Researchers have begun using AI and machine learning to reconstruct ancient proto-languages that have no written records. A notable example is a 2013 system from UC Berkeley that reconstructed Proto-Austronesian (ancestor of languages across the Pacific and SE Asia) with about 85% accuracy, closely matching what linguists achieved manually. This system used probabilistic models (Markov chain Monte Carlo algorithms) to analyze sound correspondences in over 140,000 words, effectively speeding up reconstruction from years to hours. Similarly, AI-driven methods have been applied to Proto-Indo-European (PIE), the 6,500-7,000 year-old ancestor of Indo-European languages. For example, computational modeling of PIE’s grammar by Carling and Cathcart (2021) combined a database of 125 Indo-European languages with algorithms from evolutionary biology to infer the ancient grammar. Their model could recover grammatical features of PIE – supporting the classic view that PIE’s structure resembled Sanskrit and Ancient Greek – and reveal which grammatical traits stayed stable over millennia. Recent studies are also exploring neural network approaches to proto-language reconstruction. For instance, researchers have adapted encoder-decoder models (RNNs and Transformers) to generate proto-words from modern descendants, using semi-supervised learning to improve accuracy. These AI-based efforts, while still experimental, augment historical linguistics: they can propose proto-language vocabulary and sound changes at scale, offering hypotheses for linguists to verify. In sum, LLMs and other AI models have demonstrated the ability to partially “reverse-engineer” ancient languages, reconstructing proto-lexicons and grammars in a fraction of the time traditional methods would take. Accuracy is continually improving – early models achieved ~85% accuracy for Austronesian lexicons, and ongoing research is refining these techniques for other families (e.g. recent neural reconstructions of Sino-Tibetan and Romance protolanguages). AI will not replace expert linguists, but it serves as a powerful tool to test reconstruction hypotheses and potentially illuminate the evolution of languages that died out long before writing.
Evolutionary Divergence in AI Language Models
Interestingly, large language models (LLMs) like GPT can be studied in analogy to evolving species or language families. Each model version or fine-tuned descendant may introduce changes akin to linguistic drift. Researchers in 2024 introduced PhyloLM, a method that treats LLMs as if they have “genomes” of features, allowing the inference of a phylogenetic tree of language models. By comparing model responses to many prompts, they computed a distance matrix and built dendrograms (family trees) that cluster LLMs into distinct families, reflecting their training lineage. For example, models that share a base (like those fine-tuned from GPT-3) form a branch distinct from, say, open-source Transformer models. These phylogenetic analyses show clear “families” of LLMs and visualize their evolutionary trajectories or training similarities. Moreover, the “genetic distance” between models correlates with performance differences: surprisingly, the team found they could predict an LLM’s benchmark performance (e.g. on knowledge tests) from its position in the family tree. This suggests that as models diverge (through new training data or techniques), their capabilities shift in systematic ways.
Beyond such retrospective analysis, we have observed real-time model drift in deployed AI systems. A recent study compared GPT-3.5 and GPT-4 between March 2023 and June 2023 and found substantial changes in behaviorarxiv.org. For instance, GPT-4’s accuracy on a set of math questions dropped from 84% to 51% after an update (March to June), while GPT-3.5’s performance improved on those same questions. The models also showed divergence in other tasks: GPT-4 became less willing to answer sensitive questions or give opinions by June 2023, indicating a shift in its “personality” or alignment tuning. Such changes, despite the model name remaining the same, highlight that LLMs evolve through their checkpoints/updates – much like how languages change over time even if called by the same name. OpenAI’s updates effectively created new variants of GPT-4 with different response patterns, illustrating behavioral drift. These findings have been likened to language evolution, where an LLM’s parameters are analogous to a genome that mutates with each training iteration or fine-tune. Indeed, treating model updates as “offspring,” one can trace branches: e.g. an instruction-tuned model or a RLHF-tuned model diverging from its base pre-trained ancestor. Researchers note that even without transparency into training data, output-based phylogenetic methods can reconstruct a model’s lineage and relationships. In summary, LLMs do exhibit evolutionary-like divergence: versions separated by time or fine-tuning can drift in capabilities and style (as documented with GPT-4’s changes), and AI scientists are now using phylogenetic algorithms to map “family trees” of models, much as historical linguists map language families. This not only provides insight into model development but also helps anticipate how changes in training affect an AI’s “dialect” or performance over time.
