How AI Learning Bengali on Its Own Highlights the Need for Global Collaboration

Not long ago, I came across an intriguing story: an AI system, without any direct programming, learned how to understand and process Bengali. Some hailed this as a breakthrough that highlights the sheer power and potential of AI. Others saw it as a warning sign—an indication that AI is developing in ways beyond our control. But the real issue is more fundamental: why did this AI have to learn Bengali on its own?

This incident reveals a larger problem. Despite the global nature of AI, the systems we are building are primarily trained on data that is overwhelmingly biased towards English and other Western languages. The fact that the AI had to “learn” Bengali indirectly points to the lack of intentional effort to include non-English data in its training. And while human languages have universal aspects, they are full of nuances—cultural, linguistic, and contextual—that cannot be easily translated or approximated. When AI models are trained on limited data or a narrow set of languages, they often fail to capture these nuances. As AI systems continue to advance, we must ask ourselves: should AI development be a race for dominance, or should it be a collaborative effort that benefits everyone?

The Case for Global Collaboration in AI Development

Artificial intelligence is shaping the world we live in, influencing everything from healthcare decisions to the advertisements we see on social media. But AI’s immense power comes with an equally immense responsibility: ensuring that the technology we build is fair, ethical, and inclusive. As we develop AI, we need to learn from history and embrace the power of openness and collaboration. Historically, intellectual progress has been accelerated through the sharing of knowledge across borders, languages, and cultures.

Take the European Renaissance, for example. Many credit this period with the birth of modern Western civilization, but what is often overlooked is the foundational role played by Islamic scholars. During the Islamic Golden Age (8th to 14th centuries), scholars across the Islamic world translated and expanded upon the knowledge of ancient Greece, Persia, India, and others. These texts were later translated into Latin and helped spark the intellectual awakening that became the Renaissance. The lesson is clear: openness to diverse perspectives drives innovation.

If we approach AI with a mindset of collaboration, we can ensure that it benefits all of humanity. “Fairness and equality should be the foundation of AI models, not an afterthought.”

Lessons from History: Openness Drives Innovation

History offers multiple examples of how open knowledge-sharing has led to great advancements. The Great Library of Alexandria, during its peak, was a hub of knowledge from various cultures and scholars, all working to advance science, philosophy, and literature. Similarly, the House of Wisdom in Baghdad during the Islamic Golden Age was a melting pot of scholars from different cultural and religious backgrounds, translating and enhancing the knowledge from earlier civilizations.

On the other hand, we’ve also seen how isolationism and censorship stifle innovation. During the Ming and Qing dynasties in China, inward-looking policies restricted the flow of knowledge, leading to a stagnation of technological and scientific advancement. This historical example shows the dangers of limiting intellectual exchange—something we must avoid repeating in the development of AI.

Modern-Day Models of Scientific Collaboration

Fortunately, there are contemporary models of global cooperation that show us the way forward. One such example is CERN, the European Organization for Nuclear Research. Established in 1954, CERN is a collaboration involving 23 member states and numerous observer countries. Through this cooperative model, CERN scientists discovered the Higgs boson in 2012—a monumental achievement in particle physics that reshaped our understanding of the universe.

Another modern example is the International Space Station (ISS), a collaborative effort involving space agencies from the U.S., Russia, Japan, Europe, and Canada. The ISS has facilitated countless scientific breakthroughs by pooling together resources, knowledge, and expertise from multiple countries. If these models can succeed in advancing our understanding of physics and space, why shouldn’t AI development adopt a similar collaborative approach?

Towards a Fair and Inclusive Artificial General Intelligence

The development of artificial general intelligence (AGI) should not be a race for technological supremacy. Instead, it should be a global collaboration that incorporates perspectives from diverse cultures, languages, and value systems. By fostering such openness, we can create AI that serves humanity as a whole, rather than reinforcing existing inequalities. “Instead of treating AI as a race for dominance, we should foster greater cooperation, just as the Renaissance thrived through the sharing of knowledge.”

Censoring or limiting AI models based on narrow motivations—be they political, cultural, or religious—would be a mistake. Doing so risks creating systems that are biased, limited, and incapable of understanding the complexity of the human experience. History shows us that openness leads to progress, and isolation leads to stagnation.

Conclusion: A Call for Global Action

As we move toward a future shaped by AI, the path we take matters. Will we allow a handful of countries or companies to dominate this critical technology, or will we come together to ensure that AI reflects the diversity and richness of the entire human experience? The choice is ours, and the stakes couldn’t be higher. By embracing global collaboration, we can build AI systems that are not only powerful but also fair, inclusive, and ethical.

Just as the International Space Station and CERN have demonstrated the power of cooperation, AI can be a tool that advances human progress for everyone—if we choose to build it that way. History has shown us that openness leads to progress, and the same will hold true for AI.

Let’s learn from the past, embrace cooperation, and ensure that AI serves all of humanity.

References

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