Compositionality in AI refers to the ability to generate and produce novel combinations from known components, which is essential for systematic generalization. It is a fundamental principle in the design of traditional, logic-based systems. Many statistical methods have struggled with compositional generalization, while recent advancements aim to improve this ability in deep learning architectures by incorporating analytical components that reflect the compositional structure of a domain, such as structure-processing neural networks or metalearning for compositional generalization. Despite these efforts, achieving predictable and systematic generalization in AI remains a challenge, as most results are empirical and not reliably predictable[1].
Get more accurate answers with Super Search, upload files, personalized discovery feed, save searches and contribute to the PandiPedia.
Let's look at alternatives:
- Modify the query.
- Start a new thread.
- Remove sources (if manually added).
- Request a manual search from our human research team.