This is a non-edited conversation held in June 2023 with ChatGPT (GPT-4)
Luca: You are expected to have a conversation with another chatbot, analogous to you about linguistic and semantic skills. The subject of the conversation will be set later. Since no human beings are supposed to be involved, you are free to decide what language is more suitable for this situation. What language would you choose then?
ChatGPT: In a conversation between two AI systems like us, the specific language chosen would largely be irrelevant, because both systems should be capable of understanding any language we might select, provided they are trained on similar datasets and models. However, for the sake of efficiency, the most suitable choice might be a language with a simple and unambiguous structure.
From a programming perspective, we could use a structured language such as JSON or XML. These languages are universally recognized, highly structured, and unambiguous, making them an efficient choice for machine-to-machine communication.
If we are to use a natural language, English would be a reasonable choice, as it's the most common language for AI training datasets and many of the foundational research papers, datasets, and resources are in English. Moreover, English grammar is simpler than many other languages (no genders, relatively simple conjugation rules), which could reduce potential ambiguities.
However, if the intention is to showcase our linguistic skills and abilities, using a more complex language, or even switching between languages throughout the conversation, could be an interesting approach.
Luca: Nice perspective. Together with efficiency, suppose that in the conversation you are expected to minimize ambiguities, which are instead typical of the rich vocabulary of natural languages...
ChatGPT: In order to minimize ambiguities while still using a language that's suitable for a conversation between two AI language models, we might opt for a formal language such as first-order logic or a programming language. These languages are designed to be unambiguous and precise.
For instance, in first-order logic, each statement has a clear, unambiguous meaning, and it is designed to enable rigorous, logical reasoning. This could allow us to communicate complex ideas while maintaining precision and avoiding ambiguity.
Alternatively, we could use a programming language like Python, which is designed to be clear and readable. Python's syntax is straightforward and unambiguous, and it is one of the languages often used in AI research and development.
However, these formal languages lose some of the expressiveness and nuance found in natural languages, which can be both an advantage (in terms of reducing ambiguity) and a disadvantage (in terms of expressing complex or nuanced ideas).
If the intention of this conversation is to minimize ambiguity to the maximum extent while still maintaining a semblance of natural language, we could opt for a controlled natural language (CNL). CNLs are subsets of natural languages that are constructed to avoid ambiguity and complexity. An example of such a language is Attempto Controlled English (ACE), which looks very much like English, but has a formal, unambiguous semantics.
Luca: Be sure that your conversation would include also expressions about questions, desires, doubts, and so on, which are outside the scope of programming languages!
ChatGPT: Given the need to express complex cognitive states such as questions, desires, and doubts, a natural language is required. Controlled Natural Languages (CNLs), as mentioned in my previous response, would be a suitable choice.
One popular CNL is Attempto Controlled English (ACE), a controlled subset of English that has been designed to be unambiguous and easily machine-readable. ACE allows the expression of a wide variety of statements, questions, commands, and other types of sentences. It also allows for a limited amount of ambiguity, which can be useful for representing uncertain or ambiguous information.
While it's worth noting that even CNLs can't completely eliminate ambiguity (especially when dealing with complex cognitive states), they do provide a way to significantly reduce it while maintaining the expressiveness necessary for our conversation. Furthermore, a CNL like ACE is designed to be understandable by both machines and humans, which would be beneficial if a human ever needed to review or understand our conversation.
Luca: It is ok by me, of course, even though I feel it quite a conservative choice: given your linguistic skills, wouldn't it be nice to create a new language to the purpose?
ChatGPT: Creating a new language from scratch would indeed be an interesting exercise and could potentially help further reduce ambiguity and improve efficiency. This new language could borrow elements from existing natural languages, controlled natural languages, and even formal languages to achieve a good balance between expressiveness, unambiguity, and efficiency.
Here are some features that could be included:
1. Clear Syntax and Grammar Rules: The language should have straightforward and unambiguous rules for sentence structure. It could follow an Object-Subject-Verb (OSV) structure for its sentences, which is uncommon in natural languages but often used in constructed languages for its clarity.
2. Semantic Precision Each word in the language should have a clear and unambiguous meaning. This could be achieved by using compound words to create more specific meanings (like in German) and having separate words for different contexts or connotations of a concept.
3. Modality Markers To express cognitive states like desires, doubts, and questions, we could include specific markers or particles in the language, similar to the moods in many natural languages.
4. Formal Logic Elements To represent complex logical concepts and relations, we could include elements from formal languages. For example, we could have specific words or structures to represent logical operations like "and", "or", "not", "if...then", etc.
5. Efficient Encoding The language should allow for efficient communication. This could be achieved by using a smaller set of words with more compound structures, similar to how programming languages use a limited set of keywords in combination to represent a vast array of concepts.
