Our AI Lab leaders CTO Rigvi Chevala and Associate Vice President Cathy Feng have been busy writing articles and granting interviews on domain-specific AI. The following four articles detail some implementation best practices, how domain-specific AI can help your company identify opportunities and even leapfrog the competition, and use domain-specific AI as a knowledge management tool. Enjoy the following articles.
Best Practices for Creating Domain-Specific AI Models
What are the best practices for implementing a domain-specific AI solution in your enterprise? Evalueserve’s resident AI Expert Cathy Fang gets into the details in this KD Nuggets article. She details how use cases and subject matter expertise help, but adds companies need to find quality, domain-specific data and adapt the AI implementation cycle to resolve problems that require more specificity and relevancy.
Identifying Opportunities with Domain-Specific AI
CTO Rigvi Chevala details how to identify leapfrog and optimization opportunities inside your enterprise via this Dataversity article. Rigvi calls it, “playing ‘Sherlock Holmes’ at an organizational level, except it’s hard to find enough Sherlocks to help all businesses. That’s where domain-specific AI becomes an excellent tool to help uncover hidden opportunities buried in mountains of disparate data sources.”
Domain-Specific AI as an Innovative Knowledge Management Solution
KM World interviewed Rigvi Chevala on taxonomies and their role in AI. Specific terms, definitions, and hierarchies of terminology that are germane to individual organizations in their domain-specific AI implementations. Top knowledge management solutions have domain models that encompass taxonomies for respective verticals, according to Rigvi.
A Round of Domain-Specific AI Q&A
After digesting Cathy Feng’s KD Nuggets article, the blog team asked Cathy to answer five follow-up questions, which turned into Cathy’s recent Medium article, “5 Answers on Domain-Specific AI”. In it, Cathy answers five top questions, including differentiating general and domain AI, domain AI’s value, cultural challenges, and data challenges.