The key element for those kinds of actions. They are morphing neural networks.
The new era of nanotechnology is here. Non-centralized computing. Makes it possible to create drone swarms that have theoretically unlimited computing capacity. Those systems behave like brains. Their computing mimics the brain structures; one drone. It is like one brain cell. The system operates as an entirety, or as a morphing neural network. If the main system wants to give missions to a certain drone. Or drone group. The system just acts like an LLM, a large language model, that separates the SLM, a small language model, for the special duties.
The morphing neural network allows the use of less powerful processors for multitasking and complex operations. The less powerful processors can be used in smaller systems. So, like in human brains. In a morphing neural network. Single. Or individual actors can be weak. But together they are strong. The single processor must not be powerful. In neural networks, a single processor can call other processors. To help it. In the world of complex networks, the thing that can look like a single computer can be. The large neural network. The number of processors determines the network’s capacity.
"The robot has a complete onboard computer, which allows it to receive and follow instructions autonomously. Credit: Miskin Lab, Penn Engineering; Blaauw Lab, University of Michigan" (ScitechDaily, Microscopic Robots That Swim Think and Act on Their Own)
"A microrobot on a US penny, showing scale. Credit: Michael Simari, University of Michigan" (ScitechDaily, Microscopic Robots That Swim Think and Act on Their Own)
The system takes a group of those drones. And then downloads for those drones. Drones can act just like a regular neural network. The difference is that. Those drones move. And that allows the system to create a moving brain. This kind of architecture. Makes it possible to increase. The number of members in the network. There is no limit on the size and capacity of the morphing neural networks.
This means a morphing neural network can call more members to participate in its process. If the process is too difficult or heavy for it. And when the process is ready, it can release other networks to their own duties. The architecture of a neural network. Involve local and global architectures. The local architectures mean system architecture. In a certain part of the network. In morphing networks, the system can call other systems. To help it. Global architecture means. The architecture that is used in all networks, which can call on each other’s assistance.
All networks can have the input and mirror-input abilities. Those abilities mimic the human brain. The computing in human brains requires transmitting neurons and receiving neurons. When an impulse. Goes to the brain shell, where the mirror neuron sends the message that the information is in its goal. The system requires three layers, and the third layer's mission is to remove loops from the system. The system can have three horizontal actors. Those things mimic the cerebrum and cerebellum.
If the main parts of the system make different solutions, those systems can ask. The third party. To desire which solution is better. The system's self-learning means. The system. It can mix sensory data with the memory data. If the solution is right. The system uses it as a matrix for other cases. When the system detects something, it searches for similarities in its memory. And if there is similarity, the system uses the solution that was generated. For those types of cases.
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