This is quite off topic, but it reminded me of something I have been thinking about recently – perhaps at the limit all highly capable narrow AI systems must become generally intelligent.
I was thinking about the complexity of expression in TTS voice synthesizers recently and it struck me just how difficult a problem that is.
To be as expressive as a human the AI model would need to fully "understand" the context of what is being said. Consider how a phrase like "I hate you" can be said in a loving way between friends sharing a joke at each others expense, vs being said with anger or in sadness.
It got me wondering if all sufficiently complex problems require models to be generally intelligent – at least in the sense that they have deep, nuanced models of the world.
For example, perhaps for a self-driving car to be as "good" as a human it actually needs to generally intelligent in that it needs to understand that it's appropriate to drive differently if it is in an emergency situation vs a leisurely weekend drive through a scenic part of town. When driving through my city after 8PM on the weekend I tend to drive slower and more cautiously because I know drunk people often walk out in front for my car – would a good self-driving car not need to understand these nuances of the world too?
This is interesting because it highlights just how important the element human understanding is in to accurately convey expression in a voice synthesizer. While I'd argue modern voice synthesizers have been more intelligible than this for some time the expressiveness of this machine has probably only been recently been rivalled by state of the art AI models.