as opposed to a closed book.”

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