The paper "Relational Inductive Biases, Deep Learning, and Graph Networks" by Peter W. Battaglia et al. discusses the importance of combinatorial generalization in achieving human-like intelligence in artificial intelligence (AI). The authors argue that structured representations and computations are key to realizing this objective, and propose the use of *relational inductive biases* within deep learning architectures to facilitate learning about entities, relations, and rules for composing them. They introduce *graph networks* as a new building block for the AI toolkit, which generalizes and extends various approaches for neural networks that operate on graphs. Graph networks support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. The paper also includes an open-source software library for building graph networks.The paper "Relational Inductive Biases, Deep Learning, and Graph Networks" by Peter W. Battaglia et al. discusses the importance of combinatorial generalization in achieving human-like intelligence in artificial intelligence (AI). The authors argue that structured representations and computations are key to realizing this objective, and propose the use of *relational inductive biases* within deep learning architectures to facilitate learning about entities, relations, and rules for composing them. They introduce *graph networks* as a new building block for the AI toolkit, which generalizes and extends various approaches for neural networks that operate on graphs. Graph networks support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. The paper also includes an open-source software library for building graph networks.