Ontology Summit 2026

[Ken Baclawski]

We are thrilled to announce that the tomorrow's session of the Ontology Summit 2026 will feature two eminent speakers:

Gary Berg-Cross will be presenting "AI and Ontologies: Past, Present and some future views"

Ram D. Sriram will be presenting "Ancient Indian View of Ontologies"

The session will be on Wednesday 4 March 2026 at Noon US/Canada Eastern Time and will last one hour.  The summit is open to the public and no registration is necessary.  All summit sessions are recorded and are available on the summit web pages and on the Ontology Summit YouTube channel.

The session page is: https://ontologforum.com/index.php/ConferenceCall_2026_03_04

Zoom information:

https://us02web.zoom.us/j/86994661673?pwd=mMUeaWyWhBMSzTw3SgH5GjMv2Qx4rH.1
Meeting ID: 869 9466 1673
Passcode: 803090

Ken Baclawski
Chair, Ontology Summit 2026

[Ken Baclawski]

We had a very well attended session on 4 March.  If you were unable to attend be sure to check out the videos on the session page at https://ontologforum.com/index.php/ConferenceCall_2026_03_04 or on the Ontology Summit YouTube channel.

We are pleased to announce that the next session of the Ontology Summit 2026 will feature

Randy Goebel

who will be presenting

A (partial) framework for debugging foundation models

Randy Goebel is a Professor of Computing Science and adjunct Professor in the Faculty of Medicine at the University of Alberta, and Fellow and Co-founder of the Alberta Machine Intelligence Institute (AMII), one of three Canadian federally-funded AI research organizations.

As usual, all summit sessions are zoom sessions on Wednesdays at Noon US/Canada Eastern Time and each session lasts one hour.  The summit is open to the public and no registration is necessary.  All summit sessions are recorded and are available on the summit web pages and on the Ontology Summit YouTube channel.

The session page is: https://ontologforum.com/index.php/ConferenceCall_2026_03_11

Zoom information:

https://us02web.zoom.us/j/86994661673?pwd=mMUeaWyWhBMSzTw3SgH5GjMv2Qx4rH.1
Meeting ID: 869 9466 1673
Passcode: 803090

Abstract: The current most popular mechanisms of AI are Large Language Models (LLMs) despite the reality that they are computer programs that produce incorrect results. If any evolution of AI systems are to be trusted, the possible choices of foundation models must be further developed.  We propose a simple framework that admits a number of different formalisms for so-called foundation models, and argue that, while the methods for debugging them are varied, the crucial scientific question should focus on how to provide a foundation for their debugging. The overall hypothesis is that if we want to establish trust in AI system behaviour we must ensure mechanisms to ensure their reliable operation.

Relevant ideas come from discrete mathematics (e.g., Gödel, Turing), logic and logic programming, Bayesian probability, reinforcement learning, and transformers. Overall, we seek to understand how to choose amongst such methods and how to integrate them, depending on expectations about application correctness (or not).