For the foreseeable future there will need to be ground units in which case miniaturized drones optimized for collecting only image and sound data and aggregating that data will prove useful. The aggregation techniques would be AI based in nature. Here is a excerpt from "Neural and Adaptive Systems" that illustrates this point and ties it into the broader point of the importance of information and math moving forward:FishbellykanakaDude wrote: I suppose you could call that "AI", but that entire scenario could easily be conducted by remotely controlled drones (flying and "walking").
But I'm certainly not opposed to an AI that could DO that, as it's easily contained to "a mission".
Aloha nui ko'u hoaloha! <shaka!>
Engineering is a discipline that builds physical systems from human dreams, reinventing the physical world around us. In this respect it transcends physics, which has the passive role of explaining the world, and also mathematics, which stops at the edge of physical reality. Engineering design is like a gigantic Lego construction, where each piece is a subsystem grounded in its physical or mathematical principles. The role of the engineer is to first develop the blueprint of the dream through specifications and then to look for the pieces that fit the blueprint. Obviously, the pieces cannot be put together at random, since each has its own principles, so it is mandatory that the scientist or the engineer first learn the principles attached to each piece and then specify the interface. Normally, this study is done using the scientific method. When the system is physical, we use the principles of physics, and when it is software, we use the principles of mathematics.
The scientific method has been highly successful in engineering, but let us evaluate it in broad terms. First, engineering design requires the availability of a model for each subsystem. Second, when the number of pieces increases the interactions among the subsystems increase exponentially. Fundamental research will continue to provide a steady flux of new physical and mathematical principles (provided the present trend of reduced federal funding for fundamental science is reversed), but the exponential growth of interactions required for larger and more sophisticated systems is harder to control. In fact, at this point in time, we simply do not have a clear vision of how to handle complexity in the long term. But there are two more factors that present major challenges: the autonomous interaction of systems with the environment and the optimality of the design. We will
discuss these now.