Artificial intelligence (AI) is not science fiction from the future, but already an active part of the modern digital economy. It is used in medicine, education, finance, manufacturing, customer service, and even art.
However, behind every AI solution lies the painstaking work of developers and the use of powerful programming languages, libraries, and frameworks. To ensure that AI is not just “smart” but truly useful and safe, it is important to choose the right tools for its creation. Yevhen Kasyanenko, an expert in high-tech development and team lead at https://kis.software/what-we-do/artificial-intelligence/ , shares his recommendations on languages and technologies to get started in AI.
AI programming: where does a developer’s journey begin?
AI is not a single algorithm, but a whole set of methods: machine learning, natural language processing (NLP), computer vision, neural networks, and much more.
To create effective solutions, developers need to understand not only the theory, but also how to apply it in practice using programming languages and specialized tools.
The choice of technology depends on the project’s objectives: whether rapid prototyping, high performance, working with large data sets, or precise mathematics are required. Below, we will look at popular languages and their features.In real-world applications, technologies like an AI Voice Agent demonstrate how these methods come together to enable natural and responsive voice interactions in customer service and automation.
Python — the world leader in AI
Python is now considered the primary programming language for artificial intelligence — and for good reason.
Advantages:
- Simple syntax, which speeds up development;
- Large community and a huge number of ready-made libraries: TensorFlow, PyTorch, scikit-learn, Keras, NLTK;
- Versatility — suitable for analytics, visualization, automation, and model training.
Disadvantages:
- Low execution speed compared to compiled languages (e.g., C++);
- Not always suitable for projects where real-time performance is critical.
Recommendation from Yevhen Kasyanenko:
“Python is the ideal choice for getting started and quickly developing an MVP in AI. It allows you to focus on logic rather than syntax. It is especially useful in projects where analytics and working with models are important.”
Java — stability and scalability
Although Java is less popular than Python in AI, it remains a popular choice for large corporate projects.
Advantages
- High reliability and cross-platform compatibility
- Scales well in large projects
- Supports multithreading and high performance
Disadvantages
- More cumbersome and complex syntax compared to Python
- Limited number of specialized AI libraries (compared to the Python ecosystem)
Java is used in financial technology, banking systems, and web services, where AI is only part of a complex infrastructure.
C++ — maximum performance
If the task requires working with graphics, robotics, or embedded systems, C++ is indispensable.
Advantages
- Highest performance and memory control
- Ideal for creating real-time systems and integrating with hardware.
Disadvantages
- Difficult to learn and developFewer ready-made solutions for AI (compared to Python).
C++ is often used in the gaming industry, autopilots, industrial automation, and resource-constrained systems.
What tools and libraries should you use?
In addition to languages, it is important to choose the right libraries and frameworks. Here is a list of the most popular ones:
- TensorFlow — a powerful tool from Google for deep learning;
- PyTorch — a flexible framework that is convenient for experimentation and academic tasks;
- Keras — a simplified shell for TensorFlow;
- scikit-learn — for classic machine learning;
- OpenCV — for working with images and video;
- NLTK and SpaCy — for natural language processing (NLP).
The team at https://kis.software/ uses a combination of these tools, depending on the specifics of the project, speed requirements, accuracy, and ease of integration with other