[zyxin]

This makes me wonder what would happen if neural networks contain manually programmed components. It seems like trivial components such as detecting DNA sequences could be programmed in by manually setting the weights. The same thing could be done for example to give neural networks a maths component. Would the network when training discover and make use of these predefined components, or would it ignore them and make up its own ways of detecting DNA sequences?

[IanCal]

This is called feature engineering if you want to look up more of a history and use of this idea.

Edit - tokenising is a form of this, you're pre-transforming the data to save it having to learn patterns you know are important.

[astrange]

You can manually program transformers:

https://srush.github.io/raspy/

I don't know if you can integrate them into a model. I think you might run out of space, since these aren't polysemantic and so would take up a lot more "room" than learned neurons.

[btown]

In a way, this could be considered adding a speculative transformation of the input as part of the input to some layer, and the network deciding whether or not to use that transformation. It would be akin to a convolution layer in a CNN, albeit far more domain-specific. But I’m not sure how much research has been done on weird layers like this!

[WiSaGaN]

This is indeed interesting. In certain use cases where precision is paramount, we might opt for manually crafted code for the computations. This allows us to be confident in the efficiency of our manual method, rather than relying on LLM for such a specific task. However, it remains unclear whether this would be directly integrated with the network or simply be a tool at LLM's disposal. Interestingly, this situation seems to parallel the choice between enhancing the human brain with something like Neuralink and simply equipping with a calculator.

[drsopp]

I wonder what the limitations are. Do LLM's have Turing completeness?