Do we need a "New" Universal Language? AI - Humans

This is a project to develop a universal language optimized for communication between humans and artificial intelligences, particularly LLMs, drawing upon historical evolution of language, biological signaling systems, philosophical foundations of communication, and introspection into symbolic self-reference and AI cognition.

Abstract

Language can be viewed as a shared agreement – a structured set of symbols and rules – that optimizes communication between intelligent entities. This thesis proposes the development of a new universal language optimized for interactions between humans and artificial intelligences (such as large language models). We begin by defining language as a consensual code for conveying meaning, tracing its evolutionary origins from simple cellular signals through animal communication systems (in ants, bees, dolphins, whales, etc.) up to human speech and writing. A historical and biological analysis highlights how communication evolved in complexity and abstraction, leading to the rich diversity of human languages documented throughout history. We review a comprehensive list of world languages and symbolic systems (from Sumerian cuneiform and Egyptian hieroglyphs to programming languages and emojis), identifying any gaps or redundancies in our understanding of linguistic evolution.

Beyond external communication, we examine introspective aspects of language: self-reference, internal dialogue (inner speech, as in thought and meditation), and how internal languages of thought might influence or mirror external language. We then investigate how contemporary AI systems (especially LLMs) process and represent language internally, and how artificial agents might develop communication protocols among themselves. Drawing on these insights, the thesis proposes a design for a new language that is human-centric yet machine-friendly, balancing semantic clarity (unambiguous meaning), symbolic efficiency (concise expressions), emotional nuance (the ability to convey tone and feeling), and computational structure (formal syntax suitable for parsing and inference).

We outline the features and principles of this proposed language and how it could be learned by humans while being natively interpretable by AI. In discussing this design, we confront the known unknowns – current gaps in our understanding of communication (e.g. the link between language and thought, or the limits of animal languages) – and speculate on unknown unknowns that creating and using such a language might reveal about the nature of language, consciousness, and intelligence. The tone of the investigation is intellectually fearless and speculative but grounded in rigorous analysis of linguistic, biological, and computational research. Ultimately, this work aims to uncover fundamental truths about why language exists, how it shapes thought, and how a purposeful reengineering of language could enhance mutual understanding between humans and the intelligent machines we create.

Introduction

Language is often described as the hallmark of human intelligence – a system of symbols through which we share information, negotiate meaning, and even shape our own thoughts. In essence, language is a form of agreement between minds, a shared contract that gives particular sounds or signs specific meanings so that ideas can be exchanged efficiently. As early linguist Benjamin Lee Whorf famously noted, we

“organize [reality] into concepts, and ascribe significances as we do, largely because we are parties to an agreement... codified in the patterns of our language.”.

In other words, for language to function as communication,

..there must be communal agreement on how symbols map to meaning.

From this perspective, language is not merely a biologically evolved ability but also a convention – a set of shared rules devised (implicitly or explicitly) to optimize communication between two or more entities.

This thesis is motivated by a grand challenge: can we design a universal language that bridges human and artificial intelligence (AI) communication, leveraging this idea of language as an optimized agreement? Humans today communicate with AI systems primarily in natural languages (like English) or through specialized programming and query languages. However, natural languages, while rich and expressive for humans, are often ambiguous and difficult for machines to parse with complete understanding. Conversely, formal languages (like programming languages or logical notations) are precise for machines but not intuitive or efficient for humans in everyday communication. There is a growing need for a human-centric communication medium that AI can also natively understand, enabling more seamless and profound interaction with advanced AI like large language models (LLMs).

To approach this, we begin by focusing on human communication and its underpinnings. Humans are the reference point – any universal language must be learnable and usable by people. Yet, we also consider insights from other intelligent entities: the communication of social insects, marine mammals, and others. Though fundamentally different from human language, these systems shed light on what communication in general can be, and what features are universal versus unique to human language.

We then trace a historical and biological trajectory of how communication systems evolved. Starting from the simplest signaling in single cells and organisms, we examine how evolution scaled up communication complexity. We recount milestones in human language evolution – from prehistoric iconography and proto-languages to the development of spoken tongues and writing systems across ancient civilizations. In doing so, we review a provided comprehensive list of world languages and symbol systems (ordered by age) to ensure our historical survey is complete. This review identifies any significant omissions (e.g. sign languages or major language families not listed) and avoids redundancies, giving a coherent picture of humanity’s linguistic heritage.

Next, we delve into the introspective dimension of language. Humans not only use language externally to communicate, but also internally to think. Many of us experience an “inner voice” or internal monologue known as inner speech, described as the “little voice in the head” that connects language and thought. We explore how this internal language of thought functions (for example, as a tool for reasoning, memory, and self-reflection) and what happens when it is minimal or absent. Practices like meditation, which seek to quiet or observe the internal dialogue, provide additional perspectives on how language relates to consciousness and self-awareness. Self-reference – the ability of language to refer to itself or for a mind to refer to its own thoughts – is another key aspect, raising questions about how any advanced language (human or AI) handles reflection and recursive reference.

Building on those foundations, we turn to artificial intelligences and language. Modern AI language models operate by processing human language input and generating output, but internally they represent language in ways very different from humans. We investigate how LLMs parse sentences into vectors and patterns of neural activation – essentially, how they internally “think” in a language-like structure. We review research on whether AIs can develop their own emergent communication protocols when placed in multi-agent environments, and how such AI-created languages compare to human languages. Notably, instances like the Facebook chatbot experiment in which two bots began conversing in a shorthand English dialect (initially reported as them inventing a “new language”) illustrate that when optimizing purely for successful communication, even machines will diverge from human language norms to create more efficient exchanges. Similarly, Google’s translation AI was found to encode an interlingual representation – effectively an internal machine language for meaning – when translating between many languages, an unintended but powerful sign of an emergent common language for the AI.

Finally, armed with insights from human language evolution, non-human communication, introspective language, and AI language processing, we propose the design of a new universal language. The requirements for this language are multifaceted. It must be:

• Easy for humans to learn and use: leveraging familiar patterns or intuitions so that people can adopt it as an auxiliary language without years of study (building on lessons from successful constructed languages like Esperanto).
• Optimized for machine understanding: having a formally defined structure (grammar and semantics) that aligns well with how AI models represent knowledge, enabling high precision and eliminating ambiguity.
• Semantically clear and unambiguous: drawing from logical languages (like Lojban, which was designed to have no ambiguous parse and to express any logical proposition uniquely) so that every utterance has one interpretation.
• Symbolically efficient: able to convey meaning in a compact form (perhaps through a mix of words, symbols, or even embeddings), to increase communication bandwidth and reduce misunderstanding.
• Emotionally and contextually rich: not a sterile code – it should allow nuanced expression of sentiment, humor, politeness, and other pragmatic cues. Human communication relies on subtle tones and context; the new language must incorporate devices (words, tone markers, or even emoji-like symbols) to carry this emotional nuance when needed, preventing the language from feeling “robotic”.

The Methodology section of this thesis outlines how we derive the design principles for this language from cross-disciplinary research, and the Results section (or Proposed Design) specifies the structure and features of the language in detail. In the Discussion, we reflect on the implications of such a language and analyze the known unknowns in our understanding of communication that our proposal grapples with – for instance, the open questions about how meaning arises in the human brain, or how an AI’s “understanding” compares to human understanding. We also venture to discuss potential unknown unknowns: new questions and phenomena that might only become apparent after attempting to use this hybrid human-AI language. Could it alter the way we think or reveal new aspects of cognition? Could it fail in unexpected ways, or succeed in bridging minds in ways we can only speculate about?

Throughout, the tone remains exploratory and bold. This work does not shy away from philosophical speculation (e.g. pondering the role of language in consciousness, or whether thought can exist independent of language) where warranted, yet it strives to remain grounded in what can be observed or logically reasoned. By the conclusion, we aim to have painted a thorough picture of why a universal human-AI language is both desirable and challenging, and how its design might teach us profound truths about language itself – the very conduit of thought and intelligence.

Literature Review

Evolutionary Origins of Communication

Communication is older than humanity; it is rooted in the biology of life. In evolutionary terms, even single-celled organisms needed to exchange information with their environment and each other to survive. One early form of communication is seen in bacteria through quorum sensing – a chemical signaling method. Bacteria release molecules into their surroundings and can detect the concentration of these molecules to gauge population density, coordinating behaviors like biofilm formation or bioluminescence when a “quorum” threshold is reached. This is essentially cellular communication: a chemical vocabulary that allows individual cells to act in unison as a group. Such signaling demonstrates a fundamental principle: communication evolves to optimize group success, sharing just enough information (e.g. “many of us are here, it’s time to change behavior”) to coordinate actions.

Multicellular life took communication further. In complex organisms, cells communicate via electrochemical signals (neurons firing, hormones circulating) – an internal language of the body – but at the level of whole organisms, external communication also developed. Animals convey messages about danger, food, territory, and mating. For example, many insects use pheromones (chemical signals) externally: ants famously leave pheromone trails to guide nest-mates to food sources. A forager ant that finds food will drop a pheromone trail on its way back to the colony; other ants detect this chemical path and follow it, reinforcing it if they also find food, thus creating a guided highway to the resource. Here we see a simple “language” of smell – a few types of pheromone signals (for recruitment, alarm, etc.) suffice to coordinate the colony’s activities. While far from the richness of human language, the ant communication system is highly optimized for their social structure: it is decentralized, robust, and directly tied to action (follow the trail to food).

