Show HN: Open-Sourcing Google's Lattice Models in PyTorch

[willbakst]

We're a group of engineers, AI/ML enthusiasts, and author of this paper https://openreview.net/forum?id=0pxiMpCyBtr who saw a closed door in AI/ML and decided to open it. This project is a PyTorch reimagining of Google's TensorFlow Lattice models, which despite being labeled open-source, were previously open in name only (you have to be a Googler to contribute). Also, side point…TensorFlow is dying https://thenextweb.com/news/why-tensorflow-for-python-is-dyi...

Here's the deal: Lattice models excel in making AI interpretable—key for sectors where understanding model rationale is as crucial as the prediction. Yet, their potential was gated behind Google's walls.

Fueled by the spirit of true open-source philosophy and driven by the robust, community-centric ecosystem of PyTorch, we've built a toolkit that not only matches the functionality of Google's TensorFlow lattices but is open for everyone to contribute, improve, and innovate.

We're inviting you to join in. Whether it's to critique, contribute, or simply to learn, we believe your voice can help shape the future of open-source AI/ML.

https://github.com/ControlAI/pytorch-lattice

[willbakst]

Hey all, I'm William Bakst, the creator of the library. I used to work at Google where I worked on the Glassbox research team (creators of TensorFlow Lattice and TensorFlow Constrained Optimization). It was crazy useful internally and approaches problems relevant to all kinds of businesses so I was kind of baffled that the 'open-source' version was only available to Google employs to contribute to. I think PyTorch has a better community around it anyways, so i've made an actual open-source version so any company that wants to take advantage of the explainability of a simple model like linear regression, but the accuracy of more complex models like deep neural networks, can start applying this now AND anyone can contribute, so hopefully this gets better and more accessible over time.

[shaqbert]

Hi William, super nice tech. Alas, I am suffering from lack of imagination, can you give some example use cases where this tech would really shine?