Artificial Intelligence (AI) is the buzzword of the decade—reshaping industries, redefining job roles, and unlocking the door to future innovations. But as AI becomes the driving force of the next technological revolution, important questions remain:

• Are we at the risk of forgetting the core subjects - Mathematics, Physics, Chemistry, Biology, Commerce, Management and Humanities—that have guided us to this point?
• Are these disciplines becoming irrelevant in the face of AI’s growing dominance?

The answer is a firm NO! These foundational subjects are not just essential—they’re the very backbone of AI and its real-world applications. In fact, AI’s future depends on the knowledge embedded in these subjects.

Let's break this down and explore why this generation must prioritize basic sciences first, as a strong foundation in these subjects is essential for anyone aspiring to work in AI or any tech-related field.

AI: Built on Traditional Academic Disciplines

AI is not a stand-alone phenomenon. It’s rooted in centuries of knowledge from traditional disciplines.

For example:

• Mathematics is the backbone of AI algorithms. Machine learning, deep learning, and neural networks—all rely on complex mathematical models. Calculus helps AI systems optimize and improve through iterative learning, while statistics and probability are essential for decision-making processes in AI.
• Physics is crucial when dealing with AI applications like robotics and hardware development. Whether we’re talking about autonomous vehicles or drones, principles like Newton’s Laws, energy conservation, and motion are directly applied.
• Chemistry plays a key role in areas like material science and chemical engineering, which are fundamental to the development of AI-powered hardware. AI is used to optimize chemical processes, predict molecular behavior, and design new compounds for drug discovery, making chemistry essential for advancements in fields like healthcare, energy storage, and manufacturing.
• Biology contributes to AI through fields like bioinformatics and medical AI. AI mimics biological systems, such as the human brain, for its algorithms. Neural networks themselves are inspired by how neurons communicate within the brain. Similarly, AI is used in healthcare for diagnostic tools and drug discovery, where knowledge of biology is indispensable.
• Commerce is integral to AI’s application in financial systems, market predictions, e-commerce, and retail analytics. AI-driven tools enhance decision-making in economics, supply chain management, and financial modeling, helping businesses optimize operations and increase profitability. The integration of AI with commerce also drives innovation in customer experience and business strategies.
• Management disciplines benefit from AI in areas such as data-driven decision-making, project management, and resource allocation. AI helps managers predict market trends, assess risks, and optimize workflows, providing a competitive advantage in business operations. Furthermore, AI assists in automating routine tasks, enabling managers to focus on strategic planning and innovation.
• Humanities like English play a vital role in shaping ethical and communicative AI systems.

Together, these disciplines enable AI to revolutionize fields such as healthcare, environmental science, and robotics, ensuring it is scientifically grounded, ethically applied, and innovatively transformative.

The Risk of Ignoring the Basics

AI provides tools but not the reasoning behind them. While AI is exciting and promises revolutionary outcomes, ignoring the core subjects in favor of just learning AI tools could result in major gaps in understanding.

Basic sciences teach analytical skills that allow professionals to understand the "why" and "how" behind AI's outputs.

Without these foundational subjects, students and professionals may:

• Lack the Critical Thinking Skills to Innovate: Innovation doesn’t just come from using existing technology but out of box thinking which AI cannot. It comes from the ability to question, analyze, and improve on it. A solid understanding of math and science helps in questioning how AI can be improved or applied to new areas.
• Poor Real-World Application: AI’s potential is vast, but it needs context from basic sciences.For example, AI may suggest models for climate, but the science behind it helps to understand .

Interesting Statistics:

"According to a 2023 report by the World Economic Forum, 85% of jobs that will exist in 2030 have not yet been invented, but the foundational skills in mathematics, science, and critical thinking will be essential for future innovators in AI and other emerging technologies."

AI and Basic Sciences: Building the Future Together

A future in AI isn’t about choosing between AI and basic sciences—it’s about combining both. The best AI developers, scientists, and professionals are those who not only know how to use AI but understand the principles behind it.

If subjects like mathematics, physics, chemistry, biology, commerce contribute to understand the outcome of AI, management and humanities are the keys to connect the outcome to society.

In our race to adopt AI, let’s not forget the basics—they are the true building blocks of progress of the society!

Dr. M. Raja Rajeswari, Assistant Professor,December-2024