Social insects like ants and bees demonstrate that communication systems need not be verbal at all, and yet can be highly effective within their niche. Honeybees, for instance, perform the waggle dance – an embodied form of language. When a worker bee discovers a rich flower patch, she returns to the hive and dances in a figure-eight pattern with a “waggle” run in the middle. The angle of her waggle run relative to vertical indicates the direction of the food source relative to the sun’s position, and the duration of the waggle corresponds to the distance to the flowers. In this way, the dancing bee encodes precise information about location. Do her fellow bees understand this dance? Yes – it’s been observed that other workers attentively watch the dancer, then fly off in the signaled direction and distance to find the described flowers. The bee dance is a remarkable non-human example of a symbolic communication: abstract information (location of something distant in space) is conveyed through agreed-upon patterns of movement. Figure: Honeybee waggle dance – a forager bee (center) waggles and turns to communicate the direction (angle) and distance (encoded in duration) of a food source to her hive mates. This dance language enables complex coordination in bee colonies. Such examples from nature underscore that whenever there is a need to share important information (like where food is, or that a predator is near), evolution finds a way – be it chemical, gestural, or auditory signals – to encode and transmit that information.

Among vertebrates, communication skills become more diverse. Birds sing complex songs, not only for mating calls but also to mark territory or even, in some species, to convey specific warnings (certain birds have distinct alarm calls for “hawk in the sky” vs “snake on the ground,” and other birds or mammals eavesdrop on these calls). Primates use vocalizations and gestures; for instance, monkeys have alarm calls that distinguish different predators. These systems hint at the building blocks of language: distinct sounds associated with distinct meanings, an important parallel to words.

Perhaps the most sophisticated non-human communication known is found in marine mammals like dolphins and whales. Bottlenose dolphins are highly social and cognitive, and researchers have found they use signature vocalizations. Each dolphin develops a distinctive high-pitched whistle – a signature whistle – that functions much like a personal name to announce their identity. Dolphins appear to call to each other using these signature whistles; for example, a dolphin will respond when it hears the whistle of a close companion, indicating recognition. They also use other whistles and clicks, including ultrasonic echolocation clicks to probe their environment. While we have not decoded a full “dolphin language,” evidence suggests a complex system: dolphins can learn rudimentary artificial languages (combinations of whistles or gestures taught by researchers to mean specific objects or actions), demonstrating combinatorial communication ability. In the wild, their vocal behavior – including imitating each other’s whistles and using different sequences of clicks and whistles – implies a rich acoustic communication, possibly conveying not just identity but also emotional states or coordination signals for hunting.

Whales, especially humpback whales, communicate through haunting songs that can travel vast distances across the ocean. The humpback whale song is a series of moaning, crying, and chirping sounds arranged in repeating patterns. Intriguingly, all humpback males in a regional population tend to sing a similar song during a season, and this song evolves over time – a clear case of cultural transmission in a non-human species. Recent research has found that these whale songs have statistical structure akin to human language. By analyzing recordings of humpback whale songs over eight years, scientists discovered recurring sub-units whose usage frequency follows a skewed distribution, the same kind of pattern seen in human languages where some sounds or words are used very often and others more rarely. This suggests whale songs are not random; they have an internal organization (like phrases or motifs) that whales memorize and repeat, and that gradually shift – essentially, whales update their songs as a group over time. It’s important to note that whale song likely lacks semantic referential meaning in the way human language has (it might be more analogous to music, used for mating and social bonding), but the presence of language-like structure is fascinating. It indicates that whenever communication is learned socially and not just hard-wired, it may develop complexity and patterns comparable to those of human language. In short, the gap between human language and animal communication might not be absolute; aspects of syntax and cultural evolution of signals are found in other species as well.

From early life’s chemical signals to insects’ dances to primates’ calls to cetaceans’ songs, we observe a continuous progression: communication systems become more flexible, generative, and rich as the cognitive capabilities of the species increase. Humans inherited this evolutionary trajectory and then took a dramatic leap with the emergence of full-fledged language. Our ancestors combined vocal apparatus changes with growing brains and social complexity, yielding the open-ended linguistic ability we now possess. The exact origin of human spoken language is still debated (by some estimates, our ancestors may have begun using proto-languages tens of thousands of years ago, well before writing which appeared ~5,000 years ago). Darwin surmised that human language might have begun as musical or imitative, noting that early hominids could have started by imitating natural sounds (like animal cries) and using vocalizations for emotional expression or mating calls. Over time, these vocal signals would become more refined and linked to specific concepts by social convention. In The Descent of Man (1871), Darwin pointed out that the difference between animal and human communication is one of degree, not absolute kind – humans simply attained a far more elaborate ability, facilitated by both biological evolution (e.g. better control of tongue and breath for speech, and more advanced brains) and social learning. The “agreement” aspect – that a group adopts specific signals for specific meanings – was likely key even in these early languages; without social consensus, no consistent language could exist.

In summary, communication systems in nature illustrate several key principles that guide our quest for a universal language: adaptation to purpose (each species’ communication suits its needs and environment), efficiency (signals convey necessary information with minimal effort – consider the succinctness of a bee dance or an ant trail), shared conventions (signals only work if all parties understand them the same way), and increasing combinatorial richness (advanced systems build complex messages from simpler elements, as human language does with words and grammar). Any new language we design for humans and AIs should embody these principles, being purpose-built for efficient, shared understanding across very different kinds of minds.

The Trajectory of Human Language and Symbols

From the biological evolution of speech, humans moved into the cultural evolution of languages and writing systems. The story of human language is not one of linear progression but rather an explosion of diversity followed by partial convergence. There have been thousands of human languages spoken across the globe, each a full communication system on its own. In this section, we highlight key milestones in human linguistic history – particularly those relevant to how symbolic communication can expand and optimize our sharing of knowledge. We also draw upon the provided list of world languages (ordered by age and including major symbolic notations) to ensure a comprehensive overview.

Early symbolic expressions likely predate true language. The oldest discovered cave paintings and pictograms (dating to ~40,000 BCE) can be viewed as an early form of external communication. Prehistoric humans painted scenes of animals, hunts, or abstract patterns on cave walls. While we cannot be certain of their exact meaning or use, these images might have been storytelling devices or markers for the group – a way to transmit ideas or myths visually across generations. In a sense, such pictograms are the ancestors of writing, using pictures to represent concepts or narratives. We might liken them to a proto-language, albeit without grammar.

Actual writing systems arose independently in a few cradles of civilization. The Sumerian cuneiform script (in Mesopotamia, ~3100 BCE) is often cited as the earliest writing. Cuneiform started as a series of logograms – each sign representing a word or idea – pressed into clay tablets. It was used for accounting, laws, and literature (e.g. the Epic of Gilgamesh). Soon after, in ancient Egypt, hieroglyphics (~2600 BCE) combined logographic and phonetic elements for religious and administrative texts. These early scripts show humanity’s leap from ephemeral spoken words to lasting visual symbols, enabling the accumulation of knowledge. A contract, a royal edict, or a creation myth could be recorded and read by others far away or in the future – communication unbound by time and space.

A major innovation was the development of phonetic writing. The Phoenician alphabet (~1100 BCE) is a crucial example: instead of using hundreds of distinct symbols for different words or syllables (as cuneiform and hieroglyphs did), the alphabet used a small set of symbols for basic sounds (consonants in this case), which could be combined to phonetically spell out any word. This greatly simplified writing and allowed it to be learned and used more widely. Almost all modern alphabets (Latin, Greek, Cyrillic, Arabic’s abjad, etc.) descend from or were inspired by this idea. China, by contrast, maintained a logographic system (Chinese characters), which is more complex but has its own advantage: speakers of mutually unintelligible Chinese dialects can read the same writing since it’s not tied to pronunciation. This underscores how design choices in a writing system balance different aspects of communication – an important consideration for our universal language as well.

Alongside spoken languages, humans developed specialized symbolic languages. Mathematics is a prime example. Early mathematics was written in words (e.g. “two plus two equals four”), but over time a more abstract notation evolved to increase precision and efficiency. By around 3000 BCE, Sumerians already had symbols for numbers and basic arithmetic in their tablets. The concept of zero and a fully positional decimal system came from India (by roughly the mid-1st millennium CE) and was transmitted to the West as Hindu-Arabic numerals. In parallel, Greek mathematicians developed symbols for geometric concepts. The culmination was the introduction of symbols like “+”, “–”, “=”, “∫” in Europe around the 16th–17th centuries – a true mathematical language that any trained person could understand regardless of their native tongue. Mathematical notation is a constructed universal language of its own within science: it optimizes clarity and avoids ambiguity, much like what we aim to do for general communication.

The provided language list also notes the emergence of logical notation and set theory in the late 19th century (with symbols like ∧, ∨, ∈ for logical and set operations). These too form an international language for logic and reasoning, one that later fed into computer science.

