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As impressive as they are, language models like GPT-3 and BERT all have the same problem: they\\u2019re trained on reams of internet data to imitate human writing. And human writing is often wrong, biased, or both, which means language models are trying to emulate an imperfect target.

Language models often babble, or make up answers to questions they don\\u2019t understand. And it can make them unreliable sources of truth. Which is why there\\u2019s been increased interest in alternative ways to retrieve information from large datasets \\u2014 approaches that include knowledge graphs.

Knowledge graphs encode entities like people, places and objects into nodes, which are then connected to other entities via edges, which specify the nature of the relationship between the two. For example, a knowledge graph might contain a node for Mark Zuckerberg, linked to another node for Facebook, via an edge that indicates that Zuck is Facebook\\u2019s CEO. Both of these nodes might in turn be connected to dozens, or even thousands of others, depending on the scale of the graph.

Knowledge graphs are an exciting path ahead for AI capabilities, and the world\\u2019s largest knowledge graphs are trained by a company called Diffbot, whose CEO Mike Tung joined me for this episode of the podcast to discuss where knowledge graphs can improve on more standard techniques, and why they might be a big part of the future of AI.

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Intro music by:

\\u279e Artist: Ron Gelinas

\\u279e Track Title: Daybreak Chill Blend (original mix)

\\u279e Link to Track: https://youtu.be/d8Y2sKIgFWc

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0:00 Intro

1:30 The Diffbot dynamic

3:40 Knowledge graphs

7:50 Crawling the internet

17:15 What makes this time special?

24:40 Relation to neural networks

29:30 Failure modes

33:40 Sense of competition

39:00 Knowledge graphs for discovery

45:00 Consensus to find truth

48:15 Wrap-up