Definition: A machine learning technique where a pre-trained model is fine-tuned for a different but related task.
Better definition: When your computer takes its existing knowledge and applies it to something new, like a jack-of-all-trades.
Where does this fit in the AI Landscape?
Transfer learning has become an essential tool for AI researchers, enabling them to leverage pre-trained models to save time and resources. It's widely used in computer vision and natural language processing tasks, accelerating the development of new AI applications across industries.
What are the real world impacts of this?
Transfer Learning allows AI models to be effectively applied across different tasks, saving time and computational resources. It's the reason why pre-trained models can be utilized for various applications, such as image recognition or language translation, enhancing the efficiency and speed of AI-based services. For developers, transfer learning offers a shortcut to develop models without needing extensive resources or large amounts of data.
What could go wrong in the real world with this?
A transfer learning model trained to write restaurant reviews gets repurposed to critique dance performances, leading to reviews that describe ballerinas as "spicy" and "savory."
Can be used to apply pre-trained models (like GPT for chat, or a code-completion model) to save time and computational resources during training.