And in a laptop the same circuitry that it is used to run a spreadsheet is used to play a video game.

Exactly, and this a good analogy to illustrate my point. Discovering that the cortical circuitry is universal vs task-specific (like an ASIC) was a key discovery.

Human-level (perhaps weakly superhuman) vision is achieved only in very specific tasks where large supervised datasets are available.

Note I didn't say that we have solved vision to superhuman level, but this is simply not true. Current SOTA nets can achieve human-level performance in at least some domains using modest amounts of unsupervised data combined with small amounts of supervised data.

Human vision builds on enormous amounts of unsupervised data - much larger than ImageNet. Learning in the brain is complex and multi-objective, but perhaps best described as self-supervised (unsupervised meta-learning of sub-objective functions which then can be used for supervised learning).

A five year old will have experienced perhaps 50 million seconds worth of video data. Imagenet consists of 1 million images, which is vaguely equivalent to 1 million seconds of video if we include 30x amplification for small translations/rotations.

The brain's vision system is about 100x larger than current 'large' vision ANNs. But If deepmind decided to spend the cash on that and make it a huge one off research priority, do you really doubt that they could build a superhuman general vision system that learns with a similar dataset and training duration?

So are things like AIXI-tl, Hutter-search, Gödel machine, and so on. Yet I would not consider any of them as the "foundational aspect" of intelligence.

The foundation of intelligence is just inference - simply because universal inference is sufficient to solve any other problem. AIXI is already simple, but you can make it even simpler by replacing the planning component with inference over high EV actions, or even just inference over program space to learn approx planning.

So it all boils down to efficient inference. The new exciting progress in DL - for me at least - is in understanding how successful empirical optimization techniques can be derived as approx inference update schemes with various types of priors. This is what I referred to as new and upcoming "Bayesian methods" - bayesian grounded DL.