DAIM: Inside The Algorithm | Why Neurosymbolic AI Solves What Scaling Alone Cannot with Dr Vaishak Belle

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Large language models are surprisingly good at producing fluent, plausible text. So why do they still confidently get simple things wrong?

In this episode, Dr Jeremy Bradley is joined by Dr Vaishak Belle, Reader at the University of Edinburgh's School of Informatics, Alan Turing Institute Faculty Fellow and Director of Research and Innovation at the Bayes Centre. Vaishak has spent 16 years working at the intersection of logic, probability and machine learning and brings that lens to one of AI's most persistent problems: hallucination.

The conversation traces why scaling alone will not solve reliability, what neurosymbolic AI actually is and why tools like Claude Code quietly depend on it, how theory of mind is being engineered into language models, and where reinforcement learning fits into the future of AI reasoning.

If you work at the frontier of AI research or engineering, this is a grounded, technically rich conversation worth your time.

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If you enjoyed this conversation, you might also like this episode featuring Dr Petar Veličković. Petar joined us on Data and AI Mastery to explore how graph neural networks bring structured reasoning into systems like Google Maps and how AI is being used as a genuine discovery partner in mathematics.

Apple: https://podcasts.apple.com/gb/podcast/bridging-ai-research-and-real-world-impact-dr-petar/id1779783413?i=1000734000576

Spotify: https://open.spotify.com/episode/7qA0AY9MlLS2L9PANqlXNi?si=37d8a8fb43c14cc9

YouTube: https://www.youtube.com/watch?v=GwMUSNidnvE

Glossary Terms

Neurosymbolic AI: an emerging field that merges the intuitive pattern recognition of neural networks with the logical, rule-based reasoning of symbolic AI.