Using Graphs for AI and ML with no BS

Graphs are brilliant. They’re really well studied and have some amazing properties, especially for predictive analytics. But then there’s AI: it’s super-cool and also has some amazing abilities to guess the future given the past.

So, which are we meant to choose? I’d argue we should use both.

In this talk we’ll see how graphs give us a framework for contextualizing the world around us. We’ll explore how simple rules from graph theory can evolve our model to show how it might be in the future.

But that’s not all, we’ll also see how we can take our graphs and feed them into our ML pipelines for better models and outcomes than simple row-wise data using graph neural nets. And we’ll see how to learn on graphs directly with graph convolutional networks.

Finally, we’ll close the loop by asking our machine learning to tell us about other queries we should be running against the input graph to find patterns in data we don’t even know are valuable today.