Ryan Wisnesky

Ontologies are seeing renewed interest as 'semantic applications' such as LLMs proliferate. In this talk Ryan will go over the hundred+ year history of ontologies, including their origin in taxonomies, the rise and fall of the semantic web, how knowledge graphs have been serving as lightweight ontologies and how computational extensions to knowledge graphs turn them into ontologies proper.

Organizations using LLMs (Large Language Models) to generate SQL code face a significant hurdle: ensuring the generated code is reliable and safe to execute. Unforeseen errors in the code can lead to unpredictable behavior, ranging from minor inconveniences to catastrophic data loss.

In this session, Ryan Wisnesky will show how computation has three aspects: logic, type theory, and algebra. He describe how type theory can be used to formalize almost anything, how logic informs 'proof engineering', and how algebra, and specifically category theory, allows us to relate our type-theoretic constructions to each other.

We claim that symbolic, rule-based AIs - millions of which have already been deployed since the phrase ‘expert system’ became popular in the 1980s - can and should be repurposed as generative AIs, enabled by recent advances in mathematics and computer science.

They say theory is not so much learned outright as absorbed over a long period of time. In this talk, we aim to motivate the audience to start absorbing and applying theory by telling the history of how theory (including category theory, database theory, programming language theory, and more) led directly to many of the foundational advances in query processing that we take for granted today, incl