Hokkien as the Oldest Sinitic Language
Hokkien (a major Southern Min dialect) is often regarded by linguists as one of the most conservative and ancient surviving Chinese languages. The Min branch, which includes Hokkien, appears to have split off directly from Old Chinese, rather than passing through the later Middle Chinese stage that gave rise to Mandarin, Cantonese, and other branchesen.wikipedia.org. Historical evidence shows that migrants from the Yellow River plains brought Old Chinese speech to Fujian by the 3rd century CE, isolated from northern developmentsen.wikipedia.org. Over time, this evolved into the Min languages (like Hokkien), meaning Hokkien preserves a lineage separate since antiquity. In contrast, most other Chinese “dialects” (like Mandarin, Yue/Cantonese, Wu, etc.) descend from a common later stage (Middle Chinese, around the Tang dynasty). Because of this early split, Hokkien retains archaisms in pronunciation, vocabulary and grammar that have been lost elsewhere. Linguists note that many words in Hokkien still carry meanings they had in Classical Chinese (Old Chinese), whereas the equivalent words in Mandarin have shifted in meaning or fallen out of useen.wikipedia.org. For example, the verb 走 is cháu in Hokkien meaning “to run (away), to flee,” preserving the old meaning “to flee” found in classical texts. In modern Mandarin, the cognate zǒu (走) has changed to mean “to walk,” a significant semantic shiften.wikipedia.org. Likewise, Hokkien’s word for “eye” (目珠, ba̍k-chiu) transparently comes from the classical term for “eye,” whereas Mandarin now uses a completely different word (眼睛, yǎnjīng)en.wikipedia.org. Such examples illustrate that Hokkien has conserved original words and meanings that Mandarin and others have altered over time. Phonologically, Southern Min dialects also keep ancient features like final consonants and tone distinctions reminiscent of Old Chinese and Middle Chinese. It is for these reasons that some scholars call Hokkien (and Southern Min in general) a living window into earlier stages of Chinese. While “oldest” is hard to quantify, Hokkien can be seen as one of the oldest surviving Sinitic languages in terms of lineage and linguistic features. Its direct line from Old Chineseen.wikipedia.org, and the preservation of old vocabulary/meaningsen.wikipedia.org, set it apart from later-developed varieties. In comparison, Mandarin Chinese underwent more drastic changes (due to influence from non-Han rulers, migrations, etc.), meaning Hokkien provides a valuable link to the ancient past of the Chinese language.
The Proto-World Hypothesis: Status and Debates
The Proto-World hypothesis (also known as Proto-Human) proposes that all human languages today stem from a single mother tongue far back in the Paleolithic eraen.wikipedia.org. In essence, it’s the idea of a common ancestor language from which all language families diverged. This concept is highly controversial and largely rejected by mainstream linguistsen.wikipedia.orgen.wikipedia.org. Historical linguistics typically can reliably reconstruct languages up to perhaps ~10,000 years ago (for example, Proto-Indo-European); beyond that, the signal gets lost due to millennia of change. Proto-World, if it existed, would date to tens of thousands of years ago (often conjectured around 50,000–100,000 years in the past), making traditional comparative methods infeasible. Most linguists therefore consider Proto-World speculative and not amenable to rigorous analysisen.wikipedia.org. We simply don’t have data or consistent correspondences across all languages to prove a single origin – languages could have emerged in multiple pockets or one, we cannot be sure with current evidence.
