AI Deciphers Ancient Mesopotamian Scripts

Step aside, Indiana Jones! There’s a new explorer in town, and it’s got a knack for ancient languages. In an unprecedented breakthrough, a team of archaeologists and computer scientists have empowered an AI to decode and translate cuneiform tablets—the ancient clay tablets that hold the secrets of Mesopotamian civilization. Yes, you heard that right! AI is now helping us uncover the mysteries of human history, one ancient script at a time.

Cuneiform tablet: Sumerian dedicatory(?) inscription

In a paper published in the PNAS Nexus, researchers shared their success in using an AI program with neural machine learning capabilities to translate Akkadian texts, an ancient language of Mesopotamia. “Think of it as Google Translate for the Mesopotamians,” joked one researcher.

The process is not just a cool party trick for the next archaeologists’ gathering. It’s addressing a significant issue—the overwhelming number of untranslated cuneiform tablets. We’re talking hundreds of thousands of ancient clay tablets inscribed with social, economic, political, and scientific happenings of ancient Mesopotamia waiting to be understood. But with limited experts capable of reading and translating these historical treasures, the past remained buried in the script. Until now, that is.

AI Translations: A Story of Success and…Hallucinations?

The AI, relying on its advanced neural machine learning translations, has been successfully decoding formal Akkadian texts, such as royal decrees and omens. It’s like it was born to read royal proclamations!

However, when faced with more poetic and literary texts, such as letters from priests or tracts, the AI experienced what researchers fondly refer to as “hallucinations.” No, the machine didn’t start seeing unicorns and rainbows; this term simply means the AI produced translations that were entirely unrelated to the original text. Imagine feeding it a solemn priest’s letter and getting back a recipe for Mesopotamian meatloaf. Fascinatingly unpredictable!

The goal of this AI project is more than just fancy translations. It aims to foster a human-machine collaboration to assist scholars or students of ancient languages. The researchers’ dream is for their AI to become an invaluable tool in the arsenal of anyone looking to explore our ancient past.

And the best part? The source code for this revolutionary AI is available on GitHub under the name “Akkademia.” Additionally, the researchers are working on an online application called the “Babylonian Engine.” It’s history-meets-technology, and we’re here for it!

In conclusion, we’ve officially entered an era where we can uncover ancient wisdom using the power of artificial intelligence. While the “hallucinations” remind us that AI still has a long way to go, the advancements in AI translations are a testament to the infinite potential of technology. So buckle up, history enthusiasts! The AI-powered journey into our rich past has just begun.

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  • Tom Serrano

    Thomas "Tom" Serrano, is a proud Cuban-American dad from Miami, Florida. He's renowned for his expertise in technology and its intersection with business. Having graduated with a Bachelor's degree in Computer Science from the East Florida, Tom has an ingrained understanding of the digital landscape and business.Initially starting his career as a software engineer, Tom soon discovered his affinity for the nexus between technology and business. This led him to transition into a Product Manager role at a major Silicon Valley tech firm, where he led projects focused on leveraging technology to optimize business operations.After more than a decade in the tech industry, Tom pivoted towards writing to share his knowledge on a broader scale, specifically writing about technology's impact on business and finance. Being a first-generation immigrant, Tom is familiar with the unique financial challenges encountered by immigrant families, which, in conjunction with his technical expertise, allows him to produce content that is both technically rigorous and culturally attuned.

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