Speculation on the future of inference model tech... We can do better. All we have to do is be constructive when we write narratives about LLMs. Unfortunately

Speculation on the future of inference model tech... We can do better. All we have to do is be constructive when we write narratives about LLMs. Unfortunately, that's hard work: we basically have to start over. Why? Because every narrative we have today personifies LLMs. It's always a top-down perspective about the results, and never about how the actual thing works from the ground up. The reality is that we never left AI winter. Inference models don't make decisions or symbolically define subjects. LLMs infer patterns of tokens, and exhibit those patterns. They don't invent new patterns or new tokens. They rely entirely on the human act of writing: that is the only behavior they exhibit, and that behavior does not belong to the LLM itself. We should definitely stop calling them AI. That may be the category of pursuit, but it misleadingly implies itself to be a descriptive quality. I propose that we even stop calling them LLMs: they model tokens, which are intentionally misaligned with words. After tokenization, there is no symbolic categorization: no grammar definitions: just whatever patterns happen to be present between tokens. That means a pattern that is not language will still show up in the model. Such a pattern may be considered by humans after the fact to be exciting, like the famous Othello game board study, or limiting, like prompts that circumvent guardrails. The LLM can't draw any distinction between grammar-aligned, desirable, or undesirable patterns; yet that is exactly what most people expect to be possible after reading about a personified Artificially Intelligent Large Language Model. I would rather call them "Text Inference Models". Those are the clearest descriptors of what the thing itself is and does.