That said, a few linguists have championed the monogenesis idea. Merritt Ruhlen and colleagues, for example, attempted to find global etymologies – cognate words across disparate language families – to support a common originen.wikipedia.org. They pointed to similarities in very basic words (like pronouns or sound-imitative words) across many languages. However, critics argue these similarities can be due to chance, onomatopoeia, or physiological constraints (e.g. mama for “mother” arises independently). In the 2010s, some quantitative studies brought renewed attention. In 2011, Quentin Atkinson published a study in Science analyzing the phoneme diversity of 504 languages. He found that languages in Africa tend to have larger sound inventories, and diversity declines with distance from Africa, mirroring the “serial founder effect” seen in human geneticsen.wikipedia.org. This was presented as evidence that language originated once (in Africa) and spread globally with migrating humansen.wikipedia.org. If true, it would support a Proto-World hypothesis by suggesting a single point of origin. However, Atkinson’s methodology was hotly debated. Other researchers (e.g. Hunley, Bowern, & Healy 2012) re-examined the data and rejected the serial founder effect model for languageen.wikipedia.org, arguing that factors other than a single origin could explain phoneme distribution. In fact, no consensus has emerged from such statistical studies – results seem sensitive to methodology and assumptions.
Beyond linguistics, genetic and anthropological findings indicate modern humans originated in a single population in Africa, which implies a single original language community. But since languages change so rapidly, direct evidence of Proto-World is effectively lost. The current scientific standing is that Proto-World remains an unproven hypothesis. Mainstream linguists largely regard attempts at finding a mother tongue as fringe science, noting a lack of testable evidence and the impossibility of verification over such a time spanen.wikipedia.org. Nonetheless, the topic continues to intrigue. Ongoing debates often revolve around whether statistical methods or ultra-long-range comparisons can overcome the noise of thousands of years. So far, no “smoking gun” for a Proto-World vocabulary or grammar has convinced the field. Most consider that while human language likely did have some origin point, every trace of that original tongue has been scrambled by tens of millennia of linguistic evolution. In summary, Proto-World (a single mother language) is an interesting idea with some tantalizing hints (like global linguistic patternsen.wikipedia.org), but it remains highly speculative and widely rejected without robust evidenceen.wikipedia.org. The prevailing view is that we may never be able to know for sure, barring a breakthrough in methodology or discovery of ancient recordings – a prospect most deem unlikely.
Singapore’s Linguistic Diversity and Singlish
Singapore is renowned for its linguistic diversity, reflecting its multi-ethnic population. The country has four official languages – English, Malay, Mandarin Chinese, and Tamil – and most Singaporeans grow up bilingual or trilingual. Malay is historically the national language, Mandarin is spoken by the Chinese majority (alongside other Chinese “dialects” like Hokkien, Teochew, and Cantonese), Tamil is used by a large segment of the Indian community, and English serves as the lingua franca and medium of education. This mix makes daily life in Singapore a rich tapestry of codeswitching and multilingual interaction. For example, among the Chinese community, older generations still speak Hokkien (Southern Min) at home – in 2017 about 1.5 million people in Singapore could speak Southern Minen.wikipedia.org – while younger Chinese tend to use Mandarin due to past language policies. Likewise, many Malays speak both Malay and English, and Indian Singaporeans often speak Tamil and English, sometimes with a third language like Malay. The result is that Singaporeans frequently alternate between languages depending on context – a practice known as code-switching. It’s common to see someone use English in a workplace or school, speak Mandarin or Hokkien with their grandparents, and perhaps use Malay phrases with neighbors.
