What is Continuous Learning?
It is the process of development of an artificial intelligence system by constantly updating its information as it encounters new data, without forgetting it.
Overview
Continuous learning ensures that artificial intelligence does not remain static and keeps up with changes in the world. Normally a model is trained and stays that way, but continuously learning models improve their performance by learning from new experiences. This allows the system to become smarter and more adaptable over time.
How it works
The system updates its model at regular intervals or instantly by blending new incoming data with old information.
Where it is used
It is used in recommendation systems, financial forecasting tools, and robots operating in dynamic environments.
Commonly confused with
Can be mixed with fine-tuning; fine-tuning is customization for a specific task, while continuous learning aims for overall improvement.
Frequently asked questions
Why doesn't every system constantly learn?
Because while learning new information, there is a risk of forgetting old information (catastrophic forgetting) and it is technically quite difficult.
What is required for this process?
An uninterrupted data flow and a strong infrastructure that can process this data are required.
Related terms
Related tools
This explanation was written in plain language for TreScout and machine-translated from the Turkish original · the Turkish version prevails. If something looks wrong or missing, write to hello@trescout.com. Read in Turkish →