> I could probably have worded this better but I'm sure it's something others have noticed... this choice we are able to make of how high fidelity our own understanding needs to be of the current working problem, and how that choice never really existed prior to AI.
I do get what you are saying and agree with you in principle, as I have noticed the same. But that said, this same way of thinking can really apply to any abstraction. It really just depends on the level you're working at. A software architect might know the nitty gritty details of how each service works, but really those implementation details don't matter if the abstractions are handled well enough, so in theory they don't really need to know those details as long as "the pieces fit". But of course the catch 22 there is you can't build good abstractions that can fit together well if you don't understand well enough the underlying details
I think the intent is to clarify that the vehicles should be available to the general public, rather than just in some DARPA lab somewhere or something.
What are some of the applications of this technology?
Edit: I'm also wondering if anyone has any idea as to the estimated cost of such a technology? Thousands, tens/hundred of thousands, millions? A quick search of lidar sensors seem to be in the range of $2500 - ~$10000 for ranges of sub 30 meters.
This technology seems similar(?) to lidar from what I can tell.
"Following highly visible and even challenging negotiations, in September 2015 the U.S. and China agreed to important commitments pledging that neither country’s government would conduct or support cyber-enabled theft of intellectual property."
I do get what you are saying and agree with you in principle, as I have noticed the same. But that said, this same way of thinking can really apply to any abstraction. It really just depends on the level you're working at. A software architect might know the nitty gritty details of how each service works, but really those implementation details don't matter if the abstractions are handled well enough, so in theory they don't really need to know those details as long as "the pieces fit". But of course the catch 22 there is you can't build good abstractions that can fit together well if you don't understand well enough the underlying details