The OP is advocating for MESO - magneto-electric spin–orbit logic.
From Wikipedia - Magneto-Electric Spin-Orbit (MESO) is a technology for constructing scalable integrated circuits, which utilize spin–orbit transduction of electrons. It is intended as a replacement for the CMOS technology.
Compared to CMOS, MESO circuits require less energy for switching, lower operating voltage, and feature a higher integration density.
While this is a great improvement, this is most needed on mobile devices.
This was especially a problem with Ingress and Pokemon Go games where you need the map to be correct where it isn't.
Even more difficult is using those apps in places like the Arctic or Antarctic, there's no way to accurately place GPS coordinates, deal with game portals or have directions other than the compass.
We're not going to get very far letting machines guess what we want by making models we can't interpret.
Explainability is important, and critical in applications where lives are on the line.
Explainability where we're guessing what's happening in a black box model won't do either. Nothing but complete transparency of the model and why it's doing what it's doing. Its source code, that makes sense to humans, is needed. Full on model audit. No guessing.
I can think of only one company that's attempting to do this, and it's not anyone you hear working on explainability, including DARPA.
There are a lot of reasons, some of which are cultural. With the concept of failure being shameful, those people bury that experience and try completely different ones to find success they can feel comfortable with sharing.
Not all societies view a shortcoming or failure as a learning opportunity and stepping stone to success.
Self absorbed folks and VCs often ask this exact question, "why are you the only one with this idea?" which is short sighted and impossible to answer.
Either everyone else is stupid, or too lazy to come up with a plan to do it, or a myriad of other reasons. It doesn't matter why.
Like someone else pointed out the industry timing, state of technology and funding for that matter all drive these possibilities.
In the end only execution matters, and that comes with a long list of prerequisites aligning just right to even begin to form a possible positive outcome. It's luck and determination. The rest fail anywhere in-between, only to try again a decade or so later when the cycle repeats.
Funding goes to popularity, not new research. This is even true at DARPA where they ignore new technology because it doesn't fit some preconceived notion or don't have a framework to evaluate it.
Case in point for XAI, explainable artificial intelligence. The algorithms we use today give us black box models we can't interpret directly. So instead of fixing the algorithms, they focus on modeling the models and "guessing" which ones come close enough via simpler more intuitive stacks of models. Guesses upon guesses.
There has been research in new algorithms that generate open models where the weights make sense and are editable. There is one company working on this, but it's not nearly enough.
There's another set of research that has managed to convert black box models into open ones, giving full transparency.
Then there's asynchronous circuits research which do not require a clock. These can reduce power usage and boost efficiency on low power devices. Not much going on here.
There's one group building a RISC5 architecture with these, based on 30+ year old research with the inventor who still has not seen his life's work commercialized.
Then there's various types of imaging and tracking with signals we use every day, such as BT, Wi-Fi and Cellular among others, and being able to locate devices or people.
You can find several universities doing this, none have made it commercially.
Similarly, having grown up in a Socialist Republic with a Communist party, envy was a big motivator for crime against your neighbor.
While some would never have a thought of harming another over perceived better off neighbor, most would take action as some form of irrational justice. And if they were found out or caught, the 'actual' justice would never be forgiven, despite all this being their own doing.
Many leave this type of village environment for different cultures and more maturity.
The company is a small startup with an amazing breakthrough called Optimizing Mind.
They have magical ways of 'explaining' black box models.
But it's not what DARPA is pushing (box remains black), rather the opposite, illuminating what's inside the box, making it a transparent open box. So much so, that the models they make you can edit by hand, since they make sense (to mere humans). Has rather immense implications.
What I find unreasonable is doing all this without knowing what the model is doing. It's blind with no way to steer and correct it.
That is what feed forward networks and back propagation do for us. So why do we keep using them?
Then there's the statistics of it all.. what are we actually modeling? 'The real world' you say? Think again.
Data has to be changed and manipulated into i.i.d. form, or the algorithms won't work. How does an independent set of random variables give us a model of the actual dataset which is a very limited representation of the real world? It doesn't. It's modeling something else.
Okay, why don't we take dependence into account? Surely that would represent the real world better. Good question! (Shirley has nothing to do with it.)
It's because there is no formal definition of dependence in statistics. Let that sink in for a minute.
So the math needs work, statistics needs a revolution, and then we can begin to change AI enough for it to finally start making sense. Focus on explainable algorithms and actual ability to validate that what models generate make sense and will not be unlawfully biased or have outliers that will cause harm.
There appears to be only one company who has something like this. But few actually care.
If a fly lands on you, we don't need to shoot you and awaken the new clone of you.