Executive interview: Adding common sense to generative AI creativity

Confidence in GQL

Webber believes the newly ratified ISO standard for graph query language (GQL) represents a significant inflection point for the technology. The ISO standard for structured query language (SQL), called SQL-86, was published in 1986.

Recalling the significance of the standard, Webber says he was “programming ZX Spectrum at that point” so “SQL meant nothing to me” back then. But the SQL-86 standard settled a debate that began in the 1970s on how to manage databases. Edgar Cobb, while working at IBM developed the relational database model. The SQL standard – adopted by the American National Standards Institute (ANSI) in 1986 and the International Organization for Standardization (ISO) in 1987 – gave application developers and enterprise software buyers the confidence to use relational databases.

An alternative proposal, the network database, developed by Charles Bachman lost out. But Bachman’s approach, according to Webber, is an ancient precursor to graph databases.

He believes standards are important when IT decision-makers have to make technology bets. “CIOs are nervous because if you make a significant investment in a system, you don’t want to be locked in or find that the system has no future and you’ve backed the wrong horse. It’s the VHS versus Betamax analogy. 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. Even though there are different dialects of SQL, the basic syntax remains the same. The same should hold true for GQL.

“In principle, you can always switch vendors because your language is going to stay the same,” he says. “The additional learning I have to do to specialise in a relational database management system like SQL Server or Oracle is marginal.”

AI common sense

Analyst Gartner recently put knowledge graphs at the centre of its impact radar for generative artificial intelligence (GenAI).

“Graph databases are knowledge graphs, a network of facts, which offer a most appropriate counterbalance for GenAI,” says Webber. If GenAI is like the creative right side of the brain, he feels that graphs are like the left side, which is more focused on reasoning.

Graph databases are knowledge graphs, a network of facts, which offer a most appropriate counterbalance for GenAI

“You’ve got a probabilistic engine in generative AI. I love it. I know it’s a robot, but it still feels just so dangerously, wonderfully close to having that spark of imagination,” he says. “But this spark needs to be tempered, and it turns out that knowledge graphs are particularly good at this, using an approach called graph RAG.” This is where the graph database provides contextual information for LLMs.

“This is probably the best way we know to get the best out of generative AI, while stopping falsehoods and misleading things from leaking to the end user. It’s the left brain working with the right brain.”

During the discussion, Webber talks about an example referred to during a National Public Radio (NPR) broadcast where an AI researcher asked an LLM how long it would take to dry two shirts on her washing line, if it takes three hours to dry one. The answer is obviously three, but an LLM may reason that two shirts would take twice as long.

“You can stop those falsehoods from leaking through by putting that vector’s map into knowledge graphs, which can be traversed,” says Webber.

For example, he says that when presented with the word “apple”, the user may want the AI system to understand that the apple in question is the company in Cupertino that makes iPods and iPhones. With the graphs, he says: “You can traverse a rich network of facts around Apple, the tech company. It is not apple, the fruit, nor Apple, the Beatles record label.” In effect, the graph database applies a level of common sense to the LLM, which, based on context, helps to direct its responses to the answers that make more sense.

“Where you’ve got a network of facts to exploit, no other data model gives you that network of facts,” Webber claims. “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.