What is the Deep Stubborn Network (StubNet)?
Deep Stubborn Network (StubNet) are network models that support the development of artificial intelligence in key areas. In these networks, various network components work against themselves to achieve better results. Deep sturgeon networks have been hailed as a major innovation in machine learning.
The idea of deep stubborn networks is based on the idea of generative adversarial networks. These generative adversarial networks have two components: a generator and a discriminatory machine. The generator tries to trick the discriminating engine into choosing between legitimate and synthetic results.
According to experts, one of the deeply rooted networks is the idea of expanding variable modeling. One possibility that the experts describe is that the program generates so many choices that the machine ultimately does not choose a particular result.
The system then has to be “persuaded” either by a human algorithm or by an additional algorithm in order to produce a result. Some characterize this type of complex AI as a move towards self-awareness, saying that the network "refuses" to give an answer if some criteria are missing.
It is important to note that deep persistent networks are still in their infancy. However, the idea plays an important role in the development of artificial intelligence. If machines can be made to "doubt themselves" through principles of machine learning and only produce results with a certain confidence rate, this will lead to future technologies that will enable a more detailed simulation of the human mind and consciousness.