A self-learning system refers to an AI system that can learn and improve its performance without manually labeled data, relying on its interactions with the environment or learning from its own past computations and operations.


Imagine you’re playing a game and you’re trying to get better at it. At first, you may not know the rules or concepts, but as you play more and more, you understand what works and start using better strategies to win. A self-learning system does similar; it learns from its own experiences or feedback about its actions and improves over time.

In-depth explanation

A self-learning system, in the context of AI and machine learning, is one that uses algorithms and techniques to self-improve without the constant need for human intervention or labeled data. Often the learning process involves interactions with its environment, forming a cycle of perception-action-learning.

High-level abstraction of the self-learning process can be seen as experiencing or performing an action, getting a feedback (which could be a reward or punishment-like signal), and then tuning itself based on feedback to increase the occurrence of rewarding actions in future.

For instance, reinforcement learning is a type of self-learning system. Starting from a very initial state of knowledge, such systems interact with their environment and learn from a reward signal generated by the results of their actions. They aim to maximize the cumulative rewards over time. This learning technique has been applied in various domains, from playing games like Go or chess to autonomous cars and robotic control.

Another self-learning approach is called unsupervised learning, which reduces the reliance on labeled data. These systems can identify patterns and data distributions by themselves and form clusters or reduce dimensions, which can then be leveraged in different tasks.

An important aspect of self-learning systems is their adaptability. If the environment changes, self-learning systems have the ability to accommodate changes, thanks to their continual learning methods. This continuous learning can lead them to evolve their knowledge or policies perpetually as long as they’re operating and interacting with their environments.

A certain level of caution is however required when relying on self-learning systems as they can also learn undesirable behaviours if not properly guided or constrained, due to their reliance on experience or reward signals for learning. Thus, ensuring their alignment with human values or desired objectives is a vital aspect of their deployment.

Machine Learning, Reinforcement Learning (RL),, Unsupervised Learning, Algorithm, Continual Learning, Adaptability, Learning from Demonstration, Semi-supervised learning.