Knowledge Regime in the AI Era: An Academic Definition

A knowledge regime refers to the structured system through which knowledge is conceptualized, produced, curated, legitimized, controlled, and disseminated within a society or across global networks. It represents the interplay of cultural, political, and technological forces that shape public understanding, societal norms, and power structures. Knowledge regimes operate as the mechanisms that govern not only what is considered legitimate knowledge but also how it flows, who controls it, and how it impacts the socio-political landscape. In the AI era, knowledge regimes are increasingly defined by their ability to navigate tensions between localization and internationalization, as well as their reliance on global linguistic systems, particularly the dominance of the English language.

The figure highlights the table of culturally sensitive topics in Thailand, emphasizing areas such as the monarchy, gambling, and cannabis, which reflect the localized priorities of the Typhoon model. This curated dataset aligns with efforts to maintain cultural norms through AI. However, the paper also mentions the Typhoon model’s claimed “pre-training” and “fine-tuning” processes, which deviate from conventional definitions. Despite referencing foundational models like Llama 3.2/3.3 and Nemotron — renowned for their detailed accounts of hardware setups (e.g., dark fiber, pods), saving checkpoints, and extensive training times — the Typhoon paper omits such specifics, raising questions about the rigor and scalability of its development. Furthermore, the approach described aligns more closely with fine-tuning rather than genuine pre-training. The inclusion of Qwen, a multimodal model with advanced VQA (Visual Question Answering) and OCR (Optical Character Recognition) capabilities, reflects an effort to enhance visual multimodal functions. However, it also raises concerns about the integration of global methodologies into a model primarily tailored to Thai cultural sensitivities. This synthesis underscores the broader tension between localization and leveraging global AI advancements. This synthesis underscores the broader tension between localization and leveraging global AI advancements, reflecting deeper dynamics governed by the concept of the Knowledge Regime. The Knowledge Regime operates as an abstract, overarching framework that transcends the more perceptible façade layers of data and AI sovereignty. While data and AI sovereignty are often framed as tools for asserting control over datasets, algorithms, and localized narratives, the Knowledge Regime encompasses a higher level of abstraction, focusing on the governance of knowledge itself — its creation, curation, dissemination, and legitimacy within global and local contexts. In this sense, data and AI sovereignty act as mechanisms within the Knowledge Regime, serving as intermediaries that reflect deeper societal and political priorities. For instance, the Typhoon model’s emphasis on culturally sensitive topics and Qwen’s alignment with state narratives illustrate how sovereignty frameworks often address immediate concerns about autonomy and cultural preservation. However, these efforts remain tied to the foundational structures and asymmetries of global knowledge production, largely dominated by English-centric frameworks and the immense resources of global AI leaders. The Knowledge Regime, therefore, provides a lens to analyze how these sovereignty efforts are shaped, constrained, or enabled by broader global dynamics, highlighting the complex interplay between localization, internationalization, and power.

At its core, a knowledge regime serves as a gatekeeper of knowledge (c.f. value-based/morality policy,) mediating between local priorities and global accessibility. This duality reflects the challenge of balancing cultural preservation with participation in an interconnected, globalized world. The production of knowledge within these regimes involves institutions such as universities, think tanks, media organizations, and increasingly, artificial intelligence models. These systems curate knowledge by filtering vast amounts of information, selecting what aligns with societal values or political goals while excluding or marginalizing narratives deemed disruptive or irrelevant. The control of knowledge through censorship, narrative framing, or algorithmic filtering ensures that dominant ideologies remain intact, even as knowledge regimes adapt to changing social and technological contexts.

The nature of knowledge itself within a regime is dynamic and contested. Knowledge is not monolithic but exists in various forms — explicit and implicit, authoritative and marginalized, legitimate and disruptive. Explicit knowledge is codified and universally accepted, often represented in scientific research or legal frameworks, while implicit knowledge derives from cultural practices, traditions, or lived experiences. The process of legitimizing knowledge is deeply influenced by power structures, as regimes determine what qualifies as valid knowledge based on epistemological, political, or economic considerations. For instance, scientific research funded by global institutions is often prioritized over local or indigenous knowledge systems, reflecting a hierarchical structure that favors dominant powers.

The dominance of English as a global lingua franca has further entrenched inequalities within knowledge regimes. Most foundational knowledge, whether scientific, technical, or cultural, is preserved and disseminated in English or other Latin-based languages, marginalizing non-Latin-based linguistic systems. This hegemony creates asymmetries in access to knowledge, as states with less proficiency in English face barriers in contributing to and benefiting from the global knowledge ecosystem. Foundational AI models, which rely on vast English-based corpora, exemplify this dynamic, as they shape global narratives and perpetuate linguistic dominance. Even multilingual models are typically anchored in English, with other languages relegated to secondary importance, amplifying the epistemological dependency of non-English-speaking regions.