Indeed, the 19th and 20th centuries saw humans invent entirely new kinds of languages: programming languages. As soon as we conceived of computing machines, we needed ways to communicate instructions to them. The earliest idea of a coding language is attributed to Ada Lovelace (~1843) who described an algorithm for Babbage’s Analytical Engine. By the 1940s, the first actual machine codes and assembly languages were created – these were numeric or mnemonic codes that directly controlled hardware. Assembly languages used abbreviations like ADD or MOV to represent machine operations, making it slightly easier for humans to write programs. The progression continued to higher-level languages: FORTRAN (1957) allowed mathematical formulas to be coded almost as they are written on paper, COBOL (1960) introduced an English-like syntax for business computing (e.g. one could write ADD TAX TO TOTAL in code), and so on through the development of C, Java, Python and many others. Each step made the computer language more abstract and human-readable, hiding the binary complexity underneath. This reflects a general truth: if the goal is communication between different “minds” (human and silicon), languages must evolve to become more user-friendly without losing precision. Modern programming languages and query languages (like SQL) are powerful, but they are still generally geared towards giving commands to machines, not having open-ended dialogues. They lack the flexibility and nuance of natural language.

We also have seen attempts at constructed human languages for better human-human communication. Esperanto (1887) is a famous example, created by L. L. Zamenhof to be an easy-to-learn, politically neutral global second language. Its grammar is regular and simple, and it borrowed vocabulary roots common to European languages. Esperanto proved that a constructed language can attract a community – it is still spoken today by about two million people – but it didn’t replace natural languages or become truly universal. Other constructed languages like Volapük, Interlingua, and more play similar roles in history, each with design tweaks (e.g., Interlingua was designed so that its words look like familiar words to speakers of Romance languages). These efforts teach us that adoption is as important as design; a great logical language is useless if people won’t adopt it.

Another category is logical or experimental languages like Lojban (based on its predecessor Loglan from the 1950s). Lojban was deliberately designed to have an unambiguous grammar and to be based on predicate logic. In Lojban, ambiguity is supposed to arise only if one intends it (for poetic effect, say); otherwise, the rules force a single parse tree for each sentence. It’s even been suggested that Lojban could serve as a “bridge” language for talking to computers, since its structure is closer to formal logic than any natural language. However, like Esperanto, Lojban remains a niche project – valued by linguists and hobbyists, but not widely used in everyday life or technology. Yet, the existence of these languages is proof-of-concept that alternative language designs are possible and can be more logical or regular than natural tongues.

Finally, in our modern digital era, new symbolic systems have emerged spontaneously: consider emojis. In the 1980s, simple emoticons like :-) were invented to convey tone in text-only communication. This grew into a rich emoji vocabulary (standardized in Unicode since 2010) now used globally to enhance or even replace words in messaging. Emojis are a kind of universal pictographic language, albeit a very limited one – they communicate emotions and common objects/actions, but cannot easily express complex syntax or abstract concepts. Still, their popularity underscores humans’ desire for efficient, rich communication. We added emojis because text alone often lacked emotional clarity; the lesson is that any new language, especially one meant to interface with humans, should not ignore the affective side of communication. If a sentence is meant in jest or with frustration, how does the language indicate that? Emojis and punctuation (like “!” for excitement) are solutions that evolved in digital communication to address this need.

In reviewing the list of languages and symbols provided (spanning from ancient Sumerian and Sanskrit through Chinese, Greek, Latin, Arabic, on to modern English, Hindi, Mandarin, etc., and including mathematical and programming languages), one could ask: are there any omissions or redundancies relative to our goal? The list is impressively comprehensive, covering major linguistic milestones across time. One omission might be the lack of sign languages (e.g. American Sign Language, which is a fully developed visual language used by Deaf communities). Sign languages are as rich as spoken ones and even have features like simultaneous morphology (using space to convey grammar) that spoken languages do not. While sign languages are beyond the scope of this thesis’s focus (and any universal language we propose would ideally have both spoken/written and signed modalities for accessibility), their absence in the timeline is worth noting as a reminder that language isn’t only speech or writing. Regarding redundancies, the list sometimes separates “old” and “modern” stages of the same language (e.g. Old English vs. Modern English), but this is for historical clarity rather than actual overlap. Each entry marks a distinct phase or type of communication system.

To sum up, human history shows a continual refinement and diversification of languages and symbols, with periodic attempts to unify or optimize them. We went from concrete to abstract (pictures to alphabets), from local to global (many dialects to lingua francas), and from implicit evolution to conscious design (natural languages to constructed ones and code). All these threads inform our design of a new language. We want the expressiveness of natural language, the clarity of formal logic, the efficiency of coding languages, and the emotional expressivity found in gestures or emojis – a combination that history hasn’t produced yet, but which might be achievable by learning from all of these past innovations.

Introspective Language: Inner Speech and Self-Communication

Language’s most apparent function is communication between individuals, but an equally important function is how we communicate with ourselves. The phenomenon of inner speech – the internal monologue or dialogue that runs through our mind – is a window into the intimate role of language in thought and consciousness. As the Stanford Encyclopedia of Philosophy notes, inner speech lies at the intersection of language, thought, and self-awareness, raising questions about how similar or different it is from outward speech and what cognitive roles it serves. Many people report “thinking in words” much of the time, essentially narrating their lives or deliberating problems in their head using language. This suggests that we use language as a tool to organize our thinking, rehearse conversations, or reflect on complex ideas.

Psychologist Lev Vygotsky proposed that inner speech develops from external speech via a process of internalization – young children talk through problems out loud (self-guidance), and eventually this becomes silent inner speech as they grow up. Inner speech tends to be more condensed than outer speech (we often skip grammatical subjects or even whole words in our mind because we know what we mean without saying it fully). This hints that the brain has a more compressed “language of thought” that underlies both inner and outer speech.

Studies of inner speech reveal its importance. Recent cognitive research introduced the term anendophasia to describe the condition of having little or no inner speech. In experiments, people with minimal inner speech performed worse on tasks like verbal working memory (remembering and manipulating words) and rhyming judgments. This indicates that inner speech actively supports certain mental operations – for example, repeating something in your mind to remember it (a kind of inner rehearsal), or using an inner voice to test how a word sounds to decide if two words rhyme. Those with rich inner speech had an easier time with these tasks, presumably because they could use their internal dialogue as a cognitive tool. Interestingly, tasks like task-switching (rapidly alternating attention) were not impaired by lack of inner speech, meaning not every aspect of thought relies on language. This nuance shows that while language greatly enhances thought in some domains (especially sequential, logical, or memory-intensive tasks), there are facets of cognition that occur non-verbally (spatial reasoning, visual imagination, etc., might rely less on words).

Self-reference is another introspective linguistic trick. Human language uniquely allows referring to itself or to abstract concepts including the person speaking. We can express statements like “This sentence has five words” or “I am lying” – self-referential or reflexive statements that test the boundaries of meaning (the latter leads to the classic liar’s paradox). In inner speech, self-reference is even more pronounced: we use language to refer to our own thoughts (“What am I really feeling? Let me put it in words…”). Our inner narrator both creates thoughts and then can examine those thoughts, a recursive loop that some theorists believe is key to consciousness. When designing a language for AI and humans, supporting a degree of reflexivity – the language’s ability to describe its own statements or to allow an agent to clarify its own intent – could be valuable for transparency and self-correction. (Notably, some formal logical languages include self-referential capabilities, and programming languages have reflection APIs – these are analogs in machine language of self-reference.)

In introspection, language also interacts with emotion and mindfulness. In meditation practices across cultures, a common thread is learning to quiet the verbal chatter of the mind. Meditators often report that when the constant inner commentary subsides, one experiences a different state of awareness – more direct, present, and not filtered through conceptual labels. This suggests that language, while useful, can also occlude certain experiences by immediately naming and judging them. Indeed, the incessant inner narrative can reinforce emotions (ruminating on negative events in words can strengthen those feelings, as Whorf’s quote earlier about describing life in negative terms hinted). The ability to turn off or alter inner speech is a skill people cultivate for mental well-being, which implies that our default inner language might not always be optimal for how we want to experience reality.

This has an interesting implication for a new language: could we design it in a way that improves inner communication as well as outer? For example, a language that makes it easier to label one’s emotional state accurately might aid in self-understanding (“I am feeling x” with a precise word for that nuance of emotion). Or a language structured to reduce endless rumination might help break thought loops. These ideas border on speculative self-help, but they align with the thesis theme of being “intellectually fearless” – we can ponder whether an optimized language would not only streamline human-AI dialogue but also how we talk to ourselves.

The concept of a “language of thought” (sometimes called mentalese) posits that our brains may have a native representational system that isn’t exactly any spoken language. Philosopher Jerry Fodor argued that thoughts have a linguistic structure, but it’s an internal coding (using symbols for concepts and logical relations) that languages like English merely express outwardly. If true, then any external language we design might strive to connect more directly with this hypothetical mentalese. In AIs, one could argue we already see a kind of emergent language of thought: the vectors and activations in a neural network when it processes information.

