System One models like Jev can train their own replacements

“System One” models like Jev are fast general classifiers. Classifiers have existed since 1958, but they have to be trained for specific tasks: if you build a classifier to identify images of dogs, it can’t be used to tell you if a streetlight is red, or if a letter is urgent. Like a LLM, Jev can be prompted for a wide variety of tasks, from sorting email to playing Doom. I think models like this are going to be important. There are many tasks that a LLM could do in theory but are too slow and expensive in practice (for instance, reading each new message in Slack1 and deciding whether to notify you or not). While you could train a specific classifier for these tasks, there are two main problems with that: Despite being a well-understood ML problem, training a bespoke classifier is outside of the skillset of most ordinary engineering teams Training a classifier requires assembling a large dataset Jev obviously solves the first problem. Any engineering team can plug in a System One…

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