Can AI Decipher the Indus Script?

The Indus Valley Civilization, one of the world's earliest, left behind a script that has puzzled researchers for a century. While AI has shown promise in deciphering ancient languages like Linear B and Ugaritic, the Indus script presents unique challenges. This article explores the potential and limitations of using AI to unlock the secrets of this ancient writing system.

The Challenge of the Indus Script

The Indus script, found on seals, pottery, and bones, consists of hundreds of symbols, making it a complex puzzle for researchers. Unlike Linear B, which was deciphered using its relationship to ancient Greek, the Indus script needs a known linguistic relative, making traditional decipherment methods difficult.

Early Successes with AI and Ancient Languages

In 2019, Jiaming Luo and his team at MIT developed an AI algorithm that deciphered a significant portion of Linear B by analyzing patterns in language change over time. This breakthrough involved feeding the algorithm words in Linear B and its known relative, ancient Greek, allowing it to align words and achieve a remarkable accuracy of 67.3% in translation. The algorithm's speed, completing the task in hours compared to the potential years of manual decipherment, highlighted AI's potential in this field.

Applying AI to the Indus Script: Initial Attempts and Limitations

Applying this same model to the Indus script proves difficult due to the need for a known related language. However, AI has played a crucial role in confirming that the Indus script represents a language. Researchers have developed a computer program to analyze the patterns and frequency of symbols in the Indus script. By comparing these patterns to known linguistic and non-linguistic samples, the program showed that the Indus script exhibited characteristics of language, as opposed to mere symbols like those used in computer code.

Despite this success, a significant obstacle remains: identifying a script as a language and understanding its meaning. Deep learning, a powerful AI technique reliant on large datasets for pattern recognition, faces limitations with the Indus script due to the limited data available.

Doubts and Alternative Approaches

Some researchers express scepticism about AI's ability to decipher the Indus script due to its potential pictorial nature and the need for understanding historical symbolism, which current AI iterations struggle with. Mukhopadhyay suggests a more traditional, theory-driven approach, focusing on individual symbols and their historical context. Her research proposes that the Indus seals were used for administrative purposes like taxation and trade control. By examining words for "elephant" and "ivory" in Near Eastern languages from the same period, she argues for a Dravidian origin of the Indus language, potentially providing a crucial link for decipherment.

Further supporting the Dravidian hypothesis, the Sumerian word "Meluhha," potentially referring to the Indus Valley Civilization, finds resonance in the Sanskrit word "mleccha," meaning "foreign" or "non-Vedic," hinting at a linguistic connection. This connection is further strengthened by a 2021 research paper that argues for the presence of Proto-Dravidian in the Indus Valley, supported by linguistic and genetic evidence.

Data Limitations and the Future of AI in Decipherment

Another challenge lies in the quality and availability of data. Ancient texts are often damaged or incomplete, making accurate interpretation difficult. Creating comprehensive and reliable digital corpora of these texts is crucial for AI applications in decipherment. Efforts like the "Ancient Script Digitization and Archival (ASDA) of Indus Valley Artifacts using Deep Learning" project, led by Florida Institute of Technology researchers, aim to address this gap by using machine learning to digitize Indus script inscriptions from photographs.

While AI might not offer a fully automated solution to deciphering the Indus script, it can be a powerful tool for researchers who envision AI narrowing down possibilities and proposing candidate theories, leaving the final interpretation to human experts. This collaborative approach could significantly reduce the time and effort required for decipherment.

Conclusion

Deciphering the Indus script remains a complex challenge, with ongoing debates about the nature of the language and the best approaches to unlock its secrets. While AI has demonstrated its potential in deciphering ancient languages, its effectiveness with the Indus script is hindered by the lack of a known related language and the limited availability of high-quality data. However, ongoing efforts to create comprehensive digital corpora and develop more sophisticated AI algorithms, coupled with insights from researchers who emphasize historical and cultural context, offer hope for future breakthroughs in understanding this ancient civilization.

References

Ansumali Mukhopadhyay, B. Ancestral Dravidian languages in Indus Civilization: ultraconserved Dravidian tooth-word reveals deep linguistic ancestry and supports genetics.?Humanit Soc Sci Commun?8, 193 (2021). https://doi.org/10.1057/s41599-021-00868-w

McIntosh, Jane R. The Ancient Indus Valley: New Perspectives. Understanding Ancient Civilizations. Santa Barbara, CA: ABC-Clio, 2008.