artificial-intelligence

Prefer compositions to features in ML-Ops

I had an interesting conversation with a colleague this week. He has built a model that predicts physiological stability for inpatients on intravenous antibiotics using a recent history of vital signs. The purpose is to identify patients who could be switched to oral antibiotics and therefore discharged sooner. I had also considered that the underlying physiological stability, or its inverse, was a common component of many acute hospital prediction problems. But I wonder if we could take this further when considering how we build features under the ML-Ops paradigm.

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We are entering an era where someone might use a large language model to generate a document out of a bulleted list, and send it to a person who will use a large language model to condense that document into a bulleted list. Can anyone seriously argue that this is an improvement?

via Ted Chang in The New Yorker