16 minute read

AI Imperialism

AI Imperialism: Western Dominance and the Future of Global Technology

In the rapidly evolving landscape of artificial intelligence (AI), the emergence of transformer models has marked a significant milestone. Among these, OpenAI’s GPT-3 stands out as a groundbreaking achievement, yet its dominance raises critical questions about the concentration of power, legal ambiguities, and global technological equity. This article delves into the phenomenon of AI imperialism, exploring how Western dominance shapes the future of global technology and the implications for developing nations.

Note: This article is written from the lens of Indian living in India and has good understanding of history and culture. There may be differences in perspective in this article.

Introduction

Artificial Intelligence (AI) has transformed from a speculative concept into a pivotal technology influencing various sectors worldwide. Since the discovery of transformer models in 2017, the AI field has witnessed remarkable advancements. However, amidst this progress, OpenAI’s GPT-3 has emerged as an unparalleled success, overshadowing numerous other transformer models. This dominance is not merely a result of technological prowess but is deeply intertwined with vast computational resources, corporate backing, and strategic maneuvering within legal frameworks. This article examines the roots of OpenAI’s success, the legal and ethical challenges it presents, and the broader implications of Western dominance in AI development.

GPT3 was precursor of ChatGPT. ChatGPT is UI and it used GPT3.5 model. But, what data they used to make that model? How many parameters that model has? What training techniques they used to make it? All these kinds of information is not available to general public. They only information which is available in public domain is ChatGPT was released with GPT3.5 model. In this article we are not distinguishing much between GPT3 and GPT3.5, because that is not the debate here.

In this article when I referring Western culture in the current technological context then I am referring to USA. Otherwise Western means Europe and America.

The Emergence of Transformer Models

Discovery of Transformers (2017)

In 2017, the introduction of transformer models revolutionized natural language processing (NLP). Unlike previous architectures, transformers utilize self-attention mechanisms, allowing them to process data more efficiently and handle long-range dependencies in text. This breakthrough enabled more sophisticated language understanding and generation, laying the foundation for models like GPT-3.

Evolution Since 2017

Since their inception, transformer-based models have proliferated across the AI landscape. From BERT and RoBERTa to T5 and beyond, numerous variants have been developed, each pushing the boundaries of what AI can achieve in language-related tasks. Despite this surge in innovation, few models have matched the commercial and practical success of GPT-3 and ChatGPT, highlighting a significant disparity within the transformer model ecosystem.

The GPT-3 Phenomenon: Success Factors

Compute Power and Resources

GPT-3’s unprecedented success is largely attributable to the immense computational resources invested in its development, it was 175 billion parameters model. At that no one even could have thought a model can have such a high parameters and it could be useful. Training such a colossal model requires vast amounts of data, processing power, memory, and energy, and it wasavailable only to few organizations. This barrier to entry ensures that only a handful of entities can compete at the highest levels of AI development, reinforcing the dominance of well-funded corporations like OpenAI.

Data Acquisition and Support

Another critical factor behind GPT-3’s prowess is the extensive data scraped from the internet used to train the model. This massive dataset provides GPT-3 and ChatGPT with a broad and nuanced understanding of human language. Additionally, corporate support, particularly from Microsoft, has been instrumental in providing the necessary infrastructure and financial resources to develop and deploy ChatGPT effectively. This combination of vast data and robust support systems sets GPT-3 apart from its peers.

The practice of scraping data from the internet to train AI models like GPT-3 raises significant legal and ethical questions. While data scraping is a common method for gathering training data, it often operates in legal gray areas. Existing laws have not fully caught up with the rapid advancements in AI, creating ambiguities about what is permissible. This lack of clear regulation allows companies like OpenAI to push the boundaries of AI development, sometimes at the expense of privacy and intellectual property rights.

Historical Context of Innovation vs. Regulation

The tension between innovation and regulation is not unique to AI. Historically, technological breakthroughs such as telephony, the internet, electricity, automobiles, and petroleum initially operated in regulatory voids. Often, significant advancements challenge existing laws and societal norms, prompting regulatory responses only after the fact. AI is following a similar trajectory, where groundbreaking developments occur swiftly, outpacing the ability of legal systems to adapt and regulate effectively.

The problem with innovation is if you put stringent laws in place before something takes off then it will never take off. For example, European Union (EU) has strict rules on data protection, safety and privacy, due to this it is difficult for them to participate as USA or China is able to. On the other hand if you give it free hand then it can create chaos in the market and society.

Corporate Dynamics: OpenAI vs. Google

Origins and Strategies

Transformer research originated at Google, with the introduction of the transformer architecture marking a significant advancement in AI. Despite Google’s pioneering role, OpenAI has emerged as a more prominent player in developing large-scale transformer models like GPT-3.5 and making ChatGPT. This shift raises questions about why a smaller, less established entity like OpenAI could surpass a tech giant like Google in this domain.

