ConferenceCall 2026 03 11: Difference between revisions
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= [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} = | = [[OntologySummit2026|Ontology Summit 2026]] {{#show:{{PAGENAME}}|?session}} = | ||
* Randy Goebel | * '''Randy Goebel''' ''A (partial) framework for debugging foundation models'' | ||
* 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. | |||
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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). | |||
* Bio: 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. He has had faculty appointments and visiting faculty appointments at the University of Waterloo, University of Regina, University of Tokyo, Hokkaido University (Sapporo, Japan), Multimedia University (Kuala Lumpur, Malaysia), Instituto Tecnológico de Monterrey (Monterrey, Mexico), and has been a visiting researcher at the German Center for AI Research (DFKI), the National Institute for Informatics (NII, Tokyo), and the Volkswagen Data Lab (Munich). His research interests include formal knowledge representation and reasoning (induction, belief revision, explainable AI (XAI)), knowledge visualization, algorithmic complexity, natural language processing (NLP), systems biology, with applications in clinical medicine, legal reasoning, and automated driving. Recently he has founded a new open access research institute, Openmind, a not for profit corporation in Canada and Singapore | |||
== Conference Call Information == | == Conference Call Information == | ||
Revision as of 20:47, 2 March 2026
| Session | Ontologies and AI |
|---|---|
| Duration | 1 hour |
| Date/Time | 11 Mar 2026 16:00 GMT |
| 9:00am PDT/12:00pm EDT | |
| 4:00pm GMT/5:00pm CET | |
| Convener | Gary Berg-Cross |
Ontology Summit 2026 Ontologies and AI
- Randy Goebel A (partial) framework for debugging foundation models
- 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).
- Bio: 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. He has had faculty appointments and visiting faculty appointments at the University of Waterloo, University of Regina, University of Tokyo, Hokkaido University (Sapporo, Japan), Multimedia University (Kuala Lumpur, Malaysia), Instituto Tecnológico de Monterrey (Monterrey, Mexico), and has been a visiting researcher at the German Center for AI Research (DFKI), the National Institute for Informatics (NII, Tokyo), and the Volkswagen Data Lab (Munich). His research interests include formal knowledge representation and reasoning (induction, belief revision, explainable AI (XAI)), knowledge visualization, algorithmic complexity, natural language processing (NLP), systems biology, with applications in clinical medicine, legal reasoning, and automated driving. Recently he has founded a new open access research institute, Openmind, a not for profit corporation in Canada and Singapore
Conference Call Information
- Date: Wednesday, 11 March 2026
- Start Time: 9:00am PDT / 12:00pm EDT / 5:00pm CET / 4:00pm GMT / 1600 UTC
- ref: World Clock
- Note: The US and Canada are on Daylight Saving Time while Europe has not yet changed.
- Expected Call Duration: 1 hour
- Video Conference URL: https://us02web.zoom.us/j/86994661673?pwd=mMUeaWyWhBMSzTw3SgH5GjMv2Qx4rH.1
- Meeting ID: 869 9466 1673
- Passcode: 803090
- Please download and import the following iCalendar (.ics) files to your calendar system.
Discussion
Resources
Previous Meetings
| Session | |
|---|---|
| ConferenceCall 2026 03 04 | Ontologies and AI |
| ConferenceCall 2026 02 25 | Retrospective |
| ConferenceCall 2026 02 18 | Overview |
Next Meetings
| Session | |
|---|---|
| ConferenceCall 2026 03 18 | Ontologies and AI |
| ConferenceCall 2026 03 25 | Ontologies and AI |
| ConferenceCall 2026 04 01 | Foundations and Tools |