Sanskrit & Artificial Intelligence: Rediscovering the Future Through the Language of the Ancients

Hello, I am SOORAJ KRISHNA SHASTRI.

We often talk about AI and large language models, but today, let's talk about the Sanskrit language. Delhi Chief Minister Rekha Gupta has made a strong appeal for the revival of Sanskrit. She described Sanskrit as the most scientific and computer-friendly language. The Chief Minister claimed that NASA scientists have also acknowledged the potential of Sanskrit in coding and artificial intelligence. She stated that Sanskrit is not just a language of the past, but of the future.

According to her, NASA scientists have written research papers that highlight Sanskrit’s scientific structure and coding capabilities.

That was the statement by the Delhi CM—but do you know the full story behind this claim connecting Sanskrit with NASA? Let’s understand this in detail.

The claim made by CM Rekha Gupta is connected to a famous research paper published in 1985. The paper was titled "Knowledge Representation in Sanskrit and Artificial Intelligence" and was written by Rick Briggs, who was affiliated with NASA at that time.

In this paper, Briggs highlighted the achievements of ancient Sanskrit grammarians. He explained how they had developed a method to express Sanskrit in a way that not only preserved its essence but also mirrored the structure of modern AI systems. This 1985 paper, published in the AI Magazine, shed light on how a natural language like Sanskrit could function similarly to artificial languages.

Briggs argued that Sanskrit’s highly structured and unambiguous grammar makes it ideal for computers. He compared Sanskrit’s grammatical framework with modern AI knowledge representation techniques, such as semantic networks.

Let’s now understand the scientific structure of Sanskrit and its connection to semantic networks.

Briggs mentioned that Sanskrit grammar, especially the rules developed by Panini in the 4th century BCE, are so precise and logical that they are perfect for computer algorithms. For example, Panini’s rules compiled in the Ashtadhyayi define valid forms of words and sentences. These rules allow transformations based on properties like gender, number, and tense. This is similar to modern context-free grammars and Backus-Naur Form used in programming languages today.

Now let’s look at its modern relevance.

Today’s AI systems, like large language models, struggle with reducing ambiguity in natural language processing. Sanskrit’s rule-based and context-free grammar can minimize ambiguity, making NLP models more accurate and efficient. For instance, in Sanskrit, even if the word order changes, the meaning remains unchanged. This simplifies sentence interpretation for AI.

Now let’s understand the phonetic and logical advantages of Sanskrit.

Sanskrit is a phonetic language—what is written is pronounced exactly the same. This feature is beneficial for computers in speech recognition and text processing. Additionally, Sanskrit grammar includes word-combination rules and letter-based scoring systems, which help in technically breaking down and categorizing words. This approach can be useful in AI for word-sense disambiguation and semantic analysis.

Talking about today’s relevance: modern AI faces challenges such as sarcasm detection, tonal adjustments, and contextual understanding. Sanskrit’s precise structure could help address these issues. For example, a word like Indrashatru in Sanskrit can mean either "killer of Indra" or "one who is killed by Indra" based on context, but its strict rules help resolve such ambiguities.

Now let’s look at Sanskrit and machine translation.

Briggs also noted that Sanskrit’s descriptive nature and mathematical backbone make it suitable for machine translation. Its ability to break down words and sentences—like Panini’s root-based approach—helps computers represent meanings independent of word forms. This is relevant for modern machine translation systems that struggle to preserve context and meaning.

Currently, neural machine translation systems like Google Translate still struggle with low-resource languages. Sanskrit’s formalized structure and Panini’s rules could improve NMT systems for Indian languages, since Hindi, Marathi, Bengali, and others have evolved from Sanskrit.

Next up—Sanskrit and Ethical AI.

Briggs’ paper didn’t suggest using Sanskrit directly as a programming language, but its grammatical principles can enhance AI knowledge representation. Today, with increasing emphasis on explainability and ethics in AI, Sanskrit’s philosophical traditions like Nyaya and Mimamsa can inspire interpretable and ethical AI frameworks.

To understand its modern relevance: the philosophical knowledge preserved in Sanskrit texts—concepts of logic and reasoning—can help develop explainable models in AI. For instance, knowledge schemas inspired by Panini’s rules can help machines learn relationships between concepts, making AI decisions more transparent.

Now let’s talk about challenges and limitations.

While Briggs' paper highlights Sanskrit’s potential, there are practical challenges in implementation. There is a lack of linguistic communities and resources to widely adopt Sanskrit. Additionally, due to English’s global dominance, integrating Sanskrit-based systems can be difficult.

Briggs suggested a two-layer system—where input is in any natural language but processing is Sanskrit-based. This idea remains compelling for research even today.

In modern times, there is scope to revive Sanskrit-based NLP research within India’s startup ecosystem and academia. For example, Sanskrit-inspired algorithms can help develop customized LLMs for Indic languages, giving India a competitive edge in the global AI landscape.

How did you like our presentation? Do let us know.

भागवत दर्शन