> Likewise, as impressive as stable diffusion is, I doubt it could even get my car out of the driveway. Even this analogy oversells the state of "AI" today, as

> Likewise, as impressive as stable diffusion is, I doubt it could even get my car out of the driveway. Even this analogy oversells the state of "AI" today, as it implies that it would be possible to simply "swap in" stable-diffusion for the self-driving software in your Tesla (or whatever car with similar software) without spending months figuring out how to even hook up the inputs and outputs in such a way that you could get stable-diffusion to somehow activate when you want to drive. Their IO models are so completely different—one takes a stream of largely-visual input and determines in real time whether it represents something it has been trained to recognize as "road", "danger", etc, and thus whether it should issue real-time outputs to the car's axles, brakes, etc, while the other takes discrete text or visual input prompts and uses them to generate discrete visual outputs—that it doesn't even make sense to think about how one could replace the other. They're not AIs. They're simply software programs, using machine learning algorithms to produce highly-tailored, human-opaque results that can, yes, be pretty amazing. But given all the excessive hype and disinformation lately around what these programs can do, I've made a conscious effort not to refer to them as "AI," because people have too many sci-fi associations with it, and it causes far too many of them to anthropomorphize them and impute far, far more ability and "intelligence" to them than they actually have.