A unique product of this multilingual environment is Singlish, or Colloquial Singaporean English. Singlish is an English-based creole that has developed in Singapore, mixing English with vocabulary and grammar influences from Malay, Hokkien, Cantonese, Tamil and other languagesbehance.net. It is not simply broken English, but a full-fledged dialect with its own rules and expressions, born out of the constant code-switching and blending of languages in daily Singaporean life. For instance, a Singlish sentence might be: “Tomorrow got meeting or not? If don’t have, I go home first lah.” – which strings English words in a Chinese/Malay grammatical order and adds the particle “lah” (a trademark exclamation in Singlish). Singlish is beloved as a marker of local identity and solidarity. You’ll hear it in hawker centers, among friends, and in local media or social media (often for humor or authentic flavor). At the same time, the Singapore government historically discouraged Singlish in favor of standard English. Campaigns like “Speak Good English Movement” and education policies push for formal English, especially in schools and official communications. As a result, most Singaporeans are diglossic – they switch between standard English and Singlish based on context. In formal settings or with foreigners, they’ll use proper International English; among family or in casual conversation, the code-switch to Singlish comes naturally. This happens even within a single conversation – a phenomenon documented by sociolinguists as style-shifting. Indeed, Singlish persists robustly despite official disapproval: it is “routinely used by ordinary people” in daily life, even though it’s frowned upon in schools and mainstream mediabehance.netbehance.net. Studies of Singapore’s speech patterns show that citizens expertly calibrate their language: an educated Singaporean might use near-perfect Oxford English in a meeting, then seamlessly slip into “Eh bro, you makan already or not?” (Singlish for “Have you eaten?”) with a colleague at lunch. This fluid code-switching reflects Singapore’s linguistic ecology – English provides a common platform for communication across ethnic lines, while the ethnic languages and dialects (Malay, Tamil, Hokkien, etc.) and Singlish provide cultural warmth, nuance, and identity. In recent years, Singlish has gained recognition as a cultural treasure; it’s been studied in universities and even features in the Oxford English Dictionary (several Singlish words like “lah”, “kampong”, “hawker centre” are now documented). Singapore’s linguistic diversity thus spans official multilingualism and informal creole usage. Current sociolinguistic research in Singapore often focuses on how code-switching to Singlish serves pragmatic functions – for example, to signal informality, sarcasm, or local identity – and how younger Singaporeans navigate the balance between speaking global English and preserving their local speech forms. In sum, Singapore is a microcosm of linguistic diversity: four official languages and many dialects coexist, and out of this blend has emerged Singlish, a vibrant creole that Singaporeans toggle into as part of their everyday linguistic repertoirebehance.net.
AI in Endangered Language Documentation and Digital Dialects
AI is increasingly being leveraged to document, preserve, and revitalize endangered languages around the world. Many minority languages face extinction due to dwindling native speakers, but AI tools offer new hope by rapidly collecting and analyzing linguistic data. For instance, major tech companies have initiatives for indigenous and endangered languages: Google’s AI for Indigenous Languages and Microsoft’s Project ELLORA are two prominent efforts. These projects use technologies like speech recognition, machine translation, and OCR to help create digital archives of endangered languages. A real-world example is the Cherokee language – the Cherokee Nation partnered with Google to integrate Cherokee into Google’s tools and fonts, enabling online Cherokee translation and helping younger generations engage with their heritage tongue. Such collaborations mean that even languages with small speaker bases can gain digital presence – from having keyboards and Unicode support to having basic translation or text-to-speech systems.
Automated transcription is one critical aid from AI: researchers can feed audio recordings of rare languages into machine learning models (often after some training on the language’s sounds) to get transcriptions, which tremendously speeds up the documentation of oral traditions. Likewise, machine translation techniques, even if originally designed for big languages, are being adapted to serve endangered ones. For example, AI can align a small language with a larger related language to provide rough translations, useful for producing bilingual texts or teaching materials. There are also projects using chatbots and voice assistants as language tutors – an AI chatbot that converses in, say, an Australian Aboriginal language or the Celtic language Welsh can encourage new learners and provide practice, even if fluent human teachers are scarce. Non-profit and academic groups are active in this space too: Mozilla’s Common Voice project crowdsources voice data for many low-resource languages to train speech models; and the Living Tongues Institute and similar organizations use AI to analyze linguistic data they gather.
