Graph AI
Machine learning, and its deep learning subdomain, make a great match for graphs.
Machine learning on graphs has exploded in recent years, making Graph AI one of the hottest things in AI today.
AI and Machine Learning
The roots of AI go back to the 1950s, and include many disciplines & approaches. But the recent AI boom is mostly about Machine Learning.
More often than not, when people refer to Artificial Intelligence (AI) today, what they really mean is Machine Learning.
Machine Learning is essentially pattern matching, and it consists of a conceptually simple workflow.
AI Workflow
If you’ve done a good job, the algorithm should be able to give results such as what an image depicts, or what a customer is likely to buy next.
Graphs and AI, AI and Graphs
What does graph have to do with machine learning? A lot, actually, and it goes both ways.
Machine learning, and its deep learning subdomain, make a great match for graphs.
Machine learning on graphs quickly went from a nascent technology to one that is an AI mainstay.
Amazon, Alibaba and Apple are just some of the organizations using this in production, and advancing the state of the art.
Already back in 2019, more than 20% of the research published in top AI conferences was graph-related. Today graph AI dominates research and commercial applications.
Domain knowledge can effectively help a deep learning system bootstrap its knowledge, by encoding primitives instead of forcing the model to learn these from scratch. Nathan Benaich, author of the State of AI Report
Knowledge graph technology is the best we have for encoding domain knowledge.
Knowledge graphs in turn can benefit from machine learning to boost knowledge acquisition.
