Is AI Programming/Easy to Code Compared to Conventional Programming?

The ease of programming in AI/machine learning compared to conventional programming/software engineering depends on several factors including the complexity of the task, the developers' background, and the tools available. Here are some points to consider:

1. Conceptual Complexity

AI/ML: Understanding concepts like neural networks, training, overfitting, and feature engineering can be challenging. It often requires knowledge of statistics, linear algebra, and domain-specific expertise.

Conventional Programming: While it also has its complexities, traditional programming often focuses on logical problem-solving and algorithm design, which may be more straightforward for many developers.

2. Tooling and Libraries

AI/ML: There are many high-level libraries such as TensorFlow, PyTorch, and scikit-learn that simplify the implementation of machine learning algorithms. These tools can make it easier to start with AI programming even if the underlying concepts are complex.

Conventional Programming: Traditional programming also benefits from a rich ecosystem of libraries and frameworks, but the learning curve can vary widely depending on the language and the application domain.

3. Data Handling

AI/ML: Working with data is a core component of machine learning. Data preprocessing, cleaning, and transformation can be time-consuming and require a solid understanding of data handling techniques.

Conventional Programming: Data handling is also important but may not be as central as it is in AI/ML. Many applications can function with less emphasis on large datasets.

4. Problem-Solving Approach

AI/ML: The approach often involves experimentation, tuning hyperparameters, and iterative testing, which can be less deterministic than traditional coding.

Conventional Programming: Typically involves more structured problem-solving with defined inputs and outputs, making it easier to predict outcomes based on code changes.

5. Learning Curve

AI/ML: For those without a strong mathematical background, there can be a steeper learning curve. However, many resources exist to help bridge this gap.

Conventional Programming: Generally, it might be easier for beginners to grasp the fundamentals of programming without needing extensive mathematical knowledge.

Conclusion

In summary, whether AI programming is easier or harder than conventional programming varies by individual and context. For those with a strong background in mathematics and statistics, AI/ML might be more accessible, especially with the help of robust libraries. However, for many traditional programming tasks, the structured nature of conventional programming may feel more straightforward.