Causality – The Next Big Thing in AI

Stu Frost, CEO, Geminos Software

At Geminos, we believe that the ‘next big thing’ in AI is causality. This belief is backed up by a series of recent data points: 

  1. Articles that literally say causality is the next important thing in AI – http://bit.ly/3uvMwzJ
  2. An online Harvard course on causal diagrams that has 58,000(!) enrolled – http://bit.ly/2ZIdeqm 
  3. A whole slew of books on causal inference including, of course, the one that started it all, “The Book of Why: The New Science of Cause and Effect” by Prof. Judea Pearl. 
  4. Some great papers out of organizations such as IBM Research  http://ibm.co/2ZERXOp 
  5. Articles in respected publications such as New Scientist – http://bit.ly/3kbUt88 

 

However, most of the work on causality so far has been by people that want to gain a better understanding of their data, such as epidemiologists. Pearl himself acknowledges that there is a lot of work to be done in marrying causality with Artificial Intelligence (AI– and also that there is a lot of resistance in the data science community. See http://bit.ly/3keyttB 

This is the challenge that we are taking on at Geminos – how to build better Artificial Narrow Intelligence (ANI) solutions by leveraging causality. 

 

What do we mean by ANI solutions? 

  • Artificial General Intelligence (AGI) is what most people (esp. Hollywood) think of when we talk about AI. 
  • ANI is the current state of the art in using AI to address business problems and AGI is probably decades away from being a reality. 
  • A substantial amount of the current research on causality is targeting AGI, not ANI, since it’s seen as the next rung on the ladder towards true machine intelligence. 
  • At Geminos, we believe that much of this research can be applied right now to ANI problems. 

 

What do we mean by better? 

  • Faster time to value, due to more reuse within and across domains. 
  • More transparency by avoiding monolithic AI algorithms. 
  • Better stability by avoiding mere correlations in the data. 
  • A focus on modeling how we want our digitalized business to operate in the future as opposed to simply doing a better job of analyzing and making predictions from historic data. The problem with the latter is that it’s really just the data we happened to collect from how the business used to operate. 

 

What do we mean by leveraging causality? 

  • Using Pearl’s causal approach to model the digitalized world as we want it to be. 
  • Using the causal model to better understand which elements of our historical data are actually useful and to direct our data science efforts towards the causal relationships we need as part of the solution. 
  • Directly mapping the causal model to a microservices solution architecture so we can maintain a 1:1:1 mapping between the model, the solution and the real world throughout the lifecycle of the solution (our take on digital twins). 
  • Underpinning the entire platform with Pearl’s causal calculus so we can feedback metrics from the operation of our digitalized business into the model and also drive sophisticated simulation and planning tools. 

 

The end result will be much more robust, transparent and higher quality AI-driven solutions, and a faster path to a truly digitalized business. 

About the Author

Stu Frost has 30 years’ experience in founding and leading successful data management and analytics startups. He has raised over $300m in funding and founded/co-founded 31 startups. Successful exits have included IPOs and acquisitions, including one by Microsoft.

In the last eight years, Stu has created and incubated some of the leading companies in the IIoT market, including Maana (IIoT knowledge platform), OspreyData (oil and gas analytics), NarrativeWave (wind farm analytics), ThinkIQ (food traceability) and SWARM (AI agents for IIoT). During this time, he saw the market go from its very early gestation to the point where major industrial companies are starting to make significant, long term commitments to digitization. Through this unique experience, Stu has developed deep knowledge of the market’s needs and how to successfully create and sell key technologies to meet those needs. 

His latest company, Geminos Software, has created a platform that is designed to improve productivity of developers building AI-driven analytics by 10X.

For more information:

geminos.ai

www.linkedin.com/in/stu_frost

https://www.advisorycloud.com/profile/Stuart-Frost