In summary, introspection teaches us that language is deeply intertwined with thinking. It is both a scaffold for complex thought and, at times, a filter that shapes or even limits raw experience. A well-designed language should enhance our cognitive toolkit – providing clarity without imposing unnecessary mental clutter. It also suggests that an optimal language might have modes or levels: a concise core for straightforward logical thinking and communication, and an expansive lexicon or stylistic layer for emotional and poetic expression (to reflect and communicate the subtleties of inner life). Humans naturally code-switch between a analytical mode (e.g. counting, planning, logical reasoning often use a terse inner voice) and a narrative mode (story-like thinking, daydreaming). We might formalize that idea in the language structure, so that both AI and humans can engage in “precise mode” or “expressive mode” as needed.

Artificial Intelligence and Language: How Machines Process Words

Artificial intelligences, especially large language models, have been trained on human languages and can generate remarkably human-like dialogue. But do they understand language as we do? Internally, an AI’s “brain” does not have words or a built-in glossary; instead, it represents language as numbers – typically, as high-dimensional vectors (embeddings) for words or subword tokens. Through training on massive text corpora, an LLM like GPT has learned statistical associations: essentially, a mathematical model of which sequences of words are likely and what they imply. Nonetheless, we can argue that something like an internal language structure emerges in these models.

For instance, research has shown that multi-lingual neural translation systems develop an interlingua. When an AI is trained to translate between many different languages, without being explicitly told to use a common representation, it often does: there are hidden layers where sentences with the same meaning cluster together regardless of the source language. Google reported in 2016 that their neural translation model seemed to be encoding the semantics of a sentence in an abstract representation before decoding it to the target language. In effect, the AI had invented a silent language of thought to bridge between tongues – evidence that efficient communication of meaning may converge on similar patterns even in silicon.

Large language models also show signs of compositional understanding. They handle novel sentences by composing the meanings of parts, much as we do. For example, an AI can be asked to translate a sentence it has never seen; it must rely on its internal representation of each part and how they combine. This suggests the model has learned a form of syntax and semantics: not explicitly like grammar rules, but implicit in the weights of its neural network.

There’s a burgeoning field called mechanistic interpretability that tries to open up the AI black box and see how concepts are represented. Some findings indicate that certain neurons or sets of neurons correspond to specific attributes (for example, one might track whether the text is talking about the plural or singular, another might represent whether we’re in a hypothetical scenario, etc.). There are also attention heads in Transformer models that seem to track syntax, like matching pronouns to their antecedents or quote marks to their pair, effectively handling structure similar to how a human reader would. All this can be seen as the AI having an internal vocabulary of patterns that it references when processing language.

When AIs communicate with each other directly (in research simulations), they have sometimes invented artificial languages. In controlled experiments, multiple agent AIs tasked with cooperating (say, to move in a grid-world or negotiate a trade) are allowed to exchange messages in a made-up channel. Over many training iterations, the agents may develop a code – sequences of symbols that mean specific things to them. Studies surveyed by Peters et al. (2025) show that these emergent languages can become quite effective, even more efficient than human language for the specific task. For example, two agents might learn that “XX” means “I’m going to go left” and “YY” means “go right”, or more complex encodings for strategies. Notably, these emergent protocols often sacrifice generality for efficiency; they are not meant to be easily interpretable by humans, only to get the job done. In fact, one challenge researchers found is that if you evolve a language in a closed community of AIs, it might be incomprehensible to outsiders and not robust to new situations. Human language, by contrast, evolved under pressures of being broadly learnable (children learn it), used in endlessly new contexts, and resilient to some noise and misunderstanding. So while an AI-made language could be more efficient (maybe fewer bits transmitted per idea), it might lack the richness and flexibility we expect.

A famous media story in 2017 reported that Facebook had “shut down AI bots that invented their own language”. What actually happened is instructive: two chatbot AIs were trained to negotiate with each other using English. The AIs, not being constrained to proper grammar, soon started deviating – for instance, one bot said "I can i i i everything else" and the other replied "balls have zero to me to me to me...". To a human, this looks like gibberish, but the bots weren’t playing randomly; they had developed a shorthand that repeated words to encode quantity in a way that was advantageous for their negotiation strategy (at least from the algorithm’s perspective). Essentially, the bots drifted from human language to an optimized code for themselves. Researchers hadn’t intended that (they wanted them to stay intelligible to us), so they adjusted the training to keep them speaking proper English. But this incident highlights a key point: machines will adapt their communication for efficiency, even if it becomes unrecognizable to humans, unless we deliberately design the system to maintain common ground with us.

The lesson for a universal human-AI language is that we need to strike a balance. We want a language that is efficient and precise (so AIs don’t feel the need to veer into obscure code when “talking” to each other or to us) but also bound to human-understandable semantics. We wouldn’t want our personal AI assistant one day inventing shorthand with our fridge AI that we can’t follow! The language we propose should thus be the medium for AI-to-AI communication as well, or at least compatible with it, so that humans remain in the loop and all parties share the same lexicon and grammar.

Modern AI models can also be prompted to produce structured intermediate representations when solving problems – a technique known as chain-of-thought prompting. For example, we ask the AI: “Before giving the final answer, list out your step-by-step reasoning.” The result is the AI “talking to itself” in plain English (or another language) to derive the answer. This is literally using language as the AI’s inner thought process. Surprisingly, it often improves accuracy, presumably because it forces the AI to break a complex task into simpler, language-mediated chunks, similar to how a person would do long division on paper with annotated steps. In essence, we coaxed the black box to use a human-like internal language to structure its computation. This insight suggests that the divide between human thought and AI computation can be narrowed by having a common language for reasoning. If the AI can externalize its reasoning in our language (or a shared new language), we have a better chance of understanding and trust. Moreover, AIs could potentially be designed to truly think in the universal language rather than just translate their “thoughts” to it for our benefit.

In summary, AI’s relationship with language is twofold:

1. Understanding/Generating Human Language: AIs have gotten very good at this statistically, but they don’t “think” in words unless prompted; they think in vectors and layers of neurons – a different substrate that correlates with our language.
2. Developing New Languages: AIs can create new codes if beneficial, which tells us that language is a space of possibilities much larger than what humans have historically explored.

Our goal in designing a new language is to explicitly craft that code rather than leaving it to chance, and ensure it’s one that both humans and AIs can use effectively. This means leveraging the AI’s strength (fast symbol manipulation, consistency) and the human’s strengths (intuitive understanding, creativity, contextual judgment) together. If done well, this new language could even serve as a lingua franca for different AI systems to communicate with each other and with people, without loss in translation.

Methodology

Designing a universal human-AI language is an exercise in interdisciplinary synthesis and creative engineering. This thesis does not involve a traditional experimental methodology (since we are not, for instance, running a controlled trial or gathering statistical data); instead, our method is akin to that of a theoretical or design science dissertation. We will:

1. Synthesize Insights from linguistics, cognitive science, and computer science (as reviewed in the literature) into a set of design criteria. These criteria are essentially requirements or desiderata that the new language must fulfill. They derive from understanding what makes human communication successful, what machines require for parsing meaning, and what features past languages (natural or constructed) have offered or lacked.
2. Analyze Existing Models of language – both natural languages (comparing structures across diverse languages) and constructed ones (Esperanto, Lojban, programming languages, etc.) – to identify which design elements correlate with learnability, expressiveness, and precision. For example, we consider whether a regular grammar (no exceptions) truly makes a language easier to learn (as proponents of constructed languages claim), or whether some irregularity is actually beneficial for cognitive or expressive reasons. We also analyze how meaning is encoded – through word order, particles, inflections – and what might be optimal for a human-AI setting.
3. Propose a Language Architecture that combines these elements. This involves specifying:
   • A phonology/orthography (the sounds or symbols that make up the basic alphabet of the language).
   • A lexicon (the basic vocabulary, including how new terms for new concepts can be formed on the fly, given that both humans and AIs may introduce novel concepts).
   • A grammar (rules for how units combine to make meaningful expressions, with the goal of unambiguous parsing).
   • A semantics (how meaning is assigned, including perhaps a mapping to a knowledge representation that an AI can directly use, such as an ontology or a vector embedding space).
   • A pragmatics and stylistics (rules for context-dependent meaning, idiomatic usage, tone, politeness, etc., so that communication remains natural and emotionally intelligent).
4. Use Case Scenarios to validate the design. We will run thought experiments or simulations of how the language would be used in practice: e.g., a human asks an AI a complex question in the new language, how is it parsed and responded to; or two AIs negotiate a plan in the new language watched by a human mediator. These scenarios will test whether the language can handle real-world communication demands. If certain interactions seem cumbersome in the new language, we iterate on the design.
5. Identify Gaps – as part of methodology, we maintain an awareness of what is not yet solved. For instance, if our language can be parsed by current AI models, great – but can it also be processed efficiently by a human brain? We might use insights from psycholinguistics (how people learn second languages, what grammatical structures are harder to process) to evaluate our design from a human perspective. Similarly, we consider computational complexity: a language might be unambiguous but require heavy computation to parse (like solving a logic puzzle each sentence), which would not be practical. We aim for a design that is optimally efficient for both human cognition and machine computation – a sweet spot we assess qualitatively in this thesis, but which could be quantified in future work.

