Recent studies indicate a vast number of languages have vanished throughout history, with many remaining undeciphered, shrouding centuries of human knowledge. These lost languages represent more than just academic puzzles; they are keys to understanding the cultures and histories of the people who spoke them. The challenge lies in the scarcity of records and the lack of clear connections to existing languages, making traditional translation methods ineffective. Imagine trying to learn a language with no dictionary and grammar book, written without spaces or punctuation – this is the reality of deciphering lost languages.
However, groundbreaking research from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) offers a new beacon of hope. They’ve developed an innovative AI system capable of automatically deciphering lost languages, even without prior knowledge of their linguistic relatives. This system goes further, identifying potential relationships between languages, and has already corroborated scholarly theories, such as the disconnection between Iberian and Basque languages. The ultimate ambition is to empower this system to decode languages that have long defied linguists, using only limited textual data.
Alt text: Close-up of Rongorongo script, an undeciphered writing system from Easter Island, showcasing the complexity of lost language decipherment.
Linguistics-Driven AI: A New Approach to Language Learning
Spearheaded by MIT Professor Regina Barzilay, this system ingeniously incorporates principles from historical linguistics, a field crucial for learning languages by understanding their evolution and structures. A core concept is that language evolution follows predictable patterns. While languages rarely invent entirely new sounds, sound transformations are common and follow certain linguistic rules. For example, a ‘p’ sound might evolve into a ‘b’ in a related language, but a shift to a ‘k’ is less probable due to significant phonetic differences. By embedding these linguistic constraints, Barzilay and MIT PhD student Jiaming Luo created a sophisticated decipherment algorithm. This algorithm navigates the immense possibilities of language change and the limited clues available in ancient texts.
The algorithm’s strength lies in its ability to map language sounds into a multidimensional space. In this space, phonetic similarities are represented by proximity, allowing the system to discern patterns of language change and convert them into computational rules. This innovative model can segment words within an ancient language and link them to corresponding words in a related language, much like learning vocabulary in a new language by understanding its roots.
Building on Past Linguistic Successes
This project expands upon previous work by Barzilay and Luo, which successfully deciphered Ugaritic and Linear B, languages that had previously taken linguists decades to decode. Crucially, those earlier projects benefited from the knowledge that Ugaritic and Linear B were related to Hebrew and Greek, respectively. The significant advancement of this new system is its capacity to infer language relationships autonomously. Determining these relationships is often the biggest hurdle in decipherment. The Linear B decipherment, for instance, required decades to identify its correct linguistic descendant. In the case of Iberian, the linguistic community remains divided, with some proposing a connection to Basque and others arguing for its isolation.
Alt text: Linear B tablet showcasing the ancient script, now deciphered, highlighting the progress in understanding lost writing systems through linguistics.
The newly developed algorithm can assess the linguistic distance between languages. Testing on known languages revealed its accuracy in identifying language families. When applied to Iberian, considering Basque and less probable candidates from Romance, Germanic, Turkic, and Uralic families, the algorithm indicated that while Basque and Latin were closer to Iberian than other languages, the linguistic gap was still too significant to confirm a relationship. This capability to analyze language relationships offers valuable insights into language learning itself, showing how languages diverge and evolve over time.
The Future of Linguistic Decipherment and Language Learning
Looking ahead, the team aims to go beyond “cognate-based decipherment,” which relies on finding related words in known languages. The Iberian example underscores that not all lost languages have known relatives. The future direction involves identifying semantic meaning within undeciphered texts, even without reading individual words.
“For example, we might identify references to people or places in the text, which could then be investigated using existing historical evidence,” Barzilay explains. “These ‘entity recognition’ methods are widely used in modern text processing and are highly accurate. The key question is whether this task is achievable without any training data in the ancient language.” This innovative approach promises to revolutionize our understanding of lost languages and, by extension, deepen our understanding of the fundamental principles of language itself. By unraveling the complexities of lost languages through linguistics and AI, we are not just deciphering ancient texts, but also gaining valuable insights into the very nature of language learning and evolution.
This research, partly supported by the Intelligence Advanced Research Projects Activity (IARPA), marks a significant stride in our ability to learn from the linguistic past and unlock the secrets held within undeciphered languages.
