Yesterday I was with my friend taking a Python class. Suddenly, he asked me something that made me wonder. He asked, "If Python takes more time in computation, then why do we use it for AI and machine learning? Aren't those fields supposed to be as fast as possible?"
At first, I thought the question was simple, but the more I thought about it, the more valid it seemed. Why are we using Python?
So I started searching and discovered something interesting. In many AI and ML applications, Python acts as a wrapper. Major libraries like NumPy perform their heavy computations in C and C++, while we simply call their functions through Python. This gives us the speed of lower-level languages with the simplicity of Python.
But if that is the case, then why don't we write everything directly in C++?
The answer is probably Python's greatest advantage: its syntax. Python is much easier and faster to learn than C or C++. Researchers and developers can spend more time building AI models and less time dealing with complicated code. In many cases, reducing development time is more valuable than reducing execution time by a small amount.
The reality is that Python did not become the language of AI because it is the fastest. It became the language of AI because it combines simplicity with the power of highly optimized libraries.