Self-applying
Describing something that operates, functions, or is effective automatically or without the need for external or manual intervention. It suggests an inherent quality within the subject that allows it to be implemented, executed, or become effective by its own nature. This is often seen in rules, laws, or processes that do not require external enforcement to take effect or in technologies designed to automate actions. The concept highlights self-sufficiency and a lack of dependency on outside actors or systems for functionality.
Self-applying meaning with examples
- The new software update featured a self-applying patch system. As soon as the program detected a compatibility issue, the relevant correction was automatically downloaded and installed, eliminating the need for user intervention. This contrasted sharply with older versions that necessitated manual downloads and complicated installation instructions, simplifying the update process and saving valuable user time and effort.
- In complex legal frameworks, a self-applying clause ensures that the stated consequences or actions begin automatically upon the defined trigger event without requiring further legal action. For instance, in a contract, a penalty for late payment can be a self-applying clause meaning that when a payment deadline is missed, penalties are automatically added to the total amount owed.
- Certain types of philosophical concepts are often described as self-applying, because they generate their own justification or explanation for their continued validity. An example would be an ethical principle that derives its own legitimacy through the very act of following and enforcing the principle itself, reinforcing the idea of self-governance and the internal consistency of such a system.
- Consider a type of AI whose learning system exhibits a self-applying learning technique. The program, by recognizing failures, will then refine its processes and incorporate new data to achieve an improved performance level. This autonomous ability minimizes the need for a programmer's manual intervention and adjustment to the AI system over time, allowing it to dynamically adapt.