Definition: The process of creating new training data by applying transformations or modifications to existing data.
Better definition: When your computer clones its favorite data and gives it a makeover, just for fun.
Where does this fit in the AI Landscape?
Data augmentation is a vital technique in AI and machine learning, as it helps improve model performance and generalization by increasing the diversity of training data. It's especially useful in domains like computer vision and natural language processing, where acquiring additional labeled data can be challenging.
What are the real world impacts of this?
Data Augmentation techniques improve the performance of AI models, making our interactions with AI technologies more seamless and accurate. For developers, data augmentation offers a practical way to increase the robustness and accuracy of models, especially when dealing with limited data.
What could go wrong in the real world with this?
A data augmentation algorithm is used to diversify a recipe database, but it goes overboard, creating bizarre combinations like "spaghetti ice cream" and "pickle smoothies."
Techniques used to increase the size and diversity of training data. For code autocompletion, it might involve creating new code snippets by modifying existing ones. For chat, it could be rephrasing existing sentences.