While I'm an AI/machine learning practitioner now, my recent Ph.D. work was on computational modeling of the nervous system; namely the cerebellum. The reason I say 2030 is ambitious, is because there are still a lot of unknowns to perform whole brain simulation. To start, we need whole brain connectivity or wiring diagrams at an extremely detailed level. There are some efforts that are part of the BRAIN initiative that are taking a stab at this, but I don't think they're be ready by 2030. Second, you have to understand the physiology of these neurons in order to simulate them. This is incredibly complex and poorly understand. While we understand neuronal physiology in general, there are a great many details that vary by cell type. Additionally, you have to capture neuron morphology, synaptic plasticity, the effect of neuromodulators, ... the list goes on. By capture, I mean understand them well enough to describe them mathematically so that they can be simulated computationally.
Until then, traditional machine learning and artificial neural networks will be increasingly useful and interesting.