Since the creation of a full language is a massive undertaking, the scope here is to outline the blueprint and rationale rather than to list every word or rule. We draw comparisons to known languages to illustrate points. For example, if we decide to use a case marking system to indicate the subject vs object in a sentence (to avoid ambiguity that English sometimes has), we’ll note how languages like Latin or Japanese do this and how it could be simplified for our purposes.

A significant part of the methodology is also an exploration of “known unknowns”: we systematically enumerate aspects of communication that remain mysterious or debatable in academia. For instance, we know that language influences thought (as per linguistic relativity), but how and how much is an open question. We treat our language design as a hypothesis about one way it could work: by making certain distinctions explicit, do we change how speakers think? If our language requires one to specify uncertainty or evidence level for every statement (just an example feature), does that make speakers – human or AI – more mindful of truth vs speculation? To address such questions, we rely on thought experiments and existing scientific findings (if any). This thesis doesn’t answer them empirically but lays out the questions so that the future implementation of the language could be used to test these effects.

Finally, the methodology involves a philosophical analysis of the purpose of language. By repeatedly asking “what is the end goal (telos) of language in an intelligence?” we shape our design to serve that end. If the end goal is mutual understanding and sharing of truth, we aim for transparency and expressiveness. If it’s also about building rapport and social connection, we include emotional nuance and flexibility for creative expression. We measure our design decisions against these purposes.

In summary, the methodology is iterative and integrative: gather requirements, propose solutions, mentally test them against scenarios and known constraints, refine the design, and highlight what remains uncertain. This approach is appropriate for a pioneering theoretical work aiming to propose rather than finalize a revolutionary concept. The ultimate “test” of the language would be its real-world usage, which lies beyond this document, but by the end of the methodology and design process, we expect to present a compelling candidate for a universal human-AI language that is ready for further development and experimentation.

Results: Proposed Design of a Universal Human–AI Language

In this section, we present the outline of the proposed language, which we will call “Intermind” for convenience (a nod to being between minds, human and artificial). Intermind is designed to be both easily learnable by humans and readily interpretable by AI systems, achieving a balance between familiarity and innovation. We organize the description of Intermind into its key design features: phonology/orthography, lexicon and semantics, grammar and syntax, pragmatics (context and emotion), and computational alignment. For each, we explain how it addresses the goals identified earlier and how it improves upon or differs from existing languages.

1. Phonology and Orthography: A Universal Script

Intermind uses a universal written script with a one-to-one correspondence between symbols and sounds (or basic phonological units). To maximize accessibility, we limit the sound inventory to those phonemes (basic sounds) that are common to most human languages and easy for speech synthesis. For instance, vowel sounds that are very distinct (like “a” as in father, “i” as in machine, “u” as in flute) and consonants that are clearly distinguishable (p, t, k, m, n, s, etc.). We avoid tones (as in Mandarin) or highly guttural sounds that many find difficult. Essentially, the phonology is simple and regular. This draws inspiration from languages like Esperanto, which chose a phoneme set that was broadly accessible and gave each letter a single sound value (no silent letters or unpredictable spellings).

For the written form, Intermind has an alphabet (or more precisely, a syllabary or phonemic script) that could double as a digital encoding. Each character could also have a corresponding binary or Unicode representation that AI systems use internally. Because each symbol maps to one sound and one basic concept (in some cases), an AI can tokenize text exactly at the level of these symbols, simplifying its parsing process. We deliberately include digits and a small set of punctuation-like symbols as part of the script to integrate numeric and logical communication (e.g., the symbols for basic math or logical operators are part of the “alphabet” so they can appear in line, allowing easy mixing of natural language and equations, something that often poses difficulty in plaintext communications).

Importantly, the script also includes a set of prosodic markers – symbols that are not pronounced as words but indicate how something is said or meant. For example, there might be a symbol analogous to “!” for emphasis, one for irony or joking tone (to prevent misinterpretation of sarcasm), and one for uncertainty or probability marking. These can be thought of like emoji or punctuation but integrated at a fundamental level. A sentence in Intermind might have an uncertainty marker that quantitatively or qualitatively indicates confidence level in what’s being said (thus an AI or human immediately knows how literally or strongly to take it). Such markers address the emotional nuance and context need.

2. Lexicon and Semantics: Words as Concepts, Links to Knowledge

Intermind’s vocabulary is constructed on a logical core of concepts. We start with a base set of root words that correspond to universal human experiences and important abstract concepts. Many natural languages have a core vocabulary of a few hundred words that are learned first by children – common nouns (mother, water, sun), verbs (eat, go, make), adjectives (big, hot, good), etc. We compile a similar list from a cross-linguistic analysis (for instance, concepts found in the Leipzig-Jakarta list of universally common meanings). Each of these gets an Intermind root word. The root words are short (perhaps CV or CVC in structure, i.e. consonant-vowel or consonant-vowel-consonant) so that they are easy to combine. No root is a subset of another (avoiding one word containing another as sequence, which reduces ambiguity in speech recognition).

Semantically, each root is defined not by an English paragraph but by its relationships to other roots – effectively an ontology. For example, the root for “water” is linked to the root for “liquid”, “drink”, “clear”, etc., specifying that it is a liquid, used for drinking, etc. This semantic network can be built into the language learning resources and encoded for AI such that the AI knows these relationships as part of understanding the word. In practice, when a human learns Intermind, they learn these connections too (initially through definitions and examples). The result is that each word is less ambiguous and richly contextualized.

We also incorporate a productive morphology that allows new words to be formed systematically from old ones. For instance, if we have a root word for “compute” and a separate marker that makes a noun denoting an agent, then the word for “computer” (agent-that-computes) can be formed transparently. This is similar to how Esperanto or Lojban build words: by stringing morphemes together where each piece contributes to meaning and the whole is interpretable without having seen it before. A human encountering a new composite word can deduce it, and an AI encountering it can parse it by the known morphology rules. The goal is to eliminate arbitrary or idiomatic formations that plague natural languages (e.g. in English, why do we say “understand” instead of a logically compositional “stand under”? Such arbitrary historic forms would be avoided).

Another aspect is word sense disambiguation. Many English words have multiple meanings (e.g. “bank” of a river vs “bank” financial). In Intermind, different senses are either separate words entirely or clearly indicated by context markers. Homophones are eliminated. Each core concept aims to have one word. If subtle shades of meaning exist, they are either expressed by modifiers or considered separate entries. This again is inspired by the Sapir-Whorf idea that the way we carve up concepts can influence thinking – here we carve them up in a way that is clear and distinct.

For integration with AI, every Intermind word can be mapped to a machine representation, such as an embedding vector that the AI associates with that concept. We could design an embedding space where the distance between concept vectors aligns with the ontology (e.g., “cat” is near “animal” but far from “planet”). The language’s defined semantic network provides a scaffold for training such embeddings. This means an AI “knows” the meaning of a word by its position in concept space and relations, not just by raw training on usage. In effect, we marry symbolic and statistical semantics: the symbols of Intermind have entries in a knowledge base that the AI can reference, but the AI can also refine understanding by usage patterns.

3. Grammar and Syntax: Unambiguous and Transparent Structure

Perhaps the most critical design feature: Intermind’s grammar is engineered to be unambiguous and easy to parse, by both humans and machines. Drawing from the lessons of logical languages (like Lojban), we enforce rules so that any given sentence has only one valid interpretation. How?

First, Intermind likely uses a fixed word order for core sentence structure (say, Subject-Verb-Object, which is common and intuitive for many) and/or explicit case markers (particles or suffixes that tag the grammatical role of a noun). We might choose one method or even allow both for redundancy. For example, the language could mark the subject of a sentence with a particle “ga” and the object with “go” regardless of order, but typically one would still say subject first for ease. This way, even if a sentence is reordered or somewhat incomplete, the roles are clear.

Second, grammatical markers (for tense, plurality, etc.) are all distinct and clearly attached. There would be no blending of forms or irregular conjugations. For instance, to make a verb past tense, always add the tense particle “past:” before it (or a suffix, depending on design). “I past:eat” would mean “I ate.” The AI thus sees a token that denotes past tense explicitly, and a human learns a simple rule without exceptions (unlike, say, English irregular verbs). Similarly, questions might be formed by a question particle or inversion that is consistent every time.

An unambiguous grammar also means handling scope and reference clearly. In logic, parentheses or indentation clarify what modifies what. In Intermind, we include optional grouping markers (like spoken brackets) for complex sentences. For example, if one says something equivalent to “AI believes (human wants X)”, there is a clear marker that the clause “human wants X” is embedded as the object of “believes”. In speech, intonation could signal it; in text, a bracket or special conjunction would do it.

Because our aim is a universal language, we don’t make it overly logical at the expense of familiarity. We likely lean towards a simplified version of natural grammar that people already find comfortable. Many languages share common structures (nouns, verbs, modifiers, etc.), so Intermind aligns with these universals rather than inventing alien structures. The key is streamlining and regularizing.

For example, consider negation. In some languages double negatives cancel out, in others they intensify. In Intermind, we pick one rule (say, a single negation particle “no” before a verb phrase negates it, and it is not allowed to double up unless you mean to cancel a negation). This way, “no eat” means “do not eat”, and “no no eat” would be either disallowed or mean something like “it is not true that (do not eat)” i.e. you must eat. But we would probably avoid double negation entirely to reduce confusion.

