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#96 Prof. PEDRO DOMINGOS - There are no infinities, utility functions, neurosymbolic

Machine Learning Street Talk (MLST)

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Exploring Compositionality and Systematicity in AI

This chapter examines the significance of compositionality and systematicity in symbolic representations and vector space embeddings, introducing TensorLogic as a proposed solution for integrating these concepts into AI applications. It highlights the complexities of machine learning, contrasting discrete and continuous methodologies while emphasizing the importance of unifying symbolic AI and deep learning approaches for enhanced intelligence. The discussion also addresses the role of abstraction, symmetry, and the foundational principles of group theory in refining AI's recognition capabilities and improving learning processes.

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