Sample paper
University: Federation University Australia
Word Count: approximately 2,700 words
Language Name
The proposed language is named IntelliLang, reflecting its purpose of enabling the construction of intelligent systems.
Introduction and Language Purpose
IntelliLang is proposed as a language purpose-built for AI researchers, data scientists, and developers, addressing gaps in expressiveness, performance, and safety left by general-purpose languages currently favoured for AI work (Python, C++, Java). It offers first-class support for tensors, automatic differentiation, probabilistic programming, and neural architecture search, aiming to reduce boilerplate through clean syntax backed by a strong standard library for data preprocessing, model training, and hyperparameter tuning. Performance is addressed through optimisations targeting GPUs and TPUs, while safety is addressed through a first-class static type system that catches common errors at compile time, with future scope for formal verification and model checking. The language supports both imperative and functional paradigms and is intended to be open-source and cross-platform.
Interpretation/Compilation Methods
IntelliLang uses a hybrid interpretation/compilation approach. In interpreted mode, a REPL environment supports exploratory AI programming -- testing ideas, inspecting model state, and visualising outputs -- alongside scripting and notebook interfaces familiar to data scientists. Compiled code targets native machine code via LLVM, applying AI-specific optimisations including automatic parallelisation, SIMD vectorisation, GPU kernel fusion, and graph-based neural network optimisations, with a stated goal of matching C++ performance. The interpreter and compiler implement the same language semantics, providing a clean progression from experimentation to production, and code additionally compiles to WebAssembly for browser and cross-platform portability, with WASM's sandboxed execution model offering an additional security benefit.
Memory Management and Scoping
Memory management defaults to a parallel, generational garbage collector designed for low latency and high throughput, using generational collection to avoid full-heap pauses and concurrent/incremental collection to reduce latency further. For real-time applications, IntelliLang additionally offers memory pools for manual management, RAII types for deterministic resource cleanup, and ownership types for statically enforced memory safety. Scoping is primarily lexical, supporting encapsulation, modularity, and closures, with an optional dynamic scoping mode -- lexically controlled to avoid unwanted context leaks -- supporting context-sensitive behaviour such as runtime hyperparameter or activation-function changes in large neural networks. To support safe concurrency, IntelliLang bans shared mutable state, favours pure functions and immutability by default, and adopts a Rust-inspired ownership and borrowing model with static type checking for parallel programming.
Language Features
Simplicity: IntelliLang aims for a concise, expressive syntax with minimal boilerplate, backed by a coherent set of orthogonal features rather than the piecemeal feature accumulation seen in some mainstream languages.
Orthogonality: Core building blocks -- functions, types, modules -- are designed to combine without edge cases. Functions are first-class values supporting higher-order functions and decorators, and a small set of core constructs (algebraic data types, pattern matching) underlies all built-in types, giving the language a unified, consistent foundation.
Data Types: Built-in types span booleans, integers, floats, characters, strings, null, arrays, ranges, tuples, records, tagged unions, function types, and modules, with first-class multidimensional arrays supporting vectorised tensor computation central to neural network programming.
Syntax Design: IntelliLang favours familiar, low-clutter syntax: significant whitespace (as in Python) to reduce delimiter noise in nested AI code, distinct operators for immutable (let x = 1) and mutable (x := 1) bindings, minimal-ceremony function definitions, dotted-notation field access, and compact record/union construction syntax for ML-style data modelling.
Support for Abstraction: Abstraction tools include first-class functions (enabling training-loop abstractions parameterised over models, data loaders, and optimisers), a hierarchical module system with visibility control, interfaces/traits for polymorphism over disparate types, and metaprogramming for macros, decorators, and dynamic neural architecture generation, balanced against safety.
Expressivity: The language is functional-first (map/reduce, pipelining, immutability) but multi-paradigm, supporting imperative and object-oriented styles. Tensors, dimensions, and batched computation are first-class constructs with dedicated indexing/slicing syntax, and the type system favours gradual typing with union/intersection types and first-class tensor-shape types, alongside dedicated probabilistic programming facilities for sampling, inference, and marginalisation.
Type Checking: IntelliLang employs gradual typing, allowing dynamic type checking for prototypes and exploratory analysis, with progressive static typing as systems mature toward production, catching bugs such as tensor shape mismatches. Its static type system is sound and decidable, based on Hindley-Milner type inference.
Exception Handling: Exceptions are distinguished from expected error conditions, which are instead modelled with Result-style union types; the throw/catch mechanism is stack-based for locality between throw sites and handlers, and the compiler statically warns about undocumented thrown exceptions, keeping exceptions reserved for genuinely exceptional cases with no overhead when unused.
Restricted Aliasing: Variables are immutable by default, and mutable data is managed through three tools modelled on Rust's ownership system -- unique pointers (single mutable reference), borrowed references (temporary, scope-limited aliasing), and shared read-only references (simultaneous access without mutation) -- statically preventing data races, iterator invalidation, and aliasing hazards specific to AI workloads such as concurrent tensor access.
Readability, Writability, and Reliability
IntelliLang's clean, low-noise syntax and orthogonal semantics support readability by minimising the corner cases and feature interactions a reader must track. Writability is supported by the REPL, notebook integration, and scripting support for rapid iteration, balanced against strong static typing and immutability that surface common AI mistakes (tensor shape mismatches, use-before-initialisation) early via IDE feedback, with a deliberately limited feature surface aiding learnability. Reliability is the language's central goal: sound typing eliminates type-related runtime bugs, automatic memory management and restricted aliasing prevent memory-safety failures, and exception handling supports graceful failure during training or inference. Modular design, lexical scoping, and controlled side effects support building large systems from small, locally understandable components, while the hybrid interpreter/compiler architecture allows debug builds to insert runtime checks for AI-specific failure modes (numerical instability, poor convergence) that release builds can strip for performance, with fleet-wide logging and metrics supporting empirical reliability analysis of deployed systems.
Conclusion
IntelliLang is presented as a from-scratch language designed to make AI development more comprehensible, sustainable, and less error-prone through careful attention to readability, writability, and reliability. Its multi-paradigm approach, metaprogramming support, hybrid compiler architecture, first-class tensor and automatic-differentiation support, and restricted aliasing model are argued to jointly deliver both high performance and safety, distinguishing it from general-purpose languages retrofitted for AI use as the field continues to grow.
References
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., et al. (2016). TensorFlow: a system for large-scale machine learning. 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16) (pp. 265-283). Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A. (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145. Innes, M. (2018). Flux: Elegant machine learning with Julia. Journal of Open Source Software, 3(25), 602. Khodadadi, A., Hosseini, S. A., Pajouheshgar, E., Mansouri, F., & Rabiee, H. R. (2020). ChOracle: A unified statistical framework for churn prediction. IEEE Transactions on Knowledge and Data Engineering, 34(4), 1656-1666. Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., et al. (2019). PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, 32. Shazeer, N., Cheng, Y., Parmar, N., Tran, D., Vaswani, A., Koanantakool, P., et al. (2018). Mesh-TensorFlow: Deep learning for supercomputers. Advances in Neural Information Processing Systems, 31.