CONTINUAL LEARNING
How Intelligent Systems Learn From New Experiences Without Forgetting What They Already Know
Artificial intelligence can learn-but what happens when an intelligent system encounters something new? Can it add new knowledge without losing what it learned before?
Continual Learning: How Intelligent Systems Learn From New Experiences Without Forgetting What They Already Know introduces readers to one of the most important challenges in modern artificial intelligence: creating systems that can continue learning over time while preserving useful knowledge from the past.
Traditional AI systems are often trained on a fixed collection of data and then deployed. But the real world does not remain fixed. New information appears, environments change, unexpected situations arise, and intelligent systems may need to adjust to conditions that were never included in their original training. Continual learning explores how AI systems can adapt to these changes without having to begin their learning process from the beginning every time something new occurs.
At the center of this challenge is catastrophic forgetting-a problem that can occur when an AI system learns new information but, in the process, loses some of its ability to perform tasks it previously learned. This book explains why forgetting happens and introduces the broader challenge of balancing two important abilities: stability, the ability to preserve valuable existing knowledge, and plasticity, the ability to change and learn from new experiences.
Using accessible explanations, examples, scenarios, and learning activities, readers explore how intelligent systems can manage old and new knowledge. The book introduces major concepts associated with continual and incremental learning, including memory and replay, knowledge preservation, changing data, sequential learning, task adaptation, knowledge transfer, model updating, and strategies designed to reduce forgetting.
Readers are encouraged to think beyond algorithms and consider what continual learning could mean in real-world environments. An intelligent system operating for months or years may encounter new people, new information, changing conditions, new responsibilities, and situations its designers did not anticipate. How should that system determine what information should be retained? What should be updated? When should previous knowledge be reconsidered? And how can humans evaluate whether an adaptive system is becoming more capable without losing important abilities along the way?
The book also connects continual learning to the larger development of advanced artificial intelligence. Systems that operate in dynamic environments may need more than the ability to perform a single trained task. They may need to learn from experience, adapt responsibly, use previous knowledge when facing new situations, and continue developing without repeatedly starting over.
Designed with students, educators, families, and emerging AI learners in mind, Continual Learning makes a complex area of artificial intelligence easier to understand without requiring an advanced technical background. Educational features throughout the book reinforce comprehension, critical thinking, discussion, and practical application.
Rather than presenting AI as a system that simply "knows" information, this book encourages readers to see intelligence as an ongoing process involving learning, remembering, adapting, evaluating, and applying knowledge.
Continual Learning gives readers a foundation for understanding an important question shaping the future of artificial intelligence:
How can we build intelligent systems that keep learning from tomorrow without forgetting what they learned yesterday?
