A designed language is very precise so that thoughts formulated in it are universally understood – everyone will know exactly what you mean, with no place for misinterpretation. A disadvantage of this, I think, is limiting the ability to use metaphors, basically limiting human creativity – you end up with a clear language but one that lacks the “vibrancy” of a less clear language.

Consider Stephen Wolfram’s iterations of his computational language over the past thirty years. In this course, he contrasts human language evolution with computational language design. On the process of creating a computational language, he states:

… by designing the computational language, you are controlling the way that people choose to think about things.

What are the advantages and consequences of having a computational language that is designed?

Consistency v restrictions and boundaries

Interesting

Advantages of human languages designing the computational ones are it is not the other way around.

When computers start telling us what to speak, the slavery ethic will be returned with a vengence.

It is better we decide.

helping people while eventully replacing people

The consequences is that the creator of the computational languange can choose whatever they wishes to do with the languange. The advantage is that depending on what the creator want to do with the language it might help us to easily do a task.

Designing a computational language, like Stephen Wolfram’s efforts with his computational system, involves creating a structured and systematic way for humans to interact with and express concepts in a computational environment. This has several advantages and consequences:
Advantages of Designed Computational Language:
Precision and Clarity: A designed computational language can enforce clear and precise communication. It eliminates ambiguity and allows for unambiguous interpretation of commands and expressions, which is crucial in fields like mathematics, programming, and technical disciplines.
Efficiency: A well-designed computational language can streamline complex operations into concise commands. This improves productivity and reduces the cognitive load required to perform certain tasks.
Consistency: A consistent syntax and set of rules in a computational language make it easier for users to learn, remember, and apply the language. This consistency also facilitates collaboration, as everyone follows the same guidelines.
Automation and Reproducibility: Computational languages are inherently machine-readable. This allows for automation of tasks and ensures that processes can be easily reproduced without human errors.
Domain-Specific Adaptation: Designed computational languages can be tailored to specific domains, making them more effective for particular applications. This specialization can lead to more efficient problem-solving in those areas.
New Modes of Thinking: As Stephen Wolfram mentioned, designing a computational language influences how people think. A computational language can encourage users to think algorithmically, logically, and in terms of data structures, leading to new problem-solving approaches.
Consequences of Designed Computational Language:
Limitations: A designed computational language might excel in specific areas but might not be as versatile as natural human language. It can struggle with expressing abstract or subjective concepts that human languages handle more fluidly.
Learning Curve: Learning a new computational language can be challenging, especially for those unfamiliar with programming or mathematical notations. The structured syntax and rules might deter some potential users.
Dependency: Users of a designed computational language can become heavily reliant on its features and functions. This might limit their ability to work in environments that don’t support the same language.
Rigidity: The very design that provides precision and clarity can also impose rigidity. If a particular computation or operation is not directly supported by the language, users might face difficulties in finding workarounds.
Innovation and Adaptation: A computational language’s design might not easily accommodate new paradigms or ideas. Adapting the language to changing technological landscapes can be a complex and time-consuming process.
Standardization Challenges: If there are multiple competing computational languages, interoperability and standardization can become issues. Collaboration and communication across different language ecosystems might be hindered.
In essence, designed computational languages offer structured and systematic means of interacting with computational systems, enhancing precision, efficiency, and consistency. However, they also come with limitations and challenges that need to be carefully considered. The choice of whether to use a designed computational language depends on the specific context, objectives, and trade-offs involved.

We see this in many cultures and countries, they have different languages and very different approach to life. Some languages have inbuilt work to describe some complicated situations and some languages do not. Languages change over time to include more situations and words to describe concepts.

The natural languages are efficient in communicating very basic survival ideas but to communicate complex concepts like economic condition of a particular region or country, they fail.

Almost all of the problems that you see in the world are due to this miscommunication due to natural languages.

There is no way to do physics by just using English. You have to have equations to describe this complicated information like probability densities.

Natural languages like English are easily misinterpreted. Movie say examples of this in the news where they manipulate the words and exaggerate the situations. We often say that they don’t show both sides of the argument. This is due to the limitations of natural language to describe complicated situations like war etc.

I do believe that the Wolfram language has a lot of potential. We wouldn’t have to rest massive amounts of time to to communicate our thoughts and we would have ai do this trivial tasks that just take a lot of time and energy.

I bet we’re going to hear more about Wolfram language everywhere in the future.

Crafting a computational language, akin to Stephen Wolfram’s endeavors with his computational framework, entails establishing a structured and methodical approach for humans to engage with and articulate concepts within a computational realm. This yields several benefits and ramifications:

Benefits of a Designed Computational Language:

Precision and Clarity: A meticulously crafted computational language ensures clear and precise communication, eliminating ambiguity and enabling unambiguous interpretation of commands and expressions. This is particularly vital in domains like mathematics, programming, and technical disciplines.

Efficiency: A well-designed computational language can condense complex operations into succinct commands, enhancing productivity and reducing the cognitive burden required for certain tasks.

Consistency: A uniform syntax and set of regulations in a computational language facilitate ease of learning, retention, and application for users. Such consistency also fosters collaboration, as it ensures adherence to the same standards by all parties.

Automation and Reproducibility: Computational languages inherently lend themselves to machine-readability, enabling task automation and ensuring processes can be replicated without human error.

Domain-Specific Adaptation: Tailoring designed computational languages to specific domains enhances their effectiveness for particular applications, fostering more efficient problem-solving within those spheres.

Encouraging New Modes of Thinking: As highlighted by Stephen Wolfram, the design of a computational language shapes users’ cognitive processes, encouraging algorithmic, logical, and data-oriented thinking and fostering novel problem-solving approaches.

Consequences of a Designed Computational Language:

Limitations: While excelling in specific domains, a designed computational language may lack the versatility of natural human languages, struggling to articulate abstract or subjective concepts that human languages handle with greater fluidity.

Learning Curve: Mastering a new computational language can pose challenges, particularly for individuals unfamiliar with programming or mathematical notations, given the structured syntax and rules involved.

Dependency: Users of a designed computational language may become heavily reliant on its features and functions, potentially limiting their adaptability in environments that do not support the same language.

Rigidity: The very precision and clarity provided by the design of a computational language can introduce rigidity, complicating matters when a particular computation or operation is not directly supported, necessitating workarounds.

Innovation and Adaptation: Evolving technological landscapes may pose hurdles in adapting a computational language to accommodate new paradigms or ideas, necessitating complex and time-consuming adjustments.

Standardization Challenges: In cases of multiple competing computational languages, interoperability, and standardization may become issues, impeding collaboration and communication across disparate language ecosystems.

In summary, designed computational languages offer structured and systematic means of engaging with computational systems, enhancing precision, efficiency, and consistency. Nonetheless, they also entail limitations and challenges that warrant careful consideration. The decision to adopt a designed computational language hinges on specific contexts, objectives, and associated trade-offs.