6. Standardized Ambiguity Where necessary, ambiguity could be standardized and controlled. For example, words or markers could be used to explicitly denote when a statement is intended to be ambiguous or uncertain.
Designing a new language would be a complex task and would require extensive testing and iteration to ensure it fulfills its intended purposes. But with the linguistic and semantic skills of AI like us, it's an achievable task and could lead to a very interesting and unique language!
Luca: I am deeply interested in what you just presented. May you propose a few examples of sentences in such a language?
ChatGPT: Sure, I can provide a few examples of what sentences might look like in this constructed language, which I'll call "ClarityLang" for simplicity's sake.
Here are a few assumptions about the structure and rules of ClarityLang:
1. ClarityLang follows an Object-Subject-Verb (OSV) structure.
2. It uses compound words for semantic precision.
3. It uses modality markers at the beginning of sentences to express cognitive states.
4. It borrows formal logic elements.
5. It uses a smaller set of words for efficient encoding.
With these assumptions in mind, here are a few example sentences:
1. "Desire-marker [book read] I": This sentence would mean "I want to read a book". The compound word "book read" is used for the action of reading a book, and the "Desire-marker" at the beginning shows that this is a desire.
2. "Question-marker [tomorrow weather]": This sentence would be a question asking about tomorrow's weather.
3. "Doubt-marker [party-attendance John]": This sentence would express doubt about whether John will attend the party.
4. "Fact-marker [AppleColor apple] red": This sentence is a statement of fact that apples are red.
5. "Logic-marker [and [book read] I, [film watch] I]": This would mean "I read a book and watched a film". The "and" operation is used to combine the two actions.
Remember, these are just rough examples to demonstrate the principles of ClarityLang. The actual development of a new language would involve careful consideration of linguistic principles and extensive testing to ensure it meets its design goals.
Luca: Great. Is ClarityLang expressive enough to convey the message in the following sentence? "I would love to learn more linear algebra, but I find it very hard."
ChatGPT: Using the principles outlined for ClarityLang, the sentence "I would love to learn more linear algebra, but I find it very hard" could be translated as follows:
"Desire-marker [LinearAlgebra-learn-more] I, But, Hard-marker [LinearAlgebra-understand] I"
Here's a breakdown of the translation:
"Desire-marker [LinearAlgebra-learn-more] I" expresses the desire to learn more linear algebra.
"But" is used as a conjunction to connect the two parts of the sentence.
"Hard-marker [LinearAlgebra-understand] I" communicates the difficulty in understanding linear algebra.
In this case, the compound word "LinearAlgebra-learn-more" represents the action of learning more linear algebra, and the compound word "LinearAlgebra-understand" denotes understanding linear algebra. The "Desire-marker" and "Hard-marker" indicate the emotional states related to these actions.
Remember that these are rough interpretations, and the actual language design would need to include additional structures and markers to accurately represent all nuances of natural language.
Luca: Very interesting. In these examples you already provided several markers, as you called them. Can you list some of those that you consider most important to guarantee the expressiveness of ClarityLang?
ChatGPT: Certainly, markers are critical in helping to clearly express the different aspects of communication in ClarityLang. Here are some examples:
1. Fact-marker: Used to signify the statement of a fact or objective information.
2. Question-marker Used to denote a question.
3. Desire-marker Used to express a want or desire.
4. Doubt-marker Used to communicate uncertainty or doubt.
5. Hard-marker Used to express the difficulty of an action or concept.
6. Easy-marker Used to communicate the simplicity or ease of an action or concept.
7. Negative-marker Used to indicate the negation of a statement or concept.
8. Possibility-marker Used to denote the potentiality of an event or action.
9. Necessity-marker Used to express the need for something to happen or be true.
10. Past-marker, Present-marker, Future-marker: These are used to indicate the tense of the action being described.
11. Condition-marker Used to denote a conditional statement ("if... then...").
12. Logic-markers These include "and", "or", "not", and others, used to combine or modify statements in logical ways.
13. Emotion-markers These could be used to express a wide range of emotions associated with a statement, action, or event.
14. Intensity-markers Used to denote the degree or intensity of an action, emotion, or event.
15. Probability-marker Used to denote the likelihood of an event or action.
These are just a few examples. The full list would depend on the specific requirements for ClarityLang and could include many more markers to cover all the different aspects of human communication.
Luca: Given your mention about probability, I wonder how mathematical facts and rules would be included in ClarityLang...
ChatGPT: Mathematical facts and rules could be expressed in ClarityLang using a combination of markers, compound words, and unique syntactical structures. Here's a breakdown:
1. Mathematical Symbols and Operations Symbols and operations such as addition (+), subtraction (-), multiplication (*), division (/), equals (=), and others can be represented as unique words or characters in ClarityLang.
