The causal AI wave could be the next to hit

This article was originally published in WatersTechnology. See here for the full article.

A growing field

CausaLens was founded in 2017 by Darko Matovski and Maksim Sipos, who came from the hedge fund industry. The story goes that Matovski and Sipos were eating at a Turkish restaurant and discussing how correlation doesn’t imply causation. It was something they felt hadn’t been properly solved but given their finance backgrounds, there was potential to build something. Today, the startup’s platform, decisionOS, allows users to deploy causal models that can then be used by non-technical users. In January of last year, the startup raised $45 million in a Series A funding round.

Another company, Causality Link, provides an AI platform that takes in real-time news and market research to provide a complete view of the events and trends impacting the market. Pierre Haren, co-founder and CEO of Causality Link, says the ability to backtrack and look at what has happened in the past is important for causality.

“If you ask, ‘What was the causal model of the world just before Covid-19?’ our system can do it,” he says. “This is important for the rapid evolution of causal models because if you are trying to do back testing or explaining why a mistake was made a few years ago, you cannot use the current knowledge; you have to go back to the knowledge you had at that time.”

Haren is doubtful that the technology that underpins ChatGPT can do this. In tests, he says the chatbot can recall the net worth of Bill Gates from a specific year in time but that it struggles with backtracking on patterns. “It becomes very complex to associate a date with a pattern that has come from a statistical multiplicity of inputs,” Haren says.


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