Executive interview: Adding common sense to generative AI creativity | Computer Weekly
Summary
There is a healthy relationship between large language models (LLMs) and graph databases, which are used to draw in information across different networks of data, according to Jim Webber, chief scientist at Neo4j. Webber believes the newly ratified ISO standard for graph query language (GQL) represents a significant inflection point for the technology. I think SQL gave a significant injection into the application software market because it told everyone that relational database technology is mature and safe.” According to Webber, the ISO GQL standard, like SQL-86, protects IT buyers from making poor commercial decisions. “The additional learning I have to do to specialise in a relational database management system like SQL Server or Oracle is marginal.” Analyst Gartner recently put knowledge graphs at the centre of its impact radar for generative artificial intelligence (GenAI). “Nowadays, the way you would exploit that network of facts is by writing the query code in SQL.” But given the inefficiencies Webber speaks about in using SQL to perform joins across multiple data sources, running GQL on a knowledge graph may be how AI learns common sense going forward.