A formal notation system used to specify algorithms, data structures, and computational models for AI and software applications, characterised by defined syntax and semantics; prominent examples include Python for machine-learning ecosystems, Julia for high-performance numerical computing, and domain-specific languages embedded in ML frameworks.
Semantic Classification
Content
Key Characteristics
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Provides high-level abstractions for ML algorithms
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Supports interactive development and rapid prototyping
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Integrates with numerical libraries and accelerators
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Offers strong typing and type inference for safety
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Facilitates parallelism and distributed computing
Overview
Programming Languages for AI are formal languages designed to express algorithms, models, and computations for artificial intelligence applications. Popular languages include Python (dominant in ML/DL ecosystems), Julia (high-performance numerical computing), R (statistical analysis), and domain-specific languages like TensorFlow’s graph definition language. Key features include support for tensor operations, automatic differentiation, GPU acceleration, functional programming paradigms, and integration with ML frameworks. Modern AI languages emphasize readability, expressiveness, performance, and ecosystem richness.
Related Concepts
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References
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Van Rossum, G. & Drake, F. (2009). Python 3 Reference Manual. CreateSpace.
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Bezanson, J. et al. (2017). Julia: A Fresh Approach to Numerical Computing. SIAM Review, 59(1), 65-98.
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Lattner, C. et al. (2020). MLIR: A Compiler Infrastructure for the End of Moore’s Law. arXiv:2002.11054.