The announcement of the United States Department of Energy (DOE) on Tuesday, December 13, 2022 was a monumental milestone in nuclear Fusion research, where a “net energy gain driven by Inertia Fusion Confinement (ICF)” eventually, after almost 40 years of scientific efforts achieved for the first time in history by scientists from the Lawrence Livermore National Laboratory (LLNL) in California through its National Ignition Factify (NIF) program. For a long time since aftermath of “Manhattan Project”, scientists knew and had some clear concept of bringing sun energy to earth by means of “Fusion” process either via Magnetic Fusion Confinement (MFC)or ICF after their success of exploding their nuclear fission bomb through Nevada Test Site (NTS) via “Fission” process. Announcement of such an achievement by simply put it is one of the most impressive scientific feats of the 21st century as “Jennifer Granholm, secretary of U.S. Department of Energy said at a press conference. Such success puts production of electricity from nuclear energy of fusion puts it in different perspectives and it shows that researchers have been working on this for decades alongside of MCF and their efforts behind nuclear fission of Generation - IV (GEN-IV) reactors technology and manufacturing. In this short review paper, we describe all these issues of the differences between Fission and Fusion and each aspect of them being source of generating electricity to meet national and global demand for the source of it from nuclear point of view and not going to detail of renewable source of energy at this point.

The use of Artificial Intelligence (AI) in nuclear in-core instrumentation and control has the potential to improve the safety and efficiency of nuclear power plants. In-core instrumentation refers to the sensors and other measurement devices that are used to monitor the conditions inside the nuclear reactor core, such as temperature, pressure, and neutron flux. These measurements are used to control the operation of the reactor and ensure that it is operating within safe limits from Probabilistic Risk Assessment (PRA) point of view. One way that AI can be used in nuclear in-core and out-core Instrumentation and Control (I&C) as well as Instrumentation and Measurement (I&M) is by analyzing the data from these sensors in real-time and using machine learning algorithms to identify patterns and trends. This can help operators to detect potential problems or anomalies before they become critical, allowing them to take proactive measures to prevent accidents or malfunctions. AI can also be used to optimize the operation of the reactor by analyzing data from past operations and using this information to develop more efficient control strategies. For example, AI algorithms could be used to identify the most effective combination of control parameters to maintain a stable and safe reactor operation, while minimizing the use of fuel and other resources. Overall, the use of AI in nuclear In-core instrumentation and control has the potential to improve the safety, efficiency, and reliability of nuclear power plants. As AI technology continues to advance, we can expect to see more widespread adoption of these technologies in the nuclear industry

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