Well, yes, that's where some of the hardness comes from... but how do you build a predictive model capable of transfer learning without disentangling the latent factors? How do you perform one-shot learning without being able to lay down discrete episodic memories?
I'd say that rather than a continuous analog data stream needing an analog model, the real problem is that the causality (hence predictability - the goal) of this data stream is due to discrete actors and actions and therefore we need to discretize the stream into objects and spatio-temporal events.
Anyhow, we're making great strides with ANNs on the perceptual side to the point where it's almost a solved problem... What's lacking (outside of DeepMind) is more of a focus on intelligent embedded agents, complete with lifetime continuous learning, and adaptive behavior. IMO we're focusing too much on artificial isolated problems rather than the embedded systems/agents that are the real goal!
Just as ImageNet - and human competitiveness - drove vision research, what could accelerate AI research would be a similar annual competition for embedded agents (either in a simulated environment or maybe robots in a competition space), which would at least focus efforts on building systems and addressing the goal of AI rather than breaking it down into someone's (maybe incorrect) notions of the piece-parts necessary to get there.
Some people shy away from robotics as an unwelcome added complexity, but that never stopped the popular micromouse competitions, and these sorts of competition could go a very long way with simple robots/vehicles (e.g. based on Lego mindstorms or R/C vehicles) with remote compute.