Programming Paradigms in AI represent fundamental styles and approaches to structuring code for artificial intelligence systems. Key paradigms include imperative (procedural, object-oriented), declarative (functional, logic-based), and differentiable programming, each shaping how models are specified, composed, and optimised across symbolic and neural approaches.
Semantic Classification
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Key Characteristics
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Supports functional composition and immutability
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Enables declarative model specification
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Facilitates automatic differentiation throughout programs
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Encourages modular and reusable code structures
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Integrates symbolic and numeric computation
Overview
Programming Paradigms in AI represent fundamental styles and approaches to structuring code for artificial intelligence systems. Key paradigms include imperative (procedural, object-oriented), declarative (functional, logic-based), and differentiable programming. Functional programming (Haskell, Lisp) emphasizes immutability and higher-order functions, suited for mathematical ML algorithms. Differentiable programming treats entire programs as differentiable functions, enabling end-to-end gradient-based optimization. Modern AI development combines paradigms: object-oriented for software engineering, functional for mathematical rigor, and differentiable for learning.
Related Concepts
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References
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Innes, M. (2018). Don’t Unroll Adjoint: Differentiating SSA-Form Programs. arXiv:1810.07951.
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Van Roy, P. & Haridi, S. (2004). Concepts, Techniques, and Models of Computer Programming. MIT Press.
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Baydin, A. et al. (2018). Automatic differentiation in machine learning: a survey. JMLR 18(153), 1-43.