The second step is to select a machine learning or deep learning algorithm that can be applied to the task at hand. Each algorithm requires a specific code structure. For example, neural networks are most often implemented using frameworks such as TensorFlow or PyTorch, which use Python.
Once the algorithm is defined, the system or developers are faced with the task of optimizing the code to achieve maximum efficiency. The process can be automated using existing programming automation tools that analyze various code metrics and suggest ways to improve it.
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AI can analyze code, but choosing the “best” code requires creativity, intuition, and contextual understanding that AI does not yet have.
AI can also, through evolutionary and self-learning software processes, independently select the necessary types of code, testing different options and evaluating their effectiveness. Such systems improve through estonia phone number data iterations, where at each stage the program becomes better at a given task. For example, the Genetic Programming system can evolve code through mutations and crossovers until a solution to the problem is achieved.
Interestingly, the code selection process can use mixed approaches: static code analyzers, adaptive algorithms, and deep learning. The result is code that not only solves the problem efficiently, but can adapt to changes in the data or environment.
The final stage is testing the resulting solution. Robots or AI systems can use testing to determine the most appropriate way to implement the task. Automated tests check the correctness of the program and its ability to adequately respond to non-standard situations.
It is important to understand that automated code type selection is in its early stages of development , and in most projects the final decision is still made by a human developer. However, with the growing complexity of systems and tasks, and the improvement of AI algorithms, this process is becoming increasingly relevant and attractive for research.