| One problem is that academic CS-researcher intelligence is completely different to average human intelligence. Maybe 5% of the population can learn how to solve partial differential equations. Virtually all of the population can manage extended family-related conversations over Christmas. Even when drunk. Human intelligence is mostly social, and mostly not scientific. The average human is incredibly bad at model building and self-correcting prediction. What actually happens is that humans have developed a kind of collective cultural exoskeleton which protects - more or less - from the consequences of poor choices. But it doesn't take much for that to stop working. Covid denial and climate change denial are just two examples. The cost if living in this space is having to learn a lot of heavily scripted cues. There's a long list of acceptable and unacceptable behaviours and social registers in different social situations. It varies by culture. But generally humans can navigate this space without thinking too hard about it. Academic intelligence is completely different. There's long been a joke that an AI researcher's ideal intelligent system is another AI researcher, with typical AI researcher interests - math, puzzles, abstract language models, music in an engineering way, and so on. Current LLMs are the first cross-over product which shows signs of moving into the first space from the second. You can imagine a future system which uses facial and gait profiling to read emotions, and links a tokenised language model with a tokenised model of various transitions through emotional and social states. Personal background will be missing, and that's not hard to invent. And now you have something that mimics a large part of social intelligence. Only it has the potential to do it better than humans do. |