A key method in revitalization is creating digital dictionaries and lexicons. AI can help by scraping texts (if any exist) or even reconstructing likely words by comparing related dialects. For example, algorithms have been used to reconstruct unattested words of Native American protolanguages, which in turn can guide revitalization by reviving old terms. In one case, a team used a neural network to hypothesize ancient forms of Austronesian words, effectively helping fill gaps in the historical record. While this is about ancient proto-languages, similar techniques can guess missing words in modern endangered languages by leveraging cognates from sister languages.
AI is also contributing to the creation of “new digital dialects.” In multilingual communities, technology (especially AI translators and social media) is fostering hybrid ways of speaking. Machine translation tools like Google Translate, which are widely used, can inadvertently introduce literal translations or mix-ups that locals adopt in jest, forming new slang. More significantly, NLP tools enable people to mash languages together easily online – producing what some call “creolized digital dialects”. For example, in parts of Africa, the availability of translation between English and local languages via apps has led to a mixed code in texting that blends both, essentially a tech-mediated code-switching. AI voice assistants that handle multiple languages (like English and Spanish in the same conversation) encourage code-mixed utterances. Thus, AI is not only preserving old languages but also shaping new linguistic blends by facilitating communication across tongues. Researchers observe that digital communication is accelerating language evolution, as seen in Singapore’s case where technology helps English and Malay/Chinese elements fuse (Singlish), or in online gaming communities where a lingua franca with game-specific jargon emerges – one might view these as “new dialects” born in cyberspace.
Several prominent projects highlight the use of AI for endangered languages:
Microsoft Project ELLORA: Works on data collection and NLP for languages in India and elsewhere that have little digital footprint. By developing datasets and models for these languages, it enables tools like speech-to-text in those communities.
Google’s Indigenous Languages Initiative: Aside from Cherokee, Google has worked on Maori (New Zealand), Inuktitut (Canada), and Quechua (South America), among others, adding them to interfaces and translation platforms. This often involves training AI on whatever limited text is available, plus new data from native speakers.
The Masakhane Project: An open-source community of African NLP researchers who apply AI translation models to African languages (many of which are endangered or lack resources). They successfully created machine translation for dozens of African languages by pooling expertise, effectively using AI to bridge African languages and preserve them in digital form.
Endangered Language Documentation (ELAR & PARADISEC): These are archives where linguists upload recordings of endangered languages. Recently, they have started using AI to index and parse these recordings. For example, AI can detect speaker turns, identify frequently used words, or cluster similar sounds – valuable for linguists making dictionaries.
Despite successes, challenges remain. A major one is the data scarcity for truly endangered languages – many have no written tradition and only a handful of elderly speakers, giving AI little to learn from. As a result, many endangered languages are still digitally under-represented, and there’s a risk of a digital divide where big languages get ever more AI support while small ones are left behind. Efforts are underway to mitigate this by creatively generating training data (through crowd-sourced recordings, etc.) and by transfer learning (leveraging models of related languages). Ethical considerations also arise: communities must guide how their language data is used and ensure AI serves their needs (and doesn’t, for instance, produce incorrect or culturally insensitive outputs). Nonetheless, the overall trend is heartening – AI, when thoughtfully applied, is becoming a crucial ally in language preservation. It accelerates documentation (what used to take linguists years in the field can be processed in weeks by AI), helps create educational tools (like apps and dictionaries), and even resurrects aspects of lost languages (as in reconstructing ancient tongues or revitalizing near-extinct ones). As we move forward, we can expect to see more endangered languages find a voice in the digital world – whether through speech interfaces, predictive text in those languages, or online communities communicating in them – ensuring that linguistic diversity is carried into the future with the help of AI.
Sources: The information above is supported by research and examples from computational linguistics and sociolinguistics, including the Berkeley AI reconstruction of Austronesian languages, phylogenetic analyses of GPT models, linguistic studies on Hokkien and Old Chineseen.wikipedia.orgen.wikipedia.org, discussions of the Proto-World hypothesis in linguistic literatureen.wikipedia.orgen.wikipedia.org, sociolinguistic observations in Singaporebehance.net, and reports on AI projects for language preservation, among others. Each citation in the text corresponds to the specific source lines for verification.