Risk-Taking and Innovation

One plausible explanation is OpenAI’s greater willingness to take risks and invest heavily in cutting-edge research without the constraints typically faced by larger corporations. OpenAI, being more nimble and less burdened by existing corporate structures and legacy projects, could focus intensely on developing GPT-3.5. They didn’t have much to lose. In contrast, Google, with its vast array of projects and bureaucratic layers, might find it challenging to allocate the same level of resources and focus required to achieve similar breakthroughs. From the point of view of funding OpenAI has money available via Microsoft. And Microsoft without making hands dirty in the game achieved what they wanted. Google could also have done this but probably they were the orginator of Transformer architecture so they could not see value in engaging with OpenAI.

Western Dominance in AI Development

Historical Patterns of Innovation

The dominance of Western countries in major scientific and technological advancements is a recurring historical pattern. From the Industrial Revolution to the Digital Age, Western entities have often been at the forefront of innovation, setting global standards and shaping technological landscapes. This trend extends to AI development, where Western companies and institutions lead in research, funding, and commercialization.

Intellectual Property and Cultural Appropriation

A critical aspect of Western dominance is the tendency to appropriate ideas from non-Western sources without proper acknowledgment. Historical examples include the invention of zero, which originated in ancient Indic civilizations but was later adopted and popularized by Western scholars. This pattern of intellectual appropriation undermines the contributions of non-Western cultures and reinforces Western hegemony in scientific and technological domains. Whatever technology write you pick up today you would find trace to Greek civilization or scientist from Roman Empire or Industrial Era scientists, but almost nil trace to Indian civilization. It’s not that the people in Indian civilization has not done anything, but west hisitate to give credit and make issue over sharing they knowledge and copyrights. Even for the contribution of Zero they sarcastially say Indian Civilization has given zero contribution to science and technology. You pickup any popular journal or youtube channel or traditional media publications and you will reach to this conclusion the west doesn’t give credit to Indian for their work.

As AI technologies like GPT-3 push the boundaries of what is legally permissible, they set precedents that can shape future developments. The current legal frameworks are insufficient to address the complexities and ethical dilemmas posed by AI, leading to a regulatory lag. This gap allows for rapid innovation but also creates uncertainties about the legality and ethicality of AI practices.

Regulatory Responses to Technological Advances

Historically, significant technological breakthroughs have eventually led to the establishment of new laws and regulations. Initially, innovations often disrupt existing legal systems, necessitating updates and new policies to address emerging challenges. In the case of AI, we can expect similar developments, where regulatory bodies will eventually implement measures to govern data usage, privacy, and ethical AI deployment. As of today this is evolving stage and trying to maintain a balance between innovation and regulation.

Ethical Concerns Post-Development

Lack of Credit and Reward

One of the primary ethical concerns surrounding AI models like GPT-3 is the inadequate credit and reward given to the sources and contributors of the training data. While these models leverage vast amounts of publicly available data, the individuals and organizations that generated this data are often left unacknowledged and uncompensated. This lack of recognition raises questions about fairness and the ethical use of publicly sourced information. Initially they could not recognize because of certain regions is understandable but now when they are making lots of money they still don’t think of rewarding. May be they are figuring out, how to reward, how much to reward or accumulating more money to make themselves fail safe.

Western Practices of Appropriation

Western entities have a historical tendency to appropriate ideas and innovations from non-Western cultures without proper acknowledgment or compensation. This practice not only diminishes the contributions of those cultures but also perpetuates a cycle of intellectual imperialism. In the context of AI, this means that foundational ideas and data from diverse cultures are utilized to build powerful models, further entrenching Western dominance without due recognition.

Western Control Over Critical Technologies

Nuclear and Petroleum Technologies

The West’s control over critical technologies such as nuclear energy and petroleum has long-term geopolitical implications. After pioneering these technologies, Western nations often imposed regulations that restrict their proliferation, compelling other countries to depend on Western firms for access. This control ensures that Western powers maintain a strategic advantage in these vital sectors. Poor contries neither have money to buy western technology, nor they can use existing technology in the name of safety or environment, nor could build something like western countries have.

Impending AI Control

A similar pattern is emerging in the realm of AI. As Western companies like OpenAI, Google, and Microsoft continue to lead in AI development, they establish control over the technology’s future trajectory. This dominance could lead to a scenario where advanced AI technologies become unaffordable or inaccessible to poorer nations, exacerbating global inequalities and reinforcing Western hegemony in the AI sector.

Global Technological Inequities

Impact on Developing Countries

Western dominance in AI development has profound implications for developing countries. As AI technologies become integral to economic growth and innovation, countries unable to access or develop these technologies risk falling further behind. The high costs associated with AI research and deployment, coupled with limited access to computational resources, create significant barriers for poorer nations striving to compete on the global stage.

