Neuro-symbolic AI, Logic, and the Future of Artificial Intelligence | Zoomposium with Prof. Dr. Artur d'Avila Garcez

Can AI really reason—or is it only predicting the next word?

In this episode of Zoomposium, we speak with Prof. Dr. Artur d'Avila Garcez, Professor of Computer Science and Director of the Research Centre for Machine Learning at City St George's, University of London. As one of the pioneers of neuro-symbolic artificial intelligence, Professor Garcez has spent more than two decades developing methods that combine neural networks with symbolic reasoning, logic, and knowledge representation.

Modern AI systems such as Large Language Models have achieved remarkable success through data-driven learning. Yet they still struggle with logical reasoning, abstraction, explainability, and robust knowledge transfer.

Neuro-symbolic AI offers a fundamentally different perspective: instead of viewing learning and reasoning as competing paradigms, it seeks to integrate them into a unified architecture capable of combining statistical learning with explicit symbolic knowledge.

In this interview, we explore some of the most fundamental questions surrounding the future of artificial intelligence:

• Why statistical learning alone may not be be sufficient for Artificial General Intelligence (AGI)

• How neuro-symbolic AI combines learning, reasoning, and symbolic knowledge

• The difference between pattern recognition and genuine understanding

• Why explainable AI is essential for the next generation of intelligent systems

• Whether future AI will require new forms of logic, including many-valued and context-dependent reasoning

• The role of emergence, self-organization, and dynamic process architectures

• Artificial consciousness, autonomous agency, and internal world models

• Whether intelligence is ultimately grounded in data, symbolic structure, or both

Beyond computer science, the conversation also explores the philosophical foundations of intelligence. We discuss the relationship between logic and cognition, the role of symbolic representation in intelligent systems, structural realism, philosophy of mind, and the possibility that future AI may require entirely new concepts of reasoning and knowledge.

Professor Garcez explains why today's deep learning systems remain limited despite their impressive performance, how neuro-symbolic methods can bridge the gap between learning and reasoning, and why the integration of logic and machine learning may become one of the key ingredients for the development of trustworthy and explainable AI.

The interview also addresses one of the central questions of current AI research: Can Artificial General Intelligence emerge simply by scaling existing language models, or will future intelligent systems require fundamentally new computational principles inspired by human cognition and biological intelligence?

If you are interested in artificial intelligence, machine learning, logic, neuroscience, cognitive science, philosophy of mind, or the future of AGI, this conversation offers an in-depth exploration of one of the most promising research directions in contemporary AI.

Guest:

Prof. Dr. Artur d'Avila Garcez

Professor of Computer Science

Director, Research Centre for Machine Learning

City St George's, University of London

Hosts:

Dirk Boucsein (philosophies.de), Axel Stöcker (die-grossen-fragen.com)

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