The Realization of Zero Touch

zero touch
zero touch

“The zero-touch networks of the future will be characterized by the fact that they require no human intervention other than high-level declarative and implementation-independent intents. On the road to zero touch both humans and machines will learn from their interactions. This will build trust and enable the machines to adjust to human intention.”

A zero-touch network is capable of self-management and is controlled by business intents. Data-driven control logic makes it possible to design the system without the need for human configuration, as well as to provide a higher degree of information granularity. Applying AI technologies will enable zero-touch automation of network life-cycle management, including optimizing system performance, predicting upcoming faults and enabling preventive actions. The performance of a data-driven zero-touch function can increase by utilizing the wider network data from many local clients, but this needs to be balanced against the cost and time associated with transferring large volumes of data.

The realization of zero touch is an iterative process in which machines and humans collaborate reciprocally. Machines build intelligence through continuous learning and humans are assisted by machines in their decision-making processes. In this collaboration, the machines gather knowledge from humans and the environment in order to build models of the reality. Structured knowledge is created from unstructured data with the support of semantic web technologies, such as ontologies. The models are created and evolved with new knowledge to make informed predictions and enhance automated decision making.

To maximize human trust and improve decision quality, there is a need for transparency in the machine-driven decision-making process. It is possible to gain insights into a machine’s decision process by analyzing its internal model and determining how that model supported particular decisions. This serves as a basis for generating explanations that humans can understand. Humans can also evaluate decisions and provide feedback to the machine to further improve the learning process. The interaction between humans and machines occurs using natural language processing as well as syntactical and semantic analysis.

Dr. Asmita Yadav , Associate Professor, Deptt of CSE