The AI revolution is here—and it’s evolving fast. While coding has long been considered the core skill in tech, a new narrative is emerging. According to tech visionaries like Elon Musk and Jensen Huang (President of NVIDIA), the future of AI lies not just in computer science, but deeply rooted in physics and mathematics.
So, what does this mean?
Let’s break it down simply:
If you want to develop a piece of software, coding knowledge might be enough. But if your goal is to build a robot or an intelligent system that interacts with the real world, then physics and math become essential tools.
Take ChatGPT as an example. When you input a question, the system processes it using tensors (multi-dimensional matrices), sometimes in 2D, 3D, or even higher dimensions. These tensors contain thousands of rows and columns of data. ChatGPT then computes the best possible vector representation of your input to generate a meaningful response.
This entire process involves:
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Linear algebra
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Probability theory
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Optimization algorithms
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And even wave equations in some sensory processing cases.
AI today doesn’t just read and write text. It understands images, sound, motion, and even touch. These require deep understanding of:
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Sound waves and frequency analysis
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Vector fields
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Numerical methods
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Signal processing
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Neural dynamics, and more.
In a recent statement, Jensen Huang said that if he were a student today, he would choose to study physics instead of computer science—a bold endorsement of how the foundations of natural sciences are becoming central to future technologies.
Final Thoughts:
As AI continues to blur the line between the digital and physical world, those with a strong grasp of mathematical modeling and physical systems will be leading the charge. The message from industry leaders is clear: To build the next generation of intelligent systems, code alone is not enough.