Explainable AI in real life could mean Einstein not just answering your questions, but also providing justification. Advancing the state of the art in natural language processing is done on the intersection of graphs and machine learning.

A super geeky topic, which could have super important repercussions in the real world. That description could very well fit anything from cold fusion to knowledge graphs, so a bit of unpacking is in order. (Hint: it’s about Salesforce, and Salesforce is not into cold fusion as far as we know.)

If you’re into science, chances are you know arXiv.org. arXiv is a repository of electronic publication preprints for scientific papers. In other words, it’s where cutting edge research often appears first. Some months back, a publication from researchers from Salesforce appeared in arXiv, titled “Multi-Hop Knowledge Graph Reasoning with Reward Shaping.”

The paper elaborates on a technique for using knowledge graphs with machine learning; specifically, a branch of machine learning called reinforcement learning. This is something that holds great promise as a way to get the best of both worlds: Curated, top-down knowledge representation (knowledge graphs), and emergent, bottom-up pattern recognition (machine learning).

This seemingly dry topic piqued our interest for a number of reasons, not the least of which was the prospect of seeing this being applied by Salesforce. Xi Victoria Lin, research scientist at Salesforce and the paper’s primary author, was kind enough to answer our questions.

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