Tag Archives: Syntactic learning

Relative toposes and meta-learning

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I am glad to share the slides of my recent talk on Relative toposes and meta-learning at the Computing and AI Summit in London.

In this presentation I give a conceptual introduction to the theory of relative toposes and discuss its relevance for modelling learning processes which build on top of existing knowledge through a sequence of steps lying at increasing levels of abstraction.

The ultimate aim of artificial meta-learning should be to mimic the distinctive, multi-layered way in which human learning unfolds, whilst leveraging the superior processing capabilities of machines.

Relative toposes and meta-learningDownload

Syntactic learning

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The slides of my recent talk at the AI workshop at RL China are available below.

In this presentation I propose an interpretation of learning processes in terms of the notions of mathematical theory and proof, and advocate for the importance of empowering artificial learnings systems with large formal vocabularies that will serve for expressing the concepts (and relations between them) that they will learn from data.

The aim is to obtain more robust and structured forms of learning with generalisation capabilities, and greater resilience and adaptability, mimicking the distinctive features of human intelligence.

We also consider this project as an essential step for arriving at a toposic theory of semantic information; indeed, syntax and semantics are interwined (think, for instance, of the syntactic construction of classifying toposes).

I look forward to experimentally testing these ideas with our team at the Lagrange Center in Paris.

Syntactic Learning Via Topos TheoryDownload