Integrating Vedic techniques with AI: Can traditional wisdom educate modern technology?

Kalyanee Devi

(Assistant Professor, School of Natural and Applied Science, Pragjyotishpur University, Guwahati, Assam)

For thousands of years, India has been a hub of innovation and knowledge, and it has made significant contributions to the fields of astronomy, medicine, metallurgy, linguistics, and philosophy. Aryabhata, Brahmagupta, Charaka, Sushruta, and Panini were among the ancient Indian scholars who established the groundwork for numerous contemporary scientific and technological fields. India’s philosophical roots have impacted the world’s scientific community in a number of ways, from the decimal number system and the concept of zero to developments in logical thinking. Vedic mathematics, a set of techniques or sutras that are derived from the ancient Indian scriptures known as the Vedas, provides a distinctive and efficient method of conducting mathematical calculations. Although these methods have been acknowledged for their capacity to streamline and accelerate fundamental arithmetic, their ability, especially in advanced fields like artificial intelligence, algorithm development, network security, and cryptography, remains unrealised. Artificial intelligence, data analytics and computer models have all greatly increased India’s position as a global leader in technology. But it’s crucial to make sure that this advancement aligns with inclusive, sustainable, and ethical principles that are ingrained in India’s traditional knowledge systems. It becomes evident as we investigate the potential of Vedic mathematics in contemporary computational settings that this age-old knowledge may stimulate creative answers to some of the most challenging issues facing today’s technological revolution.

The early 20th-century work of Jagadguru Swami Bharati Krishna Tirthaji Maharaj was largely responsible for introducing Vedic mathematics to the modern world. He compiled 13 sub-sutras (corollaries) and 16 sutras (aphorisms) that cover a broad spectrum of mathematical concepts. The purpose of these sutras is to streamline computations, making them quicker and easier to understand. There are many popular Vedic methods which are even used by students to compute many complex mathematical problems in a simpler way in a shorter amount of time. For example, Ekadhikena Purvena (By One More than the Previous One) is a method for finding squares of numbers ending in 5. Navatashcaramam Dashatah (All from 9 and the Last from 10) is a subtraction technique applicable to large numbers. Vertically and crosswise is a general multiplication formula that enables quick mental computation of products. These methods facilitate rapid mental calculations and diminish the cognitive stress associated with difficult arithmetic, rendering them suitable for educational contexts and general problem-solving.

Artificial Intelligence (AI) depends significantly on rapid and precise computations, especially in domains such as machine learning, neural networks, and data processing. Vedic mathematics, due to its ability for swift mental computations, may inspire innovative techniques for more efficient processing of extensive data sets. This could result in more effective massive data management in different applications. Artificial Intelligence (AI) depends significantly on rapid and precise computations, especially in domains such as machine learning, neural networks, and data processing. For example, the rapid and precise multiplication techniques of Vedic mathematics (Urdhva Tiryagbhyam Sutra) may enhance the training process in neural networks. In speech recognition and computer vision using AI, Vedic multipliers can accelerate DSP operations like FFT and DCT.

The foundation of computer science is made up of algorithms, and optimising the speed and increasing the accuracy of an algorithm is a challenging task. With its focus on speed and simplicity, Vedic mathematical ideas have important implications for algorithm development. Vedic sutras can motivate the formation of an efficient algorithm with lower computational complexity. For example, algorithms for quick multiplication, especially when working with big numbers, can be created by adapting the “Vertically and Crosswise” technique. These algorithms may perform better than the conventional techniques now employed in computer systems.

With its roots in ancient Indian mathematical texts like the Vedas and Sulba Sutras, Vedic mathematics provides a distinct set of methods and algorithms for geometry, calculus, algebra, and arithmetic. Also, neural network training, image processing, and natural language processing are just a few examples of the matrix operations that are important to AI models. The computational cost of traditional matrix multiplication techniques is high. By eliminating intermediate steps, Vedic multiplication helps speed up these computations. Also, AI processors and GPUs can operate faster and use less power when Vedic techniques are implemented. Millions of calculations are made every second for activation functions and weight adjustments in neural networks. For example, real-time edge detection, pattern recognition, and sensor data processing are necessary for robotics and computer vision. The Vedic technique using Anurupyena optimises the feature extraction in image recognition models. Also, complex computations are made simpler by the digit manipulation techniques used in Vedic mathematics. By manipulating digits according to specific rules, sutras such as “Ekadhikena Purvena” (By one more than the preceding one) and “Nikhilam Navatashcaramam Dashatah” (All from 9 and the last from 10) allow for rapid mental additions and subtractions.

Future research avenues and applications where Vedic mathematics can advance AI development are highlighted in the many areas. Binary arithmetic, which requires several clock cycles for multiplication and division, is carried out by conventional computers. Vedic multiplication and division algorithms can be integrated into AI accelerator processors (like NVIDIA’s CUDA cores and Google’s TPU) to increase processing speed.  Again, in the healthcare sector, AI-driven CT scans, MRIs, and X-ray analysis processes require high computational overheads. In medical imaging, Vedic mathematics can maximise feature extraction, edge detection, and picture reconstruction. Another application can be the use of Vedic techniques for AI-enabled speech and voice recognition.  By optimising speech pattern detection, Vedic phonetics concepts can improve the accuracy of AI-based voice assistants. Speech-to-text AI models can benefit from Sanskrit phonetics (Shiksha Vedanga), which is renowned for its systematic pronunciation system.

Vedic mathematics contributes significantly to the advancement of AI computations with its quick and effective calculation methods. Researchers can create AI models that are quicker, more effective, and use less energy by incorporating Vedic Sutras into AI algorithms. Future studies can investigate Vedic arithmetic-based hardware acceleration, opening the door for next-generation AI innovations.