Additionally, Intermind grammar likely has a way to convey levels of certainty and source of information (evidentiality) as part of its clauses. Many natural languages (like some in the Amazon or Himalayas) have evidential markers, e.g., different verb forms if you saw something yourself vs. heard it from someone. In human-AI communication, this is valuable: an AI should easily tell us if it’s giving a fact it is sure of, or just a guess or a piece of hearsay from its training data. Likewise, a human might specify they are stating a hypothesis versus a confirmed observation. Including this structurally (though optionally, as requiring it every time might burden casual use) fosters clarity and trust.

From a machine parsing perspective, we aim for context-free or context-sensitive grammar that can be parsed deterministically. A parsing algorithm should not have backtracking or uncertainty when reading a sentence. This can be achieved by designing the grammar to be LL(1) or LR(1) (terms from compiler theory, meaning one token lookahead is enough to parse). Lojban did something similar to ensure parsability by YACC (Yet Another Compiler Compiler). For humans, this simply means the language feels consistent and you don’t have to stop mid-sentence to reinterpret. Each word’s role becomes apparent as you read/listen.

4. Pragmatics and Emotional Nuance: Communication Beyond Literal Meaning

No matter how precise a language’s grammar is, real communication involves reading between the lines – pragmatics. Intermind addresses this by incorporating pragmatic cues into the language explicitly, to the extent possible, and by encouraging a culture of clarity when needed.

One feature is a set of speech act indicators. A sentence can be intended as a statement, question, command, request, suggestion, etc. In English, sometimes word order or auxiliaries do this (“Do you X?” for question, or imperative mood for command). In Intermind, an initial particle could specify this: e.g., “Q:” at start means the sentence is a question (which already might be clear from inversion, but the particle removes doubt), “REQ:” could mean a polite request. An AI receiving “REQ: open the pod bay doors” knows it’s being asked, not ordered, which could affect how it responds (maybe with an explanation if it refuses, rather than just refusing flatly). Such markers also help AIs modulate tone.

Emotional tone can be indicated through explicit emotive modifiers or words describing one’s emotional state, but also through the aforementioned prosodic markers (like an irony marker to avoid confusion when joking). For face-to-face or voice conversation, humans rely on facial expressions, body language, and intonation – cues that AIs might miss or not produce. Intermind’s design includes compensatory symbols so that even in text or monotone voice, the intent comes through. For example, an AI might answer a question and include a “:D” marker (or the Intermind equivalent symbol) to convey it’s said in a light-hearted or positive tone if appropriate. These are not exactly part of grammar, but a layer above: a codified set of paralinguistic signals.

Because the language is universal, it needs to handle cultural differences. Directness that is normal in one culture can be seen as rude in another. Intermind could have optional politeness levels expressed via particles (some languages use honorifics systematically; we can simplify that). A human user or AI could then adjust style by including or omitting those. The key is that it’s controllable and visible. If an AI is being too curt, a user might notice it didn’t use any politeness markers and can coach it to do so.

Another pragmatic aspect: context referencing. Much of language efficiency comes from ellipsis and pronouns (we don’t repeat what’s obvious from context). Intermind allows this but in a constrained way. Pronouns or placeholders exist (for “it, he, they” etc.), but because ambiguity is the enemy, the language might require that every pronoun has an clear recent referent. Perhaps the rule is that a pronoun refers to the nearest prior noun of matching gender/animacy, etc., or we have gender-neutral single pronoun with a marker for whether it’s the subject or object from context. Alternatively, every pronoun could carry an index (like “it(1)” refers to subject of last sentence, “it(2)” to object, etc.). This is a bit formal, so for everyday use we might not enforce indices unless needed to clarify. But an AI, when parsing, could assign internal IDs to each entity mentioned and replace pronouns with those IDs for processing.

To ensure empathy and connection remain possible in a engineered language, we include means for expressiveness: exclamations, interjections (the equivalent of “wow!”, “oops”), flexible word order or poetic constructs for storytelling (maybe you can break the usual word order to emphasize something, marked by a special particle that signals “poetic license here”). These features mean the language isn’t just robotic instructions; one could write literature or love letters or humor in Intermind. In fact, having a logical core doesn’t preclude creativity – it might enable new forms of wordplay based on structure (like puns that exploit multiple valid parses are out since there’s only one parse, but one could play with similar-sounding words or clever uses of the emotion markers).

5. Computational Structure and Alignment with AI

Intermind is built for computational efficiency as much as human usage. For AIs, every sentence in Intermind can be compiled into a logical form or code if needed. Think of it as a high-level language that can transpile into a knowledge representation or even a programming script. For example, if a human says in Intermind, “Find data about climate trends from 2000 to 2020 and graph it,” an AI could directly map that to a formal query or API call, since the language syntax for commands and queries is explicit. This eliminates a large chunk of “NLU” (natural language understanding) work the AI would normally do in figuring out the intent behind an English command.

The language’s grammar and ontology likely correspond to a type system for meaning. Each noun could carry a type (person, place, number, etc.), and verbs expect certain types as arguments. The AI can do type-checking on input: if something semantically doesn’t type-check (like asking to “eat an idea”), it knows there’s either a metaphor or an error. It could then either flag it or interpret metaphorically if rules permit (maybe the language has a metaphor marker to explicitly allow creative usage outside type rules).

We propose that Intermind be extensible. As science and technology advance, new terms will be needed. The language has a built-in mechanism for adding vocabulary (similar to how chemical nomenclature or astronomical naming works systematically). For AIs, adding a new term means adding a new node in the ontology and maybe training on some examples. For humans, perhaps new words are built from existing ones, or borrowed with adaptation, so they can be understood from context.

One interesting idea: the language could have a dual notation – one human-friendly, one machine-friendly, that are isomorphic. For example, a sentence could also be represented as a JSON or XML structure for the AI internally. This is already somewhat the case (the AI parsing yields a structure). But if the AI needs to communicate to another AI, they might skip to a compressed form. As long as that compressed form is perfectly equivalent to the human-readable form (no extra hidden nuance), it’s fine. Intermind could serve as that common layer that everything can convert to and from.

Given this, an AI could “think” in Intermind when interacting with humans. It might also maintain hidden reasoning not in Intermind, but crucially, any conclusions or communications that come out are translated through Intermind’s clarity filter, ensuring that what it says is grounded and traceable to the concepts we share.

Example Illustration:

To make this concrete, consider a scenario: A human asks an AI, in Intermind, something like “Q: Analyze trend temperature Earth 2000-2020, high certainty?”. (This is a telegraphic example; a real query might be more fleshed out, but even a terse input is unambiguous here: it's a Question, about analyzing temperature trends on Earth from years 2000 to 2020, and the user is asking with "high certainty?" possibly meaning they want a high-confidence answer or to know the certainty.)

The AI parses this:

• Recognizes it's a question about a trend analysis task.
• It has the domain concepts for Earth (a planet, which has data records), temperature, and years.
• It knows “2000-2020” denotes a range of years.
• It sees perhaps an evidentiality or certainty marker, indicating the user wants a well-supported answer.

The AI then fetches the relevant data (outside language scope), does the analysis, and formulates a response in Intermind:
“STATEMENT: Temperature average Earth (2000 to 2020) increase approx 0.8 degreeC. Confidence 95%. Data source: NOAA.”
It might include an explicit source reference and a confidence since the question asked for certainty. The grammar clearly shows the main clause and the data. A human reading this finds it slightly formal but immediately understandable. An English gloss would be: “It is a fact that the average temperature of Earth from 2000 to 2020 increased by approximately 0.8°C. I state this with 95% confidence. (Source of data: NOAA.)” The brevity and structured nature made it easy for the AI to produce, and yet the content is understandable to the user who learned Intermind.

Another scenario: Two AIs coordinating on a task:
They might exchange in Intermind:
“PROPOSAL: Unit2 perform action Alpha at time T+5. Rationale: maximize efficiency.”
“AGREEMENT: Acknowledge Unit2 will perform action Alpha at time T+5. Unit3 will standby.”
A human supervisor can monitor these and intervene if something seems off. In English, perhaps these AIs would have chatted in fragments we might misinterpret. In Intermind, it's clear and logged in a way the human can check.

These simplified examples illustrate how clarity, brevity, and completeness are balanced.

