From what I understand (layman here), general AI research is roughly divided in two school of thoughts. One school believes GAI will be achieved by incrementally working on the different components (vision, language, learning, etc.) separately and eventually gluing those components together. The other school believes we should try to understand the human brain at a biological level and simulate it. The latter school believes general intelligence will emerge from a relatively simple algorithm and we should concentrate our efforts into discovering this algorithm. The former school seems to be a lot more popular these days although research is being done on both fronts.
And the third school believes that general intelligence is normatively-correct reasoning in some sufficiently rich (where Turing-complete is a lower bound on sufficiency) domain/class of possible-words/hypotheses.
I (another layman) don't think AI research is divided that way. The most visible area of research is "machine learning", where the goal is not AGI but to find better algorithms to solve well-defined statistical engineering problems. In contrast to that, there is a wide area of research that doesn't focus on well-defined problems. On the philosophical side, it's about understanding what intelligence even is. Or about understanding what tasks the brain is solving, or why we make some mistakes but not others. On the more technical side are the embodied people who focus a lot more on grounding of an agent's perception and on interaction with the environment, rather than thinking about intelligence as a passive task that only involves the brain.
