A dive into Stafford Beer’s systems thinking that uncovers the ANT-style depth that’s often glossed over. That notwithstanding, I still think the glib and reductive POSIWID has its uses.
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LLMs are fundamentally matching the patterns they’ve seen, and their abilities are constrained by mathematical boundaries. Embedding tricks and chain-of-thought prompting simply extends their ability to do more sophisticated pattern matching. The mathematical results imply that you can always find compositional tasks whose complexity lies beyond a given system’s abilities.
