Comment by tomrod
1 day ago
It's a fun rabbit hole.
Classical ML tasks (e.g. classification, regression ), perception (vision, speech) and pattern recognition, generative AI capabilities (text, image, audio generation), knowledge representation and reasoning (symbolic AI, logic), decision-making and planning (including reinforcement learning for sequential decisions), as well as hybrid approaches (e.g. neuro-symbolic methods, fuzzy logic).
The capability areas outside of classical ML have been overlapped now to a degree by GPT architectures as well as deep learning, but these architectures aren't the whole game.
Yea, I think it's one of those things that I won't understand from the outside looking in. I'm in semiconductor software so I do a lot of classical numerical methods, graph theory, and ML research, like converting obscure ML algorithms heavy on math from academia for our ML teams. I don't think I'll get the technical side of what is now called ai without OJT in it.