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Comment by pilooch | Hacker News Reader

No. Because the model should and will replace any piece of code. This is what happened already for other tasks, in computer vision, text (entity recognition, etc...), audio, etc.

RAG will go away, decision multimodal models / LLMs will take over. No here yet, but inevitable I believe.

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End to end networks can sometimes have higher performance, but the failure mechanisms aren't explainable, and are unintuitive to humans.

If you're building something that needs to be easy to work with, and that humans can understand the limitations of, splitting the network up into stages and having human-interpretable intermediate values is a good architecture choice.

No so sure these are even mutually exclusive positions. The BYO data needs will far exceed the longest contexts for a long time to come. LLMs might integrate RAG to the point it is hard to talk about one without the other (DSPy is close) but there will still be some kind of private knowledge base graph feeding incontext learning so improvements in either area is positive.

This seems like a nonsensical position. Computer vision models have not replaced every piece of code -- there's still harness, formatting, even old-fashioned classical vision processing that goes on both before and after the model runs. It's perfectly reasonable to couch AI models inside other, classical code.

We have end to end robot learning models that take nothing but camera input and instructions and directly produce the robot motions. There is literally no code left except for the sensors and actuators. It's all in the model.

not nesc.

like I would rather use code to find apriltags than a vit or other model

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