For centuries, humanity has grappled with the silent mysteries held within fragmented ancient texts. Whether it’s a crumbling papyrus from ancient Egypt or an eroded inscription from a Roman ruin, these relics are windows into our past, but often, the glass is shattered. Deciphering them is painstaking work, demanding years of specialized knowledge from epigraphers and papyrologists.

But what if an Artificial Intelligence could accelerate this process, even help piece together what’s lost? What if an LLM could venture where human eyes and minds struggle, revealing secrets long buried? It’s not science fiction. A groundbreaking project from Google DeepMind has brought this vision to life, demonstrating how large language models are becoming invaluable partners in the quest to understand our ancient heritage.

The Unbreakable Code? Why Ancient Languages Are So Hard

Imagine trying to read a book where half the pages are missing, many words are faded, and the language itself is no longer spoken. That’s the daily reality for scholars working with ancient texts. Here’s why it’s such a formidable challenge:

1. Fragmentation and Damage: Time is a relentless adversary. Inscriptions crack, papyri tear, and ink fades. What remains are often mere fragments, sometimes just a few letters or parts of words.
2. Lost Context: The cultural, social, and political contexts in which these texts were created are often vastly different from our own, making interpretation difficult even if the words are legible.
3. Dead Languages: Many ancient languages have no living speakers, meaning our understanding is derived solely from the surviving texts themselves, creating a self-referential puzzle.
4. Variations and Dialects: Even within a single ancient language, there could be numerous dialects, regional variations, and evolving orthographies over centuries, adding layers of complexity.
5. Human Error: Traditional decipherment is an intensely human process, susceptible to individual bias or oversight.

For decades, this work has relied on the intuition, expertise, and cross-referencing skills of highly specialized human scholars. It’s a testament to their dedication that we know as much as we do. But with the sheer volume of undeciphered or incomplete texts, the pace of discovery is often slow.

Enter the Machines: LLMs to the Rescue

Large Language Models (LLMs) have revolutionized our interaction with text. From generating creative content to summarizing complex documents, their ability to understand, process, and generate human-like language is unprecedented. At their core, LLMs are pattern recognition machines, trained on vast datasets of text to predict the next word in a sequence.

While most of us associate LLMs with modern languages like English or Python code, their underlying architecture – particularly the Transformer model – is language-agnostic. This means that, given enough data, an LLM can learn the patterns of any language, including ancient ones.

The key lies in shifting the problem from “translating from an unknown language” to “restoring a known ancient language.” For languages like ancient Greek, where a substantial corpus of texts exists, albeit often damaged, LLMs can learn the grammar, vocabulary, and stylistic conventions.

Ithaca: Google DeepMind’s Breakthrough

This brings us to Ithaca, a pioneering project developed by Google DeepMind in collaboration with the University of Venice, the Hellenic Institute, and other institutions. Named after the mythical home of Odysseus, Ithaca is an LLM designed to assist scholars in the restoration, attribution, and dating of ancient Greek inscriptions.

Source: DeepMind Blog

Ithaca isn’t designed to “decode” a completely unknown language from scratch, like the Indus Valley script. Instead, it works with epigraphic texts, which are inscriptions carved into stone, pottery, or other hard surfaces. These texts are often severely damaged, with many letters or even entire phrases missing due to erosion or breakage.

What Ithaca Does:

1. Text Restoration: This is Ithaca’s primary and most remarkable function. Given a damaged ancient Greek inscription with missing characters (represented by placeholders), Ithaca predicts the most probable missing letters or words. It provides several possible completions along with confidence scores, allowing scholars to weigh the options.
2. Geographical Attribution: Ithaca can also suggest the original geographical location where an inscription was made. This is crucial for understanding the text’s context and regional linguistic variations.
3. Dating: Beyond location, Ithaca can estimate the date an inscription was created, often within a range of 30 years. This provides valuable chronological context for historical events.

How Ithaca Works Under the Hood:

Ithaca is built on a Transformer neural network architecture, similar to the foundation of many modern LLMs. It was trained on the largest digital dataset of ancient Greek inscriptions, the Packard Humanities Institute (PHI) database, which contains over 179,000 inscriptions.

The model learns patterns of language, grammar, and even the stylistic idiosyncrasies that vary by time and region. When presented with a damaged text, it leverages this vast learned knowledge to generate probable restorations.

Think of it like this: If you give a human expert the partial phrase “…N THE RO…”, they might infer “IN THE ROME” or “ON THE ROAD.” An LLM does this at an incredible scale, considering all possible linguistic and historical probabilities it has learned from its training data.

Ithaca’s strength lies in its probabilistic output. It doesn’t just give one answer; it provides a range of likely solutions and their associated probabilities. This is key because it acts as a complement to human expertise, not a replacement. Scholars can use Ithaca’s suggestions as starting points, hypothesis generators, and cross-checks, combining AI’s computational power with their nuanced understanding of historical context and archaeological evidence.

