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Self-improving LLM is as probable as a perpetual motion machine. Practically, LLMs train on data. Any output of an LLM is a derivative of the training data and
by weregiraffe 1y ago
Self-improving LLM is as probable as a perpetual motion machine.
Practically, LLMs train on data. Any output of an LLM is a derivative of the training data and can't teach it anything new.
Conceptually, if a stupid AI can build a smart AI, it would mean that the stupid AI is actually smart, otherwise it wouldn't have been able too.
- Marha01 1y agoYour logic might make intuitive sense, but I don't think it is as ironclad as you portray it. The fact is, there is no law of physics that prevents the existence of a system that can decrease its internal entropy (complexity) on its own, provided you constantly supply it with energy (negative entropy). Evolutionary algorithm (or "life") is an example of such a system. It is conceivable that there is a point when a LLM is smart enough to be useful for improving its own training data, which then can be used to train a slightly smarter version, which can be used to improve the data even more etc... Every time you inference to edit the training data and train, you are supplying a large amount of energy into the system (both inferencing and training consumes a lot of energy). This is where the decrease in entropy (increase in internal model complexity and intelligence) can come from.