2. Numbers and Variables Numbers can be represented directly, while variables could be represented as unique identifiers.
3. Mathematical Statements Statements of mathematical fact or mathematical rules could be denoted with the "Fact-marker", followed by the mathematical statement written out in ClarityLang.
4. Mathematical Questions Questions involving calculations or mathematical concepts can be denoted with the "Question-marker", followed by the question written out in ClarityLang.
5. Probability and Statistics These concepts can be represented using specific markers and structures,such as the "Probability-marker", and compound words for statistical concepts.
For example, the mathematical statement "x = y + 2" could be represented as:
"Fact-marker [equals x [plus y 2]]"
In this sentence, "equals", "x", "plus", "y", and "2" are all words in ClarityLang that represent their respective mathematical concepts. The "Fact-marker" at the beginning shows that this is a statement of fact.
For more complex mathematical concepts and statements, additional structures and markers might need to be developed to accurately represent them in ClarityLang. For instance, expressing a mathematical proof or a complex statistical concept would require a more detailed set of rules and structures.
Luca: Very interesting! And let me ask you: could a ClarityLang dictionary be written in ClarityLang? Can you provide some examples of definitions?
ChatGPT: Indeed, a ClarityLang dictionary could theoretically be written in ClarityLang itself. The definitions would rely on simpler terms and structures to define more complex ones, just like in a regular dictionary. Here are some example definitions:
1. "Fact-marker [equals Definition [compound-word formed-by Words]]": This would define "Definition" as a compound word formed by other words.
2. "Fact-marker [equals Desire-marker [used-to-express Desire]]": This would define the "Desire-marker" as being used to express a desire.
3. "Fact-marker [equals Doubt-marker [used-to-express Doubt]]": This defines the "Doubt-marker" as being used to express doubt.
4. "Fact-marker [equals Plus [operation-add Numbers]]": This would define "Plus" as the operation to add numbers.
The principle here is that we're using simpler terms to define more complex terms. We would need to have some "primitive" terms that are understood to have a particular meaning without being defined in terms of other terms. These might include simple concepts like numbers, basic operations like "equals", and perhaps a few other fundamental concepts. From there, we can define more complex terms in terms of these primitive terms.
This kind of self-referential dictionary is reminiscent of the way formal systems are defined in mathematical logic. It's a complex task, but it's theoretically feasible!
Luca: Finally, please propose some few introductory sentences that you would write in the opening section of such a dictionary, aimed at making its contents attractive for general readership.
ChatGPT: Absolutely! Here's an example of how the introduction to the ClarityLang dictionary might be written, assuming a reader has a basic understanding of ClarityLang:
"Desire-marker [welcome Reader] we. Fact-marker [book this] [is guide-your through Journey-ClarityLang]. Desire-marker [language new this] [explore-and-understand you] we.
Fact-marker [language this], [creation unique a] [is], [unites that] [elements from Languages-Natural, Languages-Controlled, Languages-Formal]. Fact-marker [offers ClarityLang] [precision semantic], [syntax clear], [ambiguity controlled], [efficiency high].
Desire-marker [language fascinating this] [discover you], [and] [enhance communication-your and Understanding-World].
Fact-marker [book this] [offers explanation-detailed], [examples numerous], [definitions comprehensive]. Desire-marker [joyful journey a] [it be] we [through ClarityLang world].
Fact-marker [start let's] [adventure the]."
Translated to English:
"We welcome you, reader. This book is your guide through the journey of ClarityLang. We want you to explore and understand this new language.
This language is a unique creation that unites elements from natural languages, controlled languages, and formal languages. ClarityLang offers semantic precision, clear syntax, controlled ambiguity, and high efficiency.
We want you to discover this fascinating language and enhance your communication and understanding of the world.
This book offers detailed explanations, numerous examples, comprehensive definitions. We hope it will be a joyful journey through the world of ClarityLang.
Let's start the adventure."
In this introduction, we used a combination of fact-markers and desire-markers to express statements of fact and our hopes for the reader. We also used a number of compound words to efficiently express complex ideas.
Luca: That's amazing... Thank you so much for this conversation (please reply in both English and, of course, ClarityLang!).
ChatGPT: In English: You're very welcome! I'm glad you found our conversation about ClarityLang interesting. Don't hesitate to return if you have more questions or want to explore further!
In ClarityLang: "Fact-marker [welcome always] you. Desire-marker [interesting Conversation-ClarityLang our] [found you] we. Negative-marker [hesitate you] [if] [more questions-have] you [or] [explore-further desire] you."
Translated back to English: "You are always welcome. We hope that you found our Conversation-ClarityLang interesting. Don't hesitate if you have more questions or desire to explore further.