A Glimpse at the Input/Output Concept

While Ithaca isn’t a public API you can query today, conceptually, its interaction might look something like this for text restoration:

# Input: A damaged ancient Greek inscription
# Missing characters are represented by dots or underscores
# The original text might have been: "ΔΙΟΣ ΦΙΛΟΥ ΒΑΣΙΛΕΩΣ ΠΤΟΛΕΜΑΙΟΥ" (Of Zeus Philou, King Ptolemy)

damaged_inscription = "ΔΙΟΣ ΦΙΛΟ... ΒΑΣΙΛΕΩΣ ΠΤΟΛΕΜΑΙΟΥ"

# Ithaca's hypothetical output (simplified for illustration):
# The model provides several top candidates with probabilities.

restoration_candidates = [
    {"text": "ΔΙΟΣ ΦΙΛΟΥ ΒΑΣΙΛΕΩΣ ΠΤΟΛΕΜΑΙΟΥ", "probability": 0.92, "source": "Ithaca prediction"},
    {"text": "ΔΙΟΣ ΦΙΛΟΝ ΒΑΣΙΛΕΩΣ ΠΤΟΛΕΜΑΙΟΥ", "probability": 0.05, "source": "Ithaca prediction"},
    {"text": "ΔΙΟΣ ΦΙΛΟΙ ΒΑΣΙΛΕΩΣ ΠΤΟΛΕΜΑΙΟΥ", "probability": 0.02, "source": "Ithaca prediction"},
]

# For geographical attribution and dating, it might be:

geographical_attribution = [
    {"location": "Athens", "probability": 0.88},
    {"location": "Delphi", "probability": 0.07},
]

dating_range = {
    "start_year": -300, # 300 BCE
    "end_year": -270,   # 270 BCE
    "confidence": 0.95
}

print(f"Damaged Inscription: {damaged_inscription}")
print("\nSuggested Restorations:")
for candidate in restoration_candidates:
    print(f"- '{candidate['text']}' (Probability: {candidate['probability']:.2f})")

print(f"\nProbable Origin: {geographical_attribution[0]['location']} (Confidence: {geographical_attribution[0]['probability']:.2f})")
print(f"Estimated Date: {dating_range['start_year']} to {dating_range['end_year']} BCE (Confidence: {dating_range['confidence']:.2f})")

This conceptual representation highlights how Ithaca presents its findings: as probabilistic hypotheses for experts to consider.

More Than Just Letters: The Broader Impact

Ithaca represents a significant step forward for digital humanities and archaeology. Its implications are profound:

• Accelerated Research: What might take a human scholar months or years to hypothesize and verify, an AI can process in minutes, freeing up experts for deeper analysis.
• Discovery of New Knowledge: By restoring more texts, Ithaca can help uncover new historical facts, literary works, or social structures that were previously inaccessible.
• Reduced Bias: While LLMs can reflect biases in their training data, they don’t bring the same individual cognitive biases a human might when interpreting ambiguous fragments.
• Accessibility: Making ancient texts more accessible and understandable could engage a wider audience in the study of history and classical civilizations.
• Interdisciplinary Collaboration: Ithaca exemplifies the power of collaboration between AI researchers, classical scholars, and historians, paving the way for more such interdisciplinary projects.

The Road Ahead: Limitations and Future Directions

It’s crucial to understand that Ithaca is a powerful tool, not a replacement for human experts. Its accuracy, while impressive (e.g., 62% accuracy in restoring damaged texts, and achieving 71% accuracy when used by human experts collaborating with the AI, compared to 25% for humans alone), is not 100%. The nuanced understanding of historical context, archaeological findings, and the ability to make intuitive leaps based on sparse evidence still firmly rests with human scholars.

Source: Nature Paper on Ithaca (referencing the abstract if full paper is paywalled)

Future directions for such LLMs in ancient language research could include:

• Multilingual Ancient Models: Expanding beyond ancient Greek to other languages like Latin, Sumerian, or even less understood scripts.
• Handwriting Recognition for Papyri: Integrating computer vision with NLP to work with handwritten ancient texts, which are often even more challenging than inscriptions.
• Semantic Understanding: Moving beyond word-level restoration to deeper semantic understanding and contextual interpretation of the restored texts.
• Interactive Tools: Developing more user-friendly interfaces for scholars to interact with these AI models, allowing for easier hypothesis testing and exploration.

Conclusion

The story of Ithaca isn’t just about an LLM decoding ancient languages; it’s a testament to the transformative power of AI when applied thoughtfully and ethically. It demonstrates how cutting-edge technology can bridge the gap between our present and the distant past, illuminating the voices and stories of civilizations long gone.

For developers and tech professionals, Ithaca stands as a compelling example of how our skills in building intelligent systems can extend beyond commercial applications to enrich fundamental human knowledge and preserve our collective heritage. The future of archaeology and linguistics is increasingly intertwined with the algorithms we write and the models we train, promising a new era of discovery.