I'm a programmer diving into the world of AI, intrigued by how models like ChatGPT and Gemini are built. I've noticed that a lot of AI programming happens in Python, which is often seen as slower than compiled languages like C, Rust, or Go. I'm curious why we stick with Python for most AI projects rather than opting for a faster language that could boost performance. Can someone clarify why readability seems to take precedence over speed in this context?
2 Answers
A lot of the performance-critical code in AI is actually written in languages like C or C++. Python tends to wrap these optimized libraries, which handle the heavy lifting while keeping the coding process simpler and more readable. So, Python isn't really a bottleneck; it just orchestrates the more complex operations happening underneath.
Exactly! It keeps the coding process accessible while leveraging the power of faster languages.
You're right to think about performance, but in practice, the time spent in Python is minimal compared to the optimized calls to libraries. Most heavy computations are running in the background, often on GPUs, which are designed for speed.
So, Python is like the conductor of an orchestra, making sure everything plays together smoothly?