Meaning-making is all about discovering useful sign (see: Peirce) rewrite rules. #ai
The conventional artificial neural network (i.e. sum of product of weights) is a rewrite rule from a vector to a scalar. Each layer is a rewrite rule from a vector to another vector.
A transformer block is a rewrite rule from a set of discrete symbols into vectors and back again to discrete symbols.
Execution of programming code are just rewrite rules transforming high-level code to machine code for execution.
Re-write rules can do everything. The hard problem is discovering these re-write rules. The even harder problem is formulating a system that discovers these re-write rules.
Deep Learning networks learn re-write rules by adjusting weight matrices. No new rules are added, just the relative importance of rules are adjusted. Like biology, this involves a differentiation process and not an additive process.
DL networks only work if given sufficient diversity on initialization. Initializing all weights uniformly is a recipe for failure.
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