LLMs are just one branch of a much wider field of AI research, and analog circuits that deal with flows instead of discrete values tend to be more efficient for modelling dynamic systems such as traffic flows.
Honestly, the article is crap. It doesn’t mention any of this. Thanks for linking it.
I remember reading about analog neural circuits somewhere but wasn’t aware this was what the author was referring to. He just went around in circles describing how they were used.
I thought it was interesting conceptually even if light on details. In the west, most application for AI has been in the realm for content generation like making images, documents, writing code. Meanwhile, in China AI systems are used for stuff like monitoring traffic systems, maintaining high speed rail networks, and other types of dynamic systems management. I haven’t really heard much about AI being applied in this way in western countries, and it seems like a far more practical use to me.
Not really, China has been experimenting with a number of different approaches using analog neural circuits
LLMs are just one branch of a much wider field of AI research, and analog circuits that deal with flows instead of discrete values tend to be more efficient for modelling dynamic systems such as traffic flows.
Honestly, the article is crap. It doesn’t mention any of this. Thanks for linking it.
I remember reading about analog neural circuits somewhere but wasn’t aware this was what the author was referring to. He just went around in circles describing how they were used.
I thought it was interesting conceptually even if light on details. In the west, most application for AI has been in the realm for content generation like making images, documents, writing code. Meanwhile, in China AI systems are used for stuff like monitoring traffic systems, maintaining high speed rail networks, and other types of dynamic systems management. I haven’t really heard much about AI being applied in this way in western countries, and it seems like a far more practical use to me.