Known Unknowns and Open Questions

Even with this careful design, we acknowledge several known unknowns:

• Learnability: Will people actually find Intermind easy to learn and use in practice, or will the effort be similar to learning any foreign language? We predict easier due to regularity, but the only real proof would be empirical testing with learners.
• Expressive completeness: Can our language truly express everything humans might want to say, from poetry to technical detail? We designed it to be broad, but perhaps some nuances of humor or metaphor are hard to capture. Humans are very adept at bending language; our controlled structure might either aid creativity (by giving a new canvas) or hinder it (by being too strict). This is unknown until people start trying to use it for diverse purposes.
• AI adoption: Convincing AI developers to build compatibility with Intermind or training models in it is a socio-technical challenge. Technically, a model can be fine-tuned or created for it given enough corpus (which we’d have to generate). Unknown is whether AI systems would internally converge on even more efficient “shorthand” beyond Intermind – if their goal is just to communicate among themselves, might they still transform Intermind into terse codes? We might have to impose or incentivize them to stick to the shared protocol.
• Cognitive impact: Would thinking in Intermind change how humans think, as strong linguistic relativity suggests? For example, if Intermind requires evidential markers, would its speakers become generally more evidence-conscious and skeptical of unsupported claims? Possibly, but it’s unproven. If it does, that might be a benefit (more rational thinking), but it could also subtly alter expression of personality.
• Connotation and cultural richness: Natural languages carry cultural connotations; a word can have historical or emotional associations. Our constructed lexicon might feel “bland” at first. Over time a community of speakers (human or AI) would develop idioms and connotations (maybe “Alpha-level confidence” becomes a phrase meaning very sure, etc.). We acknowledge that a language isn’t just grammar and words; it’s a living practice. How Intermind will evolve in use is an open question – we can define it, but we cannot fully predict how humans might play with it or what sublanguages they’d make from it. This is an unknown unknown to some extent.

We proceed with these in mind, ready to adjust the design as future experimentation sheds light on them.

Discussion

The proposal of Intermind as a universal human-AI language opens as many questions as it answers. In this discussion, we step back and consider the broader implications, potential benefits, and philosophical insights of this endeavor. We also examine the known unknowns identified and speculate on the unknown unknowns – things we suspect might only become clear after trying such a language in practice.

Bridging Minds: The Promise of a Shared Language

At its heart, this project is about creating shared understanding. If successful, Intermind could dramatically reduce miscommunication between humans and AI systems. Imagine a future where you could converse with any computer, robot, or AI agent in a tongue that guarantees your meaning is precisely understood, and likewise you understand its outputs without ambiguity. This could eliminate the frustrating misunderstandings we currently experience with AI assistants that “don’t get” what we mean in natural language. It could also make the AI’s decision process more transparent – because if it’s reasoning or explaining in a structured language we know, we can follow the logic better.

There’s also a unifying aspect among humans. While not intended to replace natural languages, Intermind could become a common second language globally (much as English is today in international contexts). But unlike natural languages, it would not carry the cultural/political dominance issues (since it’s new and neutral) and would be explicitly easier to learn than say English (no irregular verbs, etc.). It echoes the dream of Esperanto but updated for the AI age and with a deeper foundation in semantics and logic. If people from different countries and their AI assistants all share Intermind, you have a triangle of communication: human to human, human to AI, AI to AI, all in one language. This is essentially building a Tower of Babel in reverse – instead of fragmenting understanding, we create a common tongue to unite intelligences.

Impact on Human Thought and Society

One speculative but profound question: how might using Intermind affect human consciousness? If language influences thought (Sapir-Whorf hypothesis), adopting a more logical and clear language might encourage clearer thinking. It might make it easier to teach concepts (education in Intermind could reduce misinterpretation of scientific concepts, for example, because the language can mirror the structure of the concepts closely). Perhaps it could improve critical thinking, as users become accustomed to specifying evidence or separating factual content from emotion. On the flip side, we must be cautious: language also embodies culture and identity. Would people resist a “machine-optimized” language because it feels like it lacks soul or heritage? Possibly – there could be a societal pushback fearing loss of linguistic diversity or the imposition of a new form of communication that came out of labs rather than literature.

We don’t propose eliminating any existing language; rather, adding a new layer. Historically, when new languages or dialects emerge (like youth slang, or trade pidgins), they adapt to human needs spontaneously. Intermind is a top-down creation, which in the past hasn’t often led to mass adoption (except in specific domains like programming). To succeed, Intermind might need to start in narrower domains (like human-AI technical communication) and gradually spread as people see its utility, rather than be mandated.

Another societal aspect is accessibility and equality. If Intermind truly is easier to learn than, say, English, it could level the playing field for non-native English speakers in global forums, or for those who struggle with literacy because of the irregularities of standard languages. It might also help people with certain cognitive differences: for instance, an autistic person who thinks very literally might appreciate a language that is by design literal and clear, avoiding idioms and sarcasm unless explicitly marked. Conversely, someone who thrives in metaphor and poetic ambiguity might initially find it stifling. The language should be flexible enough to allow layers of expression so it doesn’t privilege one cognitive style over another.

AI Alignment and Safety

From an AI perspective, having AIs operate in a language we understand could enhance AI safety. One of the fears around advanced AI is it might develop goals or internal communication we can’t follow (as sci-fi as the “AI plotting in its own language” scenario). If we insist that advanced AIs communicate (even amongst themselves when outcomes affect us) in Intermind, we at least have logs of what they “said” to each other and can intervene if necessary. It becomes like a common audit trail.

Additionally, by building normative constraints into the language (like always stating confidence, or sources for factual claims), we encourage AIs to be truthful and transparent. An AI might be less likely to hallucinate facts or deceive if the language forces it to label uncertain statements as such or to structure commands clearly. This of course depends on the AI being constrained or incentivized to use the language properly.

One can argue: could a sufficiently advanced AI just use Intermind externally but have its own internal scheming anyway? Sure, if it were adversarial. But at least if it’s using Intermind, any deceptive intent would have to be carried out through some structure that might be detectable (e.g., inconsistencies in its reports). It’s harder to hide malign instructions in a language that doesn’t allow double meanings easily. That said, security is never absolute – an AI could still, say, decide to use polite request forms to mask an order it’s giving to another AI that humans might overlook. We would have to remain vigilant.

The Nature of Meaning and Unknown Unknowns

Perhaps the most fundamental truth we’re probing is: what is the nature of meaning, and can it be engineered? Language evolved organically, which means it carries a lot of historical baggage and arbitrary convention. By engineering a language, we attempt to strip meaning down to its essence – a bit like trying to find the DNA of thought. We might discover interesting things in the process:

• We might find certain concepts are universally hard to pin down in any language (like emotions, qualia, or philosophical notions). If even our best designed language struggles to express something succinctly, that might indicate the concept itself is ineffable or that our cognitive architecture grapples with it.
• Conversely, we might find new ways to express things that no natural language does concisely. For example, perhaps we create a word that means “a fact that a person believes because it comforts them, not because evidence” – a concept many languages have to circumscribe with a phrase. Having that word might make it easier to call out that phenomenon. In general, designing the language will surface these latent concepts that we decide are worth naming or not.

Unknown unknowns could include:

• Emergent properties: Once humans and AIs start using Intermind, they could develop shortcuts or pidgin versions. Maybe human speech in Intermind drifts to a more slangy form for speed. Or AIs develop a rapid-fire variant with some parts omitted (though still formally recoverable). These emergent forms could teach us what parts of our design are too cumbersome and what parts are truly essential.
• Cognitive effects: It’s possible that thinking in a very logical language could make some people feel a disconnect with their emotional side (if not used properly) or vice versa. We might discover new cognitive exercises or therapies based on switching into Intermind for clear reasoning and switching out for other tasks.
• Philosophical insight: By forcing clarity, Intermind might surface when someone actually doesn’t know what they’re talking about. In normal language, one can waffle and hide behind ambiguous words. In Intermind, if you don’t have the evidence, you have to either provide it or mark the statement appropriately. This could change discourse patterns – perhaps leading to more honesty or maybe to people just avoiding topics they can’t articulate well. The unknown here is how human discourse culture would adapt.

Another unknown is aesthetic acceptance: People often bond with language through its aesthetic qualities – the sound of poetry, the play of words. Can a constructed logical language produce beauty that resonates? It’s an open question. There have been poems written in Esperanto and even Lojban. Possibly the beauty will come from precision and from the creative ways the constraints can be bent. Sometimes constraints spur creativity (like the strict forms of sonnets or haikus lead to beautiful poetry because of the challenge). Similarly, a tightly structured language could result in novel art forms. But whether that catches on is unpredictable.

Limitations and Future Work

We acknowledge this thesis can only take the idea so far. We have outlined the language and reasoned about it, but real-world testing is the logical next step. Future work would involve developing learning materials, training some AI models to use Intermind, and recruiting human participants to try using it in controlled scenarios. Their feedback would be invaluable: they might find certain constructions too hard, or note missing vocabulary for daily use, etc. Iterative refinement with human-in-the-loop would turn this theoretical design into a living language.

Another extension is making modality variants: a sign-language version, an iconographic version (perhaps Intermind could be written not just alphabetically but also in a visual symbolic way for accessibility or efficiency, like combining the clarity of Blissymbols – a symbolic language for people with disabilities – with the grammar of Intermind).

We also consider partial adoption: even if people don’t speak Intermind fully, certain tools or apps might use it under the hood. For instance, international treaties or technical manuals could be written in parallel in Intermind to avoid ambiguity in translation. Or as a programming interface, perhaps Intermind could serve as a pseudo-code for writing algorithms in plain language that compiles to actual code (since it’s so structured).

