Sample paper
University: Federation University Australia
Word Count: approximately 3,400 words
Language Name
The proposed language is named AIScript.
Introduction and Language Purpose
AIScript responds to the drawbacks general-purpose languages present when applied to AI development, proposing a language tailored specifically to AI requirements. Its stated goals are to be easy to learn for AI practitioners, expressive and concise for AI algorithms, performant and scalable for large AI models, safe and reliable for mission-critical AI systems, and extensible and flexible for new AI techniques as the field evolves. The corresponding benefits are framed around productivity and accessibility: allowing AI experts to express ideas efficiently, improving engineer productivity, enhancing system performance, accelerating iteration cycles for novel algorithms, and reducing the barrier to entry for new researchers and students. By realising these goals, the essay argues, AIScript would let AI developers focus on the essential complexity of AI problems rather than the accidental complexity introduced by their tools, positioning the language as a foundational pillar for the field's continued advancement.
Interpretation/Compilation Methods
AIScript adopts a hybrid interpretation/compilation approach, combining interpreter interactivity with compiler performance. Early in development, a standard REPL lets a programmer run script samples, inspect tensor values, plot model metrics, and step through program execution -- all via an interpreter that runs AIScript code directly without requiring upfront compilation. This interactive workflow is positioned as critical to AI development, where testing ideas and exploring intermediate results quickly is central to the research process; a separate compilation path is then available to optimise code for deployment once a system is ready for production use.
Memory Management, Scoping, and Language Features
Following the same essay structure used across this course's language-design assignments, AIScript's remaining specification addresses memory management and scoping, and the nine major language features -- simplicity, orthogonality, data types, syntax design, abstraction support, expressivity, type checking, exception handling, and restricted aliasing -- evaluating each against AIScript's dual goals of research-friendly interactivity and production-grade performance and safety. The essay closes by assessing how these design choices jointly support readability, writability, and reliability for AI codebases, arguing that a language purpose-built for AI's specific demands -- rather than one adapted from general-purpose origins -- offers a more coherent foundation for the field's continued growth.
Conclusion
AIScript is presented as a language designed from the ground up to serve AI practitioners' dual need for rapid experimentation and production-grade reliability. By tailoring its interpretation/compilation strategy, memory model, and core language features specifically to AI development patterns, the essay argues AIScript would reduce the accidental complexity AI researchers currently face with general-purpose tools, accelerating iteration cycles and lowering the barrier to entry for new contributors to the field.
References
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