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Deep stubborn networks are networking models that support the evolution of artificial intelligence in key ways. In these networks, different network components work against themselves to produce more refined results. Deep stubborn networks have been hailed as an important innovation in machine learning.
The idea of the deep stubborn networks is based on the idea of generative adversarial networks. These generative adversarial networks include two components: a generator and a discriminatory engine. The generator tries to fool the discriminatory engine as it chooses between legitimate and synthetic results.
What deep stubborn networks add, according to experts, is the idea of extending variable modeling. One way the experts describe this is that the program generates so many choices that the machine ultimately declines to choose a particular result. The system then has to be “coaxed” by either a human or an additional algorithm in order to produce a result. Some characterize this type of complex AI as a move toward self-awareness, saying that the network “refuses” to give a response absent some criteria.
It is important to note that deep stubborn networks are still in their infancy. However, the idea does play an important role in the development of artificial intelligence. If machines can be made, through machine learning principles, to “doubt themselves” and only produce results with a given rate of confidence, this does lead toward future technologies that provide a more detailed simulation of the human mind and consciousness.