India vs. China: A Comparative Analysis

India and China present contrasting cases in the global AI landscape. While India is making strides in AI development, its progress is relatively very slow compared to China’s rapid advancements. China has invested heavily in AI research, education, infrastructure, and talent acquisition, positioning itself as a formidable competitor in the AI domain. In contrast, India’s talent pool is often drawn towards Western AI firms, such as those working on Gemini, ChatGPT, Claude, Meta, and SAP, rather than contributing to domestic AI initiatives. This talent migration hampers India’s ability to build a robust AI ecosystem, further widening the technological gap between the two nations. Not only this, they are developing solutions only around English language, while China is focussed only on developed their solution in their own language.

India’s talent pool is not of much use for India, it is useful for the world and especially English speaking world. Neither it useful for creating intellectual property for India. This puts India at a disadvantage position, Indian youth have knwoledge, lifestyle and money but it is not of much use for Indian society and Indian government.

Talent Migration and Brain Drain

Brain Drain to Western AI Firms

India boasts a vast pool of talented AI professionals. However, many of these individuals are employed by major Western AI companies, either directly or indirectly, through global collaborations and remote work arrangements. This trend, often referred to as “brain drain,” results in the loss of valuable human capital that could otherwise contribute to India’s domestic AI advancements.

Consequences for Domestic AI Development

The migration of talent to Western firms undermines India’s efforts to develop a competitive AI industry. Without sufficient investment in local AI research and development, coupled with supportive policies to retain top talent, India risks becoming a peripheral player in the global AI arena. This scenario not only limits India’s economic growth but also perpetuates global technological inequities.

The Ethical Responsibility of AI Pioneers

Recognition and Reward

AI pioneers bear an ethical responsibility to recognize and reward the sources and contributors of their training data. Proper acknowledgment ensures that individuals and communities that generated the foundational data benefit from the advancements built upon their contributions. This practice fosters a more equitable and inclusive AI ecosystem.

Balancing Innovation and Fairness

Balancing groundbreaking innovation with ethical considerations is crucial for sustainable AI development. AI companies must navigate the fine line between pushing technological boundaries and ensuring fairness, transparency, and accountability. Implementing ethical guidelines and equitable practices can help mitigate the negative impacts of AI imperialism and promote a more balanced global technological landscape.

Conclusion and Future Outlook

Summary of Key Points

The phenomenon of AI imperialism, characterized by Western dominance in AI development, raises significant legal, ethical, and geopolitical concerns. OpenAI’s success, driven by vast computational resources and corporate support, exemplifies the concentration of power within a few Western entities. The legal ambiguities surrounding data scraping and intellectual property further complicate the landscape, while the lack of recognition for non-Western contributions perpetuates historical patterns of intellectual imperialism.

Future Directions

To achieve a more equitable AI future, it is imperative to foster global collaboration, invest in AI research across diverse regions, and establish clear legal and ethical frameworks. Developing nations must be supported in building their AI capabilities, ensuring that technological advancements benefit a broader spectrum of the global population. Additionally, AI companies should adopt practices that recognize and reward the sources of their training data, promoting fairness and inclusivity.

Final Thoughts

AI imperialism poses a significant challenge to global technological equity. As AI continues to shape the future of various industries and societies, addressing the concentration of power and ensuring equitable access to AI technologies becomes paramount. The global community must work collectively to redefine the norms of AI development, fostering an environment where innovation thrives alongside fairness and inclusivity. Only then can AI truly become a force for positive change on a global scale.