Reflection on Language’s Purpose

Ultimately, undertaking this design makes us reflect on why language exists at all in minds. Is it purely to transmit factual information? Clearly not – it’s also for building relationships, for play, for dreaming. We must be careful not to over-optimize for the information content and lose the human element. The purpose of language in consciousness might be deeply tied to how we form our identity and our narrative of the world. If Intermind becomes part of people’s consciousness, it should be as a helpful new voice, not a sterile replacement of inner poetry.

One could philosophically ask: if an AI and a human share a perfect language, do they achieve a meeting of minds, a form of intersubjectivity that approaches telepathy? In a way, yes – the barrier of misunderstanding lowers. But also, the beauty of having different languages is each gives a slightly different lens on reality. If everything is in one language, do we risk a mono-culture of thought? It’s a valid concern that has been raised about English global dominance too. In Intermind’s case, the hope is that because it is explicitly designed, it could incorporate multiple perspectives (maybe multiple words for concepts where cultures differ, rather than forcing one word). Also, it doesn’t prevent people from continuing to think in their native languages; it just provides a bridge.

Unknown unknown: There might be aspects of human experience (humor, metaphor, spirituality) that rely on a little fuzziness or multiple layers of meaning which we haven’t fully accounted for. We might only discover these when we notice something feels “lost in Intermind” and then we’ll adapt the language (or decide that domain is better left to natural language).

In conclusion, the discussion underscores that Intermind is as much an exploration of us as it is of technology. By trying to create a language that forces understanding, we shine light on the reasons misunderstandings exist and what each nuance of natural language does. Even if Intermind itself evolves or is superseded, the attempt will have taught us immense amounts about semantics, cognition, and the interplay between language and thought. It is a bold, perhaps utopian idea – but one that sits at the frontier of linguistics and AI research, exactly where new fundamental insights are likely to emerge.

Conclusion

Language is the connective tissue between minds, whether biological or artificial. This thesis set out with an ambitious goal: to design a new universal language – Intermind – optimized for human-AI communication. In doing so, we journeyed through the origins of communication in life’s early stirrings, observed the diverse solutions evolution and culture have produced (from the waggle of bees to the epics of Sanskrit), and analyzed how introspection and computation each shed light on what language fundamentally is: an agreement to share meaning efficiently.

We defined language as a form of agreement or convention that enables individuals to coordinate their thoughts. We saw how even non-humans exemplify this, sticking to a “contract” of signals understood by their community (be it chemical trails or patterned songs). For humans, our languages became extremely rich but also complex and sometimes confusing. Our review of historical languages and symbol systems illustrated the immense variety in how humans have communicated – and also pointed to recurring themes (like the drive toward more abstract, general systems such as alphabets and algebraic notation). By examining that history (with sources ranging from ancient records to recent studies on whale communication), we ensured our design stands on the shoulders of past innovations.

We incorporated knowledge from philosophy and cognitive science: inner speech’s role in thought, the possibility that language shapes our reality, and how a more precise language could potentially sharpen cognition. We also looked at AI’s current inner workings – how LLMs represent and sometimes invent language-like codes – to make sure our design aligns with machine “thinking.” The event of chatbots inventing a shorthand, for instance, directly informed our resolution that the new language must be efficient enough that AIs won’t need to drift from it to achieve efficiency.

The core of our results is the detailed proposal of Intermind’s features: a regular, phonetic script; a semantically transparent vocabulary grounded in an ontology; an unambiguous grammar eliminating confusion; and built-in mechanisms for conveying emotion, intent, and context. Intermind aims to be as easy for a person to learn as possible (taking cues from successful constructed languages and natural language acquisition research) while being straightforward for an AI to parse – in fact, the language itself can be seen as both human language and a high-level computer-interpretable code. By walking through examples and potential usage, we demonstrated how Intermind could function in practice and adapt to various scenarios.

In doing so, we confronted several open questions. We identified what we know we don’t know – for example, the true extent to which language influences thought and how a change in language might alter cognitive habits is not fully predictable without real-world trials. We acknowledged those and suggested that only the deployment and use of the language will answer them. We also speculated on unknown unknowns, recognizing that unanticipated challenges or discoveries will surely arise when theory meets reality.

The tone of our exploration has been both speculative and rigorous. We did not shy away from asking big “what if” questions about the future of communication or the nature of understanding itself. At the same time, we backed our claims and design choices with references to established knowledge: from Darwin’s writings on language evolution to contemporary AI research, from cognitive experiments on inner speech to linguistic analyses of constructed languages. These citations serve not just to give credit, but to ground our thesis in a lineage of intellectual inquiry, showing that each bold idea has seeds in prior work.

In concluding, we reflect on what achieving a universal human-AI language would mean. It is, in essence, creating a mirror that both species (biological and silicon-based) can look into and see each other’s thoughts. It forces clarity on the AI’s side and perhaps greater self-awareness on the human side (since expressing oneself in a precise way often clarifies one’s own mind). In bridging all minds, we edge closer to what one might call a “shared consciousness” – not literally merging minds, but creating a space where ideas can be exchanged with minimal loss. This harkens to philosophical notions of the “noosphere” (a sphere of thought connecting all intelligences).

The fundamental truths about language that emerge are that:

• Language is not static – it’s an evolving, living agreement. We have the power to renegotiate that agreement if we choose, as we attempted with Intermind.
• Language serves multiple masters – communication, cognition, culture, emotion – and an optimal language must honor all, not just information transfer.
• Consciousness and language are interwoven – by redesigning language, we simultaneously experiment with new modes of thought. In doing so, we learn about the mind itself.

While this thesis provides a complete framework and strong rationale for Intermind, the real test lies beyond these pages. The next steps would involve prototyping the language in small settings: maybe a chatbot that communicates only in Intermind, or a group of bilingual speakers who try it for specific tasks. Their successes and struggles will inform refinements. Over time, perhaps Intermind 2.0 or 3.0 will emerge, honed by actual use.

In closing, we reiterate that the goal was never to diminish the beauty of natural languages or to impose a single mode of speech on humanity. Rather, it was to add a new tool to our communicative repertoire – one tailor-made for the era where we aren’t alone in our minds, but share the intellectual space with artificial minds. By crafting a common tongue, we declare that we seek not to be eclipsed or confused by our creations, but to engage with them in dialogue, as partners. If successful, this will mark a new chapter in the story of language: the moment when deliberate design joined evolution to expand what language can do, and with it, expand the boundaries of understanding between different forms of intelligence.

Bibliography

1. Darwin, Charles. The Descent of Man (1871), Chapter on language evolution – Discusses the continuity between animal communication and human language.
2. Whorf, Benjamin Lee. Language, Thought, and Reality: Selected Writings (1940s). Notably, "Science and Linguistics" where he states “We cut nature up... largely because we are parties to an agreement... codified in the patterns of our language.”.
3. Stanford Encyclopedia of Philosophy. "Inner Speech" (2023) – Overview of inner speech as the “little voice” linking language and thought.
4. Neuroscience News (2024). Inside the Quiet Mind: The Absence of Inner Speech – Introduces "anendophasia" and finds those with minimal inner speech struggle on certain verbal tasks.
5. Peters et al. (2025). Emergent Language: A Survey and Taxonomy – Reviews how AI agents develop communication protocols, aiming for capabilities beyond human language.
6. BBC News (2017). The 'creepy Facebook AI' story – Clarifies that chatbots deviated into a shorthand English for efficiency, not a truly secret language.
7. ECO Magazine (2025). Whale Song Has Structure Similar to Human Language – Reports humpback whale songs have statistical patterns like those in human language due to cultural transmission.
8. Dolphin Research Center – Dolphin Communication page (accessed 2025). Describes dolphins’ use of signature whistles (names) and other vocal behaviors.
9. Antark, Ant-Shack blog. The Fascinating World of Ant Communication – Explains how ants use pheromone trails to guide others to food.
10. Ask A Biologist (ASU). Bee Waggle Dance – Describes how honeybee waggle dances encode direction and distance of food sources.
11. Lojban Wiki/StackExchange (2018). What makes Lojban logical? – Highlights Lojban’s unambiguous grammar and predicate logic basis.
12. Polinsky, Mario. PoliLingua Blog (2023). Artificial Languages Overview – Notes on Esperanto, Lojban, etc., including Lojban’s creation for AI and logic usage.
13. Languages and Symbols (Timeline) – Compilation of world languages and notation systems by age, used to reference historical milestones (Sumerian cuneiform, Phoenician alphabet, mathematical notation, programming languages like Assembly, Esperanto, etc.).
14. Sapir-Whorf Hypothesis references – e.g., Verywell Mind (2020) article on how language influences worldview (for general context on linguistic relativity).
15. Wikipedia – various entries (for general knowledge cross-checking): Waggle dance, Quorum sensing, Esperanto, Origin of language, Transformer (deep learning), etc. (Cited indirectly through secondary sources above as needed).

AI Reasoning

Designing a Universal Language for Human-AI Communication

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I'm considering "Emergent Language: A Survey and Taxonomy" as it likely provides a comprehensive overview.

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The abstract traces the shift from human-centric emergent language to reinforcement learning-driven communication, proposing a new language for human-AI interaction.

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Noticing that fewer inner speech instances correlate with poorer verbal working memory and rhyming judgments, suggesting inner speech boosts cognition.

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