# PyTorch Geometric Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/pytorch-geometric
> Markdown URL: https://aitinkerers.org/technologies/pytorch-geometric.md
> Technology record last updated: 2026-02-27T01:31:30Z
> Generated: 2026-09-20T22:36:41Z

PyTorch Geometric (PyG) is the specialized library for building and training Graph Neural Networks (GNNs) on structured data like graphs, point clouds, and manifolds, all within the PyTorch framework.

PyTorch Geometric (PyG) is a powerful, modular extension for deep learning on irregular data structures (graphs, meshes, point clouds). It provides a comprehensive suite of state-of-the-art Graph Neural Network (GNN) models and a simplified API for rapid development. Key features include the `torch_geometric.data.Data` object for efficient graph representation, a specialized `DataLoader` for mini-batching large and small graphs, and optimized CPU/CUDA kernels for performance. PyG accelerates geometric deep learning: users can implement a GNN model in 10-20 lines of code and leverage multi-GPU support for scalable training on common benchmark datasets (e.g., Cora, QM9).

- Official technology site: https://github.com/pyg-team/pytorch_geometric
- Public AI Tinkerers demos and talks: 1
- Result page: 1 of 1

## Recent Public Talks and Demos

### [Quantum Gravity & Cognition: Two Peas in a Latent Pod](https://chicago.aitinkerers.org/talks/rsvp_jgf5XvdUe5w)

What if solving a reasoning task could feel like traversing curved space? In this talk, we explore a novel approach to cognition that treats symbolic thought as a geometric process. Inspired by quantum gravity, we model transformations in ARC-style grid tasks as paths over an emergent latent manifold — where meaning arises from entropy, transitions resemble spacetime curvature, and reasoning becomes a form of parallel transport. I have code that shows how to encode discrete instructions into a continuous manifold...which opens up a lot of cool use cases for things in physics, AI, chemistry, materials, etc. I'll just be going threw a a code notebook that shows off the math and parallels to other advanced pure math topics & theoretical physics...but its all testable which is cool.

- Event context: AI Tinkerers Chicago June Meetup ft Bonsai — 2025-06-03 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_jgf5XvdUe5w

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