I think I can confirm Julia is used in all those 7 branches of AI (note each has sub-fields, AI is an enormously big area of study, and Julia would certainly be useful for any possible AI area you can think of including e.g. robotics), except for probably expert systems, because outdated:
DSLs evolved into “expert systems”, and although “expert systems” is pretty much an abandoned line of research nowadays, some of the early success in AI came from them (like the first mathematical theorems proved by a machine) and DSLs are alive and kicking
Domain-specific languages (DSLs) are a big part of Julia, and I would say Julia excellent for them, more so than most other languages.
I wasn’t sure about, but at least found:
Is Julia most used for any current AI area (yet)? Probably not, except SciML. At least there is e.g. a book on computer vision:
Natural language processing is where stuff is happening, and such transformer models (that Julia has available) have taken over computer vision too.
One thing not mentioned there is time-series prediction (and analysis) and Julia also good for that, but intriguingly computer vision is also taking over that in the form of transformer models:
https://deeplearning.fr/visionts-revolutionizing-time-series-forecasting-with-image-based-models/
The cutting edge is moving from transformers to e.g. liquid neural networks (they used Python/PyTorch):
There will always be some area where Julia is not yet there, though people are working on this:
https://www.science.org/doi/10.1126/scirobotics.adc8892
I don’t know the difference between liquid neural networks (and Liquid Time-Constant Network) and Liquid State Machines, but I suspect all related and at least I see something in Julia related to that last one:
ReservoirComputing.jl provides an efficient, modular and easy to use implementation of Reservoir Computing models such as Echo State Networks (ESNs). Reservoir Computing (RC) is an umbrella term used to describe a family of models such as ESNs and Liquid State Machines (LSMs)