References

  • Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is All You Need. https://arxiv.org/abs/1706.03762. This seminal paper introduces the transformer architecture, revolutionizing natural language processing.
  • BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). https://arxiv.org/abs/1810.04805. Details the development of BERT, another influential transformer-based model.
  • Google AI Blog. Introducing the Transformer. (2017). https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html Google’s announcement and details about the transformer model.
  • Brown, T.B., Mann, B., Ryder, N., et al. (2020). Language Models are Few-Shot Learners. https://arxiv.org/abs/2005.14165. Introduces GPT-3 and discusses its capabilities and the resources required for its development.
  • OpenAI. (2020). GPT-3: Language Models are Few-Shot Learners. https://openai.com/research/gpt-3. Official publication and details about GPT-3 from OpenAI.
  • Microsoft Partnership with OpenAI. Microsoft Invests $1 Billion in OpenAI to Support Development of Artificial General Intelligence. (2023). https://blogs.microsoft.com/blog/2023/09/22/microsoft-invests-1-billion-in-openai/. Details of Microsoft’s financial and infrastructural support for OpenAI.
  • Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press. Explores the ethical and legal implications of AI development, including data scraping.
  • European Commission. (2021). Proposal for a Regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Details upcoming regulations that address AI’s legal boundaries.
  • Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. PublicAffairs. Discusses data privacy and ethical concerns related to large-scale data usage.
  • Vincent, J. (2020, July 20). OpenAI’s latest breakthrough is astonishingly powerful language model GPT-3. The Verge. https://www.theverge.com/2020/7/20/21330277/openai-gpt-3-language-model-ai-explained Analysis of OpenAI’s GPT-3 and comparison with other models like those from Google.
  • Sachs, J.D. (2020). The Age of Sustainable Development. Columbia University Press. Provides historical context on Western dominance in technological advancements.
  • Stark, J. (2004). Zero: The Biography of a Dangerous Idea. Penguin Books. Explores the history of the concept of zero, highlighting its origins and Western adoption.
  • Friedman, T.L. (2005). The World Is Flat: A Brief History of the Twenty-First Century. Farrar, Straus and Giroux. Discusses global technological advancements and the role of Western countries.
  • World Economic Forum. (2020). The Global AI Agenda: Trustworthy AI for a Better Future. https://www.weforum.org/reports/global-ai-agenda-trustworthy-ai-for-a-better-future. Analyzes global disparities in AI development and access.
  • Kshetri, N. (2021). AI and the Future of Global Inequality. Journal of International Development, 33(3), 456-472. https://onlinelibrary.wiley.com/doi/full/10.1002/jid.3500. Examines how AI advancements can exacerbate or mitigate global inequalities.
  • IDC. (2023). AI Adoption in Developing Countries: Trends and Challenges. https://www.idc.com/getdoc.jsp?containerId=AI-Adoption-Developing-Countries. Reports on AI adoption rates and challenges in developing nations.
  • Chaudhry, A. (2022). Brain Drain and AI: The Impact on India’s Tech Ecosystem. Economic and Political Weekly, 57(12), 45-52. https://www.epw.in/journal/2022/12/cover-story/brain-drain-and-ai.html Discusses the effects of talent migration on India’s AI sector.
  • OECD. (2021). International Migration Outlook 2021. https://www.oecd.org/migration/international-migration-outlook-1999124x.htm Provides data and analysis on global migration trends, including skilled professionals.
  • World Bank. (2020). Global Knowledge Report 2020: Skills for a Digital World. https://www.worldbank.org/en/publication/global-knowledge-report. Examines the distribution of digital skills and the impact of talent migration.
  • Floridi, L., & Cowls, J. (2019). A Unified Framework of Five Principles for AI in Society. Harvard Data Science Review, 1(1). https://doi.org/10.1162/99608f92.8cd550d1. Outlines ethical principles for AI development and deployment.
  • Jobin, A., Ienca, M., & Vayena, E. (2019). The Global Landscape of AI Ethics Guidelines. Nature Machine Intelligence, 1(9), 389-399. https://www.nature.com/articles/s42256-019-0088-2. Reviews various ethical guidelines for AI from around the world.
  • IEEE. (2019). Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Artificial Intelligence and Autonomous Systems. https://ethicsinaction.ieee.org/. Provides comprehensive guidelines for ethical AI development.
  • Lewis, M., & Poole, M. S. (1990). Elusive Quest: A New History of the Soviet Space Program. NASA History Series. While focused on the Soviet space program, it provides context on Western control over critical technologies.
  • Kell, G. (2018). The Power Surge: Energy, Opportunity, and the Battle for America’s Future. Simon & Schuster. Discusses Western control over petroleum and energy technologies.
  • Kumar, R. (2022). Global Technology Governance and the West’s Role. International Affairs Journal, 98(4), 1025-1043. https://www.internationalaffairsjournal.org/global-tech-governance Analyzes Western influence in global technology governance, including AI.
  • UNESCO. (2021). Recommendation on the Ethics of Artificial Intelligence. https://unesdoc.unesco.org/ark:/48223/pf0000373434 Provides guidelines for ethical AI development and global cooperation.
  • World Economic Forum. (2020). Shaping the Future of AI: A Multistakeholder Approach. https://www.weforum.org/reports/shaping-the-future-of-ai-a-multistakeholder-approach Discusses collaborative approaches to governing AI for equitable outcomes.
  • Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—and where they can’t (yet). McKinsey Quarterly. https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/where-machines-could-replace-humans-and-where-they-cant-yet. Explores the future of AI and its impact on various sectors.
  • Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. https://dl.acm.org/doi/10.1145/3442188.3445922. Critiques the ethical implications of large language models like GPT-3.
  • Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2083), 20160360. https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360 Explores the ethical considerations surrounding data use in AI.
  • West, D. M. (2018). The Future of Work: Robots, AI, and Automation. Brookings Institution Press. Discusses the impact of AI and automation on global workforces and economies.

Updated:

Categories:

Tags:

You may also enjoy