This linguistic dominance creates a tension between localization and internationalization. Localization efforts focus on preserving cultural identity and sovereignty, often through the creation of gatekeeping mechanisms that prioritize local narratives over global ones. For example, Thailand’s Typhoon model aims to curate knowledge aligned with Thai cultural norms while filtering out content that challenges these norms. However, such localization efforts often struggle to gain traction against the sheer scale and accessibility of global knowledge systems. Internationalization, on the other hand, facilitates participation in global networks, allowing states to access cutting-edge research and technological innovation. Yet, this comes with trade-offs, as internationalization often requires aligning with global standards that may undermine local values.

In the AI era, the challenge of censorship and knowledge control is magnified. The global interconnectedness of knowledge reservoirs, dominated by platforms like OpenAI or Google, makes localized censorship efforts increasingly futile. Foundational models such as GPT-4 or Llama 3.2 rely on data curated from global sources, making it nearly impossible for localized systems to operate independently without engaging with these global infrastructures. Thailand’s historical attempt to create the “Mangkut” programming language, which aimed to use the Thai language itself as the coding syntax instead of English, provides a telling analogy. For instance, where a program in the C language might use printf("hello world");, Mangkut employed the syntax พิมพ์("สวัสดีโลก");. This effort sought to localize the programming paradigm by aligning it with the Thai linguistic and cultural framework. However, it faced significant adoption challenges, as the global computing ecosystem—spanning hardware, software, and educational standards—was deeply entrenched in English, making Mangkut impractical for broader use. This illustrates the broader challenges of creating localized systems that diverge from established global standards. Similarly, efforts to create localized AI systems often fail to compete with global foundational models, as they lack the resources, scale, and interoperability needed to thrive.

The accessibility of global knowledge reservoirs further complicates the role of knowledge regimes. Even as localized systems attempt to gatekeep content, individuals increasingly access unfiltered global knowledge through open-source AI platforms or translation models. This paradox underscores the limitations of censorship and highlights the necessity of hybrid knowledge regimes that balance local preservation with global integration. Translation models, for instance, offer a practical solution by bridging linguistic divides while preserving cultural integrity, enabling states to participate in the global knowledge economy without eroding their identity.

A knowledge regime in the AI era must therefore navigate the inherent dominance of global systems designed in English while addressing the demands of localization. It must reconcile the desire for cultural preservation with the inevitability of internationalization, ensuring that local narratives remain relevant in a global context. As foundational AI models continue to shape the flow of knowledge, states must shift their focus from restrictive gatekeeping to fostering adaptive systems that integrate local and global priorities. This requires investment in translation technologies, digital literacy, and policies that ensure equitable participation in the global knowledge ecosystem.

In conclusion, a knowledge regime is not merely a passive system of governance but an active mediator of power, culture, and technology. In the AI era, its relevance hinges on its ability to address linguistic asymmetries, balance localization and internationalization, and adapt to the realities of globalized knowledge flows. The challenge for laggard states lies in leveraging hybrid models that preserve cultural identity while embracing the opportunities of a connected world. This approach ensures that knowledge regimes remain dynamic, inclusive, and resilient in an era defined by rapid technological change and global interdependence.

Side Note: Proposal for a Translation Model Based on BERT

In light of the challenges faced by localized AI models like Typhoon, we propose an alternative approach: developing a translation model based on BERT. This model aims to bridge linguistic gaps and enable users in non-Latin-based linguistic systems to access the global knowledge reservoir, which is predominantly preserved in English and other Latin-based languages. Unlike attempts to build localized foundational models, which demand immense resources and often lack global interoperability, a translation-focused approach provides a pragmatic and scalable solution to empower third-world countries.

BERT (Bidirectional Encoder Representations from Transformers) is a robust foundation for natural language understanding and translation tasks. Its architecture captures contextual nuances in both source and target languages, allowing for high-quality translations. By training a BERT-based model on parallel corpora of local languages and English, such a system can effectively bridge linguistic divides. It would enable speakers of underrepresented languages to access global knowledge, including scientific research, technical documentation, and cultural content. Moreover, it would preserve cultural integrity by ensuring that translations remain both accurate and sensitive to local linguistic norms.

For many third-world countries, accessing global knowledge remains a significant barrier to development. English dominates the realms of science, technology, and education, creating disadvantages for non-English-speaking populations. A translation model based on BERT could address this inequity by opening access to educational resources such as textbooks, research papers, and online courses in local languages. Additionally, it would foster economic development by enabling local businesses to engage with global markets and resources, thus encouraging innovation and competitiveness. By democratizing knowledge, this approach would empower marginalized communities to participate in global discourse and benefit from international advancements.

Rather than competing with large foundational models like GPT-4 or Llama, which dominate general-purpose AI, a translation model based on BERT would focus on a niche but critical application. This strategy enhances the global relevance of local languages, providing a resource-efficient solution for accessing global knowledge while preserving cultural identity. Such a model aligns with broader international efforts to promote digital inclusivity and linguistic equity, ensuring that no language community is left behind in the age of AI.

A BERT-based translation model is more than a technological solution; it is a gateway for third-world countries to integrate into the global knowledge ecosystem. By addressing the linguistic asymmetries inherent in the current knowledge regime, it offers a pathway to equitable access while safeguarding local cultures. This approach represents a practical and impactful contribution toward a more inclusive and globally connected AI landscape.

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