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2025-02-28
As the global race to lead in artificial intelligence (AI) intensifies, India faces both opportunities and challenges in ensuring its place as a dominant player in the field. A crucial yet often overlooked factor in this endeavor is the role of mathematics. Last week, we discussed the importance of math in AI, and the positive feedback we received has encouraged us to delve deeper into this topic. In this article, we further explore why India’s AI ambitions can only be realized by strengthening its capabilities in mathematics and statistics.
Mathematics: The Backbone of AI
AI, at its core, is powered by mathematical algorithms and statistical models that drive its functionalities. These algorithms, integral to machine learning (ML) and deep learning (DL), process vast amounts of data to extract meaningful insights and make predictions. As India moves forward with its AI aspirations, experts argue that enhancing the country’s proficiency in mathematics and statistics is the only way to create cutting-edge innovations.
Shyam S Kamath, a professor of mathematics at the National Institute of Technology Karnataka (NITK), emphasizes the mathematical nature of AI algorithms, asserting that they are essentially just mathematical equations built on logic. According to Kamath, handling large-scale data can only be accomplished through advanced mathematical techniques, making math an indispensable tool for the AI ecosystem.
Data scientists, who are at the heart of AI advancements, play a key role in transforming raw data into actionable insights. These professionals combine statistical knowledge with programming skills to power AI applications like image processing, text categorization, and predictive analytics. Rudramuni B, former head of Dell’s R&D center in Bengaluru, stresses that successful data scientists must be better statisticians than programmers and better engineers than statisticians. He notes that without a solid understanding of mathematics, they would struggle to effectively use software to solve complex problems.
AI models are not static; they are constantly evolving based on new data or environmental changes. When the accuracy of a model deteriorates, it’s not as simple as adjusting a feature via programming. According to Rudramuni, resolving such issues requires a deep understanding of the underlying mathematical equations, as adjusting the algorithm cannot be done through programming alone.
What Undercode Say:
The growing recognition of mathematics’ centrality in AI is crucial for India’s future technological advancements. As Rudramuni points out, the current approach in many engineering colleges—where AI, ML, and data science programs are housed within computer science (CS) or information technology (IT) departments—is problematic. This structure often undermines the significance of mathematics in AI development.
In contrast, NITK Surathkal has taken a different approach by offering its BTech program in computational and data science under the mathematics department. This initiative highlights the importance of integrating mathematics with AI education from the ground up. Shyam S Kamath, who spearheaded the initiative, reflects that the idea received strong support from esteemed institutions like IISc and the education ministry, and today the program enjoys great demand among students.
The core message here is clear: for India to be at the forefront of AI research and development, it must prioritize and elevate mathematical education in its tech curriculum. The rise of AI depends on a deep understanding of complex mathematical models and statistical analysis, which are often sidelined in the rush to produce software programmers.
India’s current challenge lies in rethinking its approach to AI education. A shift is needed from simply training programmers to fostering a new generation of data scientists who understand the profound connection between mathematics and AI. Without this understanding, the country risks falling behind in the global AI race, as rudimentary programming alone cannot unlock the full potential of AI.
Fact Checker Results:
- Mathematics is indeed a critical factor in developing and understanding AI models, and its role is often understated in conventional AI education programs.
- The shift toward placing AI education under mathematics departments, like at NITK, is a progressive step that reflects the growing awareness of math’s importance in AI.
- Despite the growing importance of mathematics, many Indian engineering colleges still focus on programming in AI courses, which may limit the country’s AI growth potential.
References:
Reported By: https://timesofindia.indiatimes.com/technology/times-techies/why-applied-math-must-be-core-to-data-science-courses/articleshow/118614890.cms
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