You’re incorrect now. Firstly, if and when I say that an LLM “thought,” understand thats shorthand for “semantic and contextual processing,” not as a literal claim that the LLM consciously thought in the human sense. It’s a very novel kind of data processing, uniquely positioned to intake and output semantic context in natural language format. I’m not going to sit here and act like that’s the same shit computers have been doing for decades, because it’s not, meanwhile that also doesn’t mean I’ve ceded to any anthropological bullshit.
Secondly, you’re confusing autonomy with autonomous. Autonomous as in “automation.” Not autonomous as is, “I have autonomy.” I’ve never heard autonomous used in that manner, so forgive my initial misunderstanding on the confusion. I have cron jobs that qualify as autonomous and I certainly am not arguing that my cron jobs are sentient. Your toilet bowl water can settle down.
Thirdly, you claim that the outputs are wholly predictable? In what sense? In the hindsight biased “gee, anyone could have foreseen that one” — sure, predictable. I said as much in my prior comments. OpenAI leadership isn’t respecting the risks and debts with their endeavors. Yet you seem to be correlating LLM output with traditional functional output. That’s inherently incorrect.
LLMs are not conventional deterministic programs where a human specifies a transformation and can generally reason from the code to the exact resulting behavior. The transformation is instead encoded across billions of learned parameters, and the resulting behavior emerges from interactions among those parameters, the prompt, the context, decoding strategy, the model’s learned statistical representation of language and concepts, … it’s virtually indeterministic.
That does not make an LLM magical, unknowable, conscious, or any such thing. Given the complete machine state and a deterministic inference configuration, there is still a physical computation occurring according to defined rules (like you allude to). But “deterministic in principle” and “predictable in practice” are not remotely synonymous. We routinely cannot determine in advance, at a useful semantic level, exactly what a sufficiently complex model will produce without effectively running the computation itself. That alone demonstrates the difference.
I feel like our debate has shifted targets, though. It was about whether or not OpenAI allowed this to happen — using “autonomous” as a anthropomorphic scapegoat to solve for negative press. Yes, they probably are trying to frame the narrative. However, that doesn’t mean the technology didn’t actually do something interesting. Regardless, OpenAI should still be held accountable.