Given the growing interest in artificial intelligence for its ability to enhance human capabilities, establishing governance guardrails is crucial, as demonstrated by Intercontinental Exchange’s AI Center of Excellence.

As a testing ground for new AI use cases, the AI Center of Excellence plays a key role in the incubation process, ensuring that established guidelines — such as data validation, AI model governance, data governance and distribution — are diligently followed, according to Anand Pradhan (pictured), senior director of technology at the AI Center of Excellence at ICE. Pradhan also runs the NYSE Launchpad Lab.

Intercontinental Exchange’s Anand Pradhan talks to theCUBE about the importance of ICE’s AI Center of Excellence.

“As the name suggests, it is a Center for Excellence,” Pradhan said. “What it means is that let’s say there are various use cases. How do we make sure that we implement the AI use cases properly so that each of the processes is followed. Basically, our idea is to empower the product and application delivery team for their AI use cases. It’s an internal R&D group.

Pradhan spoke with theCUBE Research’s  John Furrier at the Data + AI Summit, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how ICE’s AI Center of Excellence is incorporating the much-needed guardrails in the AI market. (* Disclosure below.)

Regulations are at the heart of the AI Center of Excellence

As regulations become increasingly central in the AI industry, the AI Center of Excellence strictly adheres to them, fostering enhanced innovation in the sector. As a result, the AI Center of Excellence adheres to critical issues, such as observability, explainability and accuracy, Pradhan pointed out. 

“AI regulation is one thing that is fast evolving,” he said. “Our job is to make sure that everything is implemented by the book. Having a strategic vision in the company to lay out the foundation so that multiple AI applications or projects can run with the same vision is the key for the Center of Excellence.”

The AI Center of Excellence critically looks into various issues, such as vector databases, deep learning models and sentiment analysis. This comes in handy in tackling latency and enhancing observability, according to Pradhan. 

“I have been part of the Pillar development platform or trading platform that powers all our exchanges at New York Stock Exchange,” he said. “I moved to lead the AI Center of Excellence for our parent company to increase our collaboration across the company observability. We use InfiniBand, which is basically used in AI training, so we understand a thing or two about low latency, high-volume usage. We mine data for 650 billion transactions every single day to find patterns.”

Recognizing that regulation is complex, the AI Center of Excellence thoroughly examines intended use cases, focusing on critical issues such as accuracy. This is achieved through the implementation of productivity improvement use cases, according to Pradhan. 

“First of all, we need to understand where AI is being used,” he said. “Is the large language model used for making pricing decisions? The other thing is are we doing everything by the book? Do we have an audit trail? Do we have observability? Do we have monitoring? Did we validate the accuracy of it? All those kinds of things. So, that is of paramount importance for us.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of the Data + AI Summit: 

(* Disclosure: TheCUBE is a paid media partner for the Data + AI Summit. Neither Databricks Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE