Event Recordings
Get Neuro-Symbolic AI Essentials digital badge

A new era of AI is rapidly emerging: neuro-symbolic AI combines knowledge-driven, symbolic AI with more traditional data-driven machine learning approaches. IBM is a leader in the research and development of neuro-symbolic AI technologies and we invite graduate students, AI practitioners, and anyone interested in this emerging field to participate in the 2022 IBM Neuro-Symbolic AI Summer School, to take place online on August 8-9 of this year.

The Summer School is a follow-on to the IBM Neuro-Symbolic AI Workshop held online in January 2022 (http://ibm.biz/ns-wkshp), which showcased the breadth and depth of the work being done in this field at IBM and by our collaborators. Participation in the first workshop is not a prerequisite for attending this year’s Summer School. All talks in the Summer School are meant to be self-contained.

The key properties of a neuro-symbolic system include:

Explainability by construction; the reasons a model makes its decisions should be open to inspection, without the need to do explanatory data analysis;
Learning with less and zero-shot learning; the system needs to be able to reason over the domain and over acquired knowledge;
Generalization of the solutions to unseen tasks and unforeseen data distributions.

IBM has demonstrated that natural language processing via the neuro-symbolic approach can achieve quantitatively and qualitatively state-of-the-art results, including handling more complex examples than is possible with today’s AI.

The summer school will include talks from over 25 IBMers in various areas of theory and the application of neuro-symbolic AI. We will also have a distinguished external speaker to share an overview of neuro-symbolic AI and its history. The agenda is a balance of educational content on neuro-symbolic AI and a discussion of recent results.

This is a virtual event and the registration for the event is free. The registered participants will get access to the recording of all sessions after the event.

Session and Time	Topic
Session 1: Opening 08:30 - 10:30ET	Opening 20 minutes Welcome (Alexander Gray - IBM) Motivation and Objective (Francesca Rossi - IBM) Summer School Overview (Jon Lenchner - IBM) Neuro-Symbolic AI Essentials Badge (Asim Munawar - IBM) Neurosymbolic AI: The Third Wave (Artur d'Avila Garcez - City University of London) 1 hour Neuro-Symbolic AI Open Problems and IBM Progress (Alexander Gray - IBM) 40 minutes
Session 2: Knowledge 11:00 - 13:00ET	Tutorial: Knowledge Foundations for AI Applications (Maria Chang - IBM) 1 hour Knowledge Acquisition and Induction Semantic Web Logic for AI IBM Research Overview: Knowledge Part 1: Universal Logic Knowledge Base (Rosario Uceda-Sosa - IBM) 25 minutes Interlinked KBs for broad encyclopedic, linguistic, and commonsense knowledge Supporting foundation for neuro-symbolic reasoning Part 2: ULKB Logic Language (Guilherme Lima - IBM) 25 minutes Higher order logic and simple type theory The ULKB Logic Language and its Python API Part 3: Deep linguistic processing (Alexandre Rademaker - IBM) 10 minutes Minimal recursive semantics and abstract meaning representation Open source tooling
Session 3: Reasoning 13:30 - 15:30ET	Tutorial: A Very Brief Introduction to Logic and Reasoning (Achille Fokoue - IBM) 1 hour First order logic (FOL) syntax and model theoretic semantics FOL reasoning and deductive systems FOL Extensions IBM Research Overview: Learnable Reasoning (Ndivhuwo Makondo - IBM, Hima Karanam - IBM) 1 hour Overview of Learning to Reason (e.g., neural theorem provers, MLNs, LTNs, etc) Introduction to LNNs - our framework for Learnable Reasoning Applications of LNNS
Session 4: Theory of Reasoning 16:00 - 18:00ET	Tutorial: Theory of Reasoning Foundations of Reasoning with Classical Logic (Marco Carmosino - IBM) 30 minutes Desiderata: what is a logic, and what makes a logic "good"? Example: First-Order Logic on finite graphs. Game-based semantics for First-Order Logic Computational Complexity (Jon Lenchner - IBM) 30 minutes Time and Space Complexity: P vs. NP and Related Questions Descriptive Complexity Bridging from Descriptive Complexity to Time and Space Complexity via Games IBM Research Overview: Complexity Part I: Theory of Real-Valued Logics (Ron Fagin - IBM) 30 minutes Allowing sentences to take values other than “true” or “false” A rich class of real-valued logic sentences A sound and complete axiomatization Part II: Games and Complexity Classes (Rik Sengupta - IBM) 30 minutes From Ehrenfecht-Fraisse Games to Multi-Structural Games From Multi-Structural Games to Syntactic Games Open Questions

Session and Time	Topic
Session 1: Machine Learning 08:30 - 10:30ET	Tutorial: What can Transformers do? (Mark Wegman - IBM, Hans Florian - IBM) 1 hour 30 minutes Computational power of transformers What limits their power and what means there are around those limits? Theoretical power of a transformer and the relation to their behavior in practice IBM Research Inductive Logic Programming with LNN (Prithviraj Sen - IBM, Sanjeeb Dash - IBM) 20 minutes Intro to Inductive Logic Programming (ILP) Generating LNNs for ILP Experimental ResAgendaItemDescts: Knowledge Base Completion (KBC) NS architecture zoo (Tengfei Ma - IBM, Ronny Luss - IBM) 10 minutes LNN for Times Series LNN for Mixed Models
Session 2: NLP via Logic 11:00 - 13:00ET	Tutorial: NLP via Logic Deep Semantic Parsing with Abstract Meaning Representation (Ramon Astudillo - IBM) 30 minutes AMR as Deep Semantic Representation AMR parsing and AMR-to-text machine learning approaches Incorporating structure to Large Language Models for AMR parsing Entity Linking (Dinesh Garg - IBM) 30 minutes Setup: What do we mean by entity, mention, and linking Linking over knowledge graphs Linking over relational databases Challenges and Approaches for Reliable Reasoning with Foundation Models: An Abstractive Summarization Use-case (Pavan Kapanipathi - IBM, Hans Florian - IBM) 1 hour Reliable Reasoning with Foundation Models Abstractive Summarization: Reasoning and Factuality in Summarization Challenges and Neuro-Symbolic Approaches
Session 3: Sequential Decision Making 13:30 - 15:30ET	Tutorial: SDM Theory and practice of RL (Miao Liu - IBM) 30 minutes Core elements in RL Computational approaches and RL tools Important mechanisms - Hierarchical RL and Multiagent RL Theory and practice of AI Planning (Michael Katz - IBM) 30 minutes What is planning and why is it hard Computational approaches to classical planning Planners and planning tools IBM Research Overview: SDM Integrating Planning and RL (Junkyu Lee - IBM) 30 minutes Introduction to integrating planning and RL AI Planning as annotation of RL Demonstration of libraries for planning annotated RL tasks Logical optimal actions (Don Joven - IBM, Maxwell Crouse - IBM) 30 minutes Text-based games as an application environment LNN rule induction for learning world models The situation calculus and theorem proving applied to planning
Session 4: NS AI Toolkit 16:00 - 17:00ET	Neuro-symbolic AI Toolkit (Naweed Khan - IBM) 45 minutes Logical Neural Networks (LNN) Universal Logic Knowledge Base (ULKB) Additional NSTK Components Closing 15 minutes Badge, Feedback (Asim Munawar - IBM) Closing Remarks (Alexander Gray - IBM)

IBM Neuro-Symbolic AI Summer School August 8-9, 2022

Agenda

Day 1 (August 8)

Day 2 (August 9)

Organizers

Speakers

Theme designed by

IBM Neuro-Symbolic AI Summer School
August 8-9, 2022