Exploring FORTH and Associative Languages as Better Fits for Transformer Architectures
By
rescrv
A bagel you'd recommend to a friend without hedging.
Summary
The article explores the idea that FORTH and associative/applicative programming languages might be better suited for transformer architectures than traditional recursive approaches. The author argues against the common practice of using LLMs to break down problems recursively like humans do, instead proposing that concatenative languages that focus on stack state agreement could be more effective for AI systems. The piece presents a philosophical and technical argument about programming language design for AI architectures.
Key quotes
· 4 pulledLLMs are wonderful, but I see too many people try to break down recursively to solve problems like top-down humans do.
Instead, I posit that FORTH and associative/applicative languages may be better for transformer architectures.
Concatenate, not integrate. Agree on the stack state.
I set out to question if this could be true.
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