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This post explores the specific challenges deep learning faces and how neurosymbolic AI aims to provide solutions.
In this post, we explore two prominent approaches: agentic workflows and zero-shot prompting
Logical Neural Networks (LNNs) represent a significant step forward in developing intelligent agent AI.
Training AI agents is a dynamic process requiring ongoing experimentation with methodologies, architectures, and parameters.
Neurosymbolic AI aims to bridge these gaps by merging deep learning's strengths with the reasoning abilities of symbolic AI.