The Quest for Computational English: Precision, Lock-in, and the Programming Language Paradox
The intriguing proposition of transforming English into a computational language, akin to LaTeX, to guarantee precision and establish a universal standard for formal communication raises several fundamental questions about language design, utility, and global adoption. This concept envisions a future where complex ideas are documented in sentences that not only convey information but also embed executable algorithms, making understanding reliant on computation rather than just reading.
The Concept of Computational English
At its core, this idea aligns with what is formally known as controlled natural language with executable semantics (CNL). CNLs are subsets of natural languages with restricted grammars and vocabularies, designed to be unambiguous and machine-readable, often allowing for automatic interpretation or execution. Projects like Attempto Controlled English (ACE) and ClearTalk are practical examples that explore how natural language can be rigorously constrained to achieve computational precision. The goal is to eliminate ambiguity, ensuring that text means exactly what it says, much like mathematical notation.
The Programming Language Parallel
As discussions around this concept evolve, a recurring theme emerges: the pursuit of extreme precision in language often leads to structures that resemble programming languages. Professional jargon, for instance, serves a similar purpose within specific domains, tightening definitions and reducing ambiguity. However, when the requirement shifts to embedding non-trivial computations, the language inevitably begins to adopt characteristics of a formal system. Any significant effort to refine English to act like a programming language will, by its very nature, result in the creation of a new programming language, albeit one syntactically inspired by English.
Turing Completeness and Interoperability Challenges
An important technical consideration brought forward is the concept of Turing completeness. A language or system is Turing-complete if it can simulate any arbitrary computation. While immensely powerful (LaTeX, for example, is Turing-complete, allowing for highly sophisticated typesetting), this power comes with a significant drawback: Turing-complete formats are inherently difficult, if not impossible, to translate flawlessly into other formats. This is because the full range of possible computations might not have direct equivalents in another system. For a computational English aiming for universality, this poses a serious challenge, as it could prevent seamless translation into other languages or document formats, potentially hindering adoption rather than ensuring it.
Precision vs. Naturalness: The Inherent Trade-off
Perhaps the most fundamental insight from this discussion is the inherent trade-off between computational precision and the qualities that make natural languages effective for general human communication. English, like any natural language, thrives on ambiguity, nuance, and flexibility. These characteristics, while problematic for computational rigor, are crucial for empathy, creativity, and the vast spectrum of human expression. To make English suitable for use as a programming language, one would need to constrain it so tightly that it would inevitably become less suitable for broad human communication. The more precise a language becomes for machines, the less 'natural' it may feel for humans, creating a tension between its potential for lock-in via exact meaning and its ease of learning and use for everyday interaction.
Ultimately, while the vision of a computational English for universal formal communication is compelling, its realization faces significant hurdles rooted in established linguistic and computational principles. The debate highlights that choosing a language for precision often means sacrificing naturalness, and that powerful computational capabilities can come at the cost of interoperability.