AI is on a roll. Adoption is increasing across the board, and organizations are already seeing tangible benefits. However, the definition of what AI is and what it can do is up for grabs, and the investment required to make it work isn’t always easy to justify. Despite AI’s newfound practicality, there’s still a long way to go.

Let’s take a tour through the past, present, and future of AI, and learn from leaders and innovators from LinkedIn, Intel Labs, and cutting-edge research institutes.

Mike Dillinger is the technical lead for Taxonomies and Ontologies at LinkedIn’s AI Division. He has a diverse background, ranging from academic research to consulting on translation technologies for Fortune 500 companies. For the last several years, he has been working with taxonomies at LinkedIn.

LinkedIn relies heavily on taxonomies. As the de facto social network for professionals, launching a skill-building platform is a central piece in its strategy. Following CEO Ryan Roslanski’s statement, LinkedIn Learning Hub was recently announced, powered by the LinkedIn Skills Graph, dubbed “the world’s most comprehensive skills taxonomy.”

The Skills Graph includes more than 36,000 skills, more than 14 million job postings, and the largest professional network with more than 740 million members. It empowers LinkedIn users with richer skill development insights, personalized content, and community-based learning.

For Dillinger, however, taxonomies may be overrated. In his upcoming keynote in Connected Data World 2021, Dillinger is expected to refer to taxonomies as the duct tape of connecting data. This alludes to Perl, the programming language that was often referred to as the duct tape of the internet.

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