"Very few people today would have the audacity to explicitly try building human-level AI."

Hmmm, there are a considerable number of people/groups who have this audacity... Have for decades... and they have been explicitly trying with much incremental success.

One thing such people speak about is ridding the space of outlandish buzz words/promotions that mask the true nature of how things function. This 'hype' creates barriers to contribution, learning, and progress.

Furthermore, the difficult efforts have been overshadowed by statistically mapped input/output flow models currently being called "A.I".

There is no mystery w.r.t how deep learning/etc work I.M.O.

You have inputs 'X'.
You take a known solution space 'Y' (supervised learning) or you create an arbitrary one (unsupervised learning) 'Z'.

You break apart the input space and map it to nodes in a graph.
You break apart the output space and map it to nodes in a graph.

Input flows are decomposed into minimal component parts and recomposed into higher orders of correlation. This is then compared (via flow restricted weighting) to increasing orders of the output space.

How does this magical 'piece apart and and piece back together' process work during active flows? It works based on guided encoding of 'importance' weights on the partial information represented by individual nodes in the flow graph network. Thus why under/over fitting can occur if you have too many/too little nodes.

How are the weights codified? By encoding the partial derivative (partial contribution) a node has w.r.t to the accuracy of the solution ... Error function (Desired - Actual). Curve fitting.

It's essentially distributed brute force statistical gradient descent which is why you have to beat on it, tune it, anneal it, and cram hoards of data through for it for it to yield anything of value. "throw enough dirt and it will stick"

Frankly, there is nothing to understand ... NNs/etc are distributed optimizers guided by partial objective information. The resultant network/weights are a spaghetti jumble of 'whatever gets the right output out the other end'.

You're throwing a bunch of 'agents' at a solution space and having them gradually combine their results to a final solution. This was previously known as constraint optimization before it got the silicon valley treatment of buzzwords :

Distributed constraint optimization...
http://mirlab.org/jang/matlab/toolbox/machineLearning/image/...

This is not A.I. I don't feel anyone who has a grain of integrity ever thought it was.

It's very slimmed down version of cortical Algorithms with lots of missing pieces at best.

Strong A.I is being developed far from such thinking and is a totally different animal. People who work in this space are necessarily guarded and un open as there is a lack of appreciation, value, and funding for their 'audacious' efforts.

Of course, once more solid systems are developed, I'm sure you'll hear from them again in the form of 'blackbox' capability presentations.

Currently, the spotlight and money are being thrown at PHDs and names as no one has a clear understanding of what they're looking for. Namely because no one wants to spend the time/money on defining that. People are moreso interested in getting products/results out the door.

".... lets get the best minds, throw them in a room, throw money at them and hopefully a solution will come about"
Seems very similar to distributed brute forcing of a problem space with a made up objective function...."throw enough dirt and it will stick"

Most of the time should be spent on defining what were after ...
The method is : https://en.wikipedia.org/wiki/Philosophy_of_science not cramming mathematical formulas and PHDs into white-papers.

There are a lack of generalist being brought into these A.I labs and efforts as they are perceived to have little value. Yet, its the 'generality' and 'fuzzy' stuff that underlies our very intelligence. From general to specific or specific to general...

So, the industry wants to brute force this w/ money/PHDs/Buzzwords/industry names...

The more complex and disjoint a problem space is, the harder it becomes to brute force....

Time will tell. All roads eventually lead to Rome.
Though, some will take considerably longer.