TRANSFER LEARNING

Transfer Learning: How AGI Applies Knowledge Across Different Subjects and New Challenges introduces students, educators, and families to an important concept in artificial intelligence: the ability to use knowledge learned in one situation to help solve problems in another.

Humans transfer knowledge naturally. We use lessons from previous experiences when we encounter new subjects, environments, and challenges. Artificial intelligence researchers use transfer learning to help AI systems reuse useful knowledge rather than always learning every new task entirely from the beginning.

Written in clear and accessible language, this book explains what transfer learning is, why it matters, and how it can help intelligent systems become more flexible and efficient. Readers explore how AI can recognize similarities across tasks, transfer useful knowledge, learn new skills with less data, and apply previous learning to new situations.

The book explores transfer learning in language, vision, decision-making, and cross-domain intelligence. It also examines positive and negative transfer, showing why previously learned knowledge can sometimes help an AI system and sometimes lead to mistakes.

As the book progresses, readers explore the potential role of transfer learning in the development of increasingly general artificial intelligence. The book distinguishes between today's AI capabilities and the broader concept of Artificial General Intelligence (AGI), while encouraging students to think critically about what future intelligent systems may require.

Students also examine challenges involving accuracy, bias, safety, responsible development, and human oversight.

Part of the IntelliGloss Series, Transfer Learning makes an important area of artificial intelligence understandable without requiring a technical background and helps prepare learners to participate thoughtfully in an increasingly AI-powered world.