IBM Neuro-Symbolic AI Summer School
- Virtual
About
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, 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.
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.
Why attend
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.
Speakers
Artur d'Avila Garcez
Alexander Gray
Ramon Astudillo
Francesca Rossi
Mark Wegman
Guilherme Lima
Ronald Fagin
Dinesh Garg
Agenda
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)
FRFrancesca RossiIBM Fellow and AI Ethics Global LeaderIBM ResearchAGAlexander GrayVP of Foundations of AIIBM ResearchJLJon LenchnerFoundations of Computer ScienceIBM ResearchAMAsim MunawarProgram Director for Neuro-Symbolic AIIBM ResearchAGArtur d'Avila GarcezProfessor of Computer ScienceCity University of LondonKnowledge Foundations for AI Applications (Maria Chang - IBM) 1 hour
- Knowledge Acquisition and Induction
- Semantic Web
- Logic for AI
IBM Research Overview 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
IBM Research Overview Part 2: Logic language and hyperknowledge (Guilherme Lima - IBM) 25 minutes
- Higher order logic and simple type theory
- The ULKB Logic Language and its Python API
IBM Research Overview Part 3: Deep linguistic processing (Alexandre Rademaker - IBM) 10 minutes
- Minimal recursive semantics and abstract meaning representation
- Open source tooling
MCMaria ChangResearch Staff Member, AIIBM ResearchRURosario Uceda-SosaResearcher, Ontologies, Semantic Models and Services, Inductive KnowledgeIBM ResearchGLGuilherme LimaResearch ScientistIBM ResearchARAlexandre RademakerResearch ScientistIBM ResearchA Very Brief Introduction to Logic and Reasoning (Achille Fokoue-Nkoutche - IBM) 1 hour
- First order logic (FOL) syntax and model theoretic semantics
- FOL reasoning and deductive systems
- FOL Extensions
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
AFAchille Fokoue-NkoutcheResearch ScientistIBM ResearchNMNdivhuwo MakondoResearch ManagerIBM ResearchHKHima KaranamSTSM, AI ReasoningIBM ResearchTutorial: Theory of Reasoning
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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
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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
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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
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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
MCMarco CarmosinoResearch ScientistIBM ResearchJLJon LenchnerFoundations of Computer ScienceIBM ResearchRFRonald FaginIBM FellowIBM ResearchRSRik SenguptaResearch InternIBM Research-