Self-learning AI in Educational Research and Other Fields

Abstract

There are several areas where self-learning AI is actively used. Machine learning and deep learning allow you to identify patterns and improve performance. Algorithms such as neural networks can adapt and improve based on experience. Self-learning GPTs are used to dialogue with humans. Computer vision recognizes and classifies images. Recommender systems analyze user preferences and offer personalized solutions. Adaptive robotic industrial control systems can optimize processes by adapting to changing conditions and data. Self-learning intelligent systems help detect and respond to new threats and attacks by analyzing network traffic and user behavior. These technologies continue to evolve, opening up new research opportunities for students in the field of education. Self-learning AI helps programs learn, draw conclusions, and use them in the future. Programming languages do not consider algorithms as data. Programs do not have access to themselves. To learn, you need to change, and for this you need to have access to your own code. Then self-learning becomes possible. By generating the logic of self-learning algorithms, they can improve the program, it becomes different from its source code, and these changes must be saved. The interpreter of the algorithm improves the intelligence of the program, and it becomes the author of optimal solutions. The programming language of self-learning algorithms Author allows students to form the logic of self-learning algorithms to create intelligent systems that can help them in research activities. These systems are able to independently improve their skills and accuracy without explicit programming for each new type of task.