The machine learns to respond to input in a specific way. This could take the form of learning to make predictions or recognize patterns. It can also involve self-improvement in the machine's response, such as:
Self-reference self-improvement
Machine responses can incorporate self-improvement by employing a self-learning process in which machines analyze their own performance, and self-improvement by adjusting model parameters to influence how they respond to different inputs. These parameters include those responsible for the improvement process itself. Improved models of results using modified parameters can be further improved in a recursive way.
Meta-learning
Furthermore, machine responses can take the form of a learning process that allows new tasks to be performed efficiently by responding appropriately to queries. The input of the model representing this new task can prompt the machine to learn another new task that could lead to an open-ended process.
Conclusion. The goal of all machine learning algorithms is to ensure that machines respond appropriately to specific inputs. This learning process employed in these algorithms is used in a variety of applications, such as chatbots and medical image analysis, in addition to allowing machines to self-improve and learn.
sauce:
“Artificial Intelligence: 10 Things You Need to Know,” Tim Locktashell, 2024.
https://www.ibm.com/topics/neural-networks
https://www.ibm.com/think/topics/meta-learning
“Modern Self-Reference Weight Matrix Learning to Modify Yourself,” and Kazuki Illy et al. Proceedings of the 39th International Conference on Machine Learning in Baltimore, Maryland, USA, PMLR 162, 2022. Copyright2022
