Rosetta Stone: Architecture as Language

While working on the Vesuvius Challenge to read the Herculaneum scrolls, I kept encountering early writing systems—not just as symbols, but as systems. The Rosetta Stone is the most famous example, yet I realized I had understood it mostly as a metaphor (“a Rosetta Stone for X”) rather than as an engineered artifact.

The stone contains a decree issued in 196 BC affirming the cult of Ptolemy V. The content itself is administrative and unremarkable. What matters is how it is encoded.

The same decree appears three times, representing three distinct data structures:

1. Hieroglyphs — Ceremonial, invariant, optimized for permanence and ritual context.

2. Demotic Egyptian — Compressed, operational, optimized for everyday administration and speed.

3. Ancient Greek — Explicit, linear, optimized for precision and governmental interoperability.

This is usually framed as "translation." But structurally, it is closer to representation learning.

Each script encodes the same semantic content at a different level of abstraction, optimized for a different inference task and audience. Meaning is conserved, but the affordances change.

Visually, the stone reads like a branching system: sparse symbolic forms at the top, dense explicit forms at the bottom. This is not unlike a modern model stack, where the same signal is re-expressed across layers—compressed, expanded, or reweighted depending on the goal.

The Multi-Architectural Analogy

This is where the comparison to modern AI becomes concrete rather than poetic. In contemporary machine learning, we see a similar "trilingual" split in how we process information:

• Convolutional Networks (CNNs): Compress spatial information into invariant, symbolic features (similar to the structural permanence of Hieroglyphs).

• Recurrent Models (RNNs): Preserve sequence and local context over time (similar to the running flow of Demotic).

• Transformers: Expose global structure explicitly via attention mechanisms, allowing for precise alignment (similar to the explicit clarity of Greek).

All three architectures can operate on the same input. What differs is not the data, but how it is represented and navigated.

Seen this way, the Rosetta Stone is not just multilingual. It is multi-architectural.

It demonstrates something humans figured out early and AI is now rediscovering: the hard part is not generating content, but choosing the right representation for the reader, the task, and the medium.

This is why architecture and language keep converging—whether in temples, administrative scrolls, or neural networks. Each is an attempt to stabilize meaning while allowing it to travel across contexts.

The Rosetta Stone worked because it did not privilege a single encoding. It accepted redundancy as the price of understanding.

That remains true.

I thought it'd be fun to recreate this but representing the common neural networks CNN, RNN, and transformers.

Download the original rosetta stone model here

Sources

History / Writing Systems

• British Museum: Explore the Rosetta Stone

• Parkinson, R. (1999): Cracking Codes: The Rosetta Stone and Decipherment

• Allen, J. P. (2013): Middle Egyptian: An Introduction to the Language and Culture of Hieroglyphs

  
  

Representation & AI

• Bengio, Y., Courville, A., Vincent, P. (2013): Representation Learning: A Review and New Perspectives

• LeCun, Y., Bengio, Y., Hinton, G. (2015): Deep Learning

• Vaswani et al. (2017): Attention Is All You Need

  
  

AI & Art / Systems Thinking

• Manovich, L. (2018): AI Aesthetics

• Mitchell, M. (2019): Artificial Intelligence: A Guide for Thinking Humans

• McLuhan, M. (1964): Understanding Media

Notes

• Date Verified: 196 BC is the correct year for the Memphis Decree (the text on the stone).

• Vesuvius Challenge: I linked explicitly to the Vesuvius Challenge (Herculaneum scrolls) in the intro to ground your "work on the scrolls" comment.

• Script Functions: The characterization of Demotic as "everyday/administrative" vs Hieroglyphs as "priestly/ritual" is historically accurate and strengthens your "compression vs. symbol" argument.

• Links: The links provided are the canonical sources (ArXiv for the papers, British Museum for the artifact).

How ancient Herculaneum papyrus scrolls were deciphered

This video is relevant because it details the Vesuvius Challenge mentioned in your intro, showing how AI "representation learning" was literally used to uncover the ink in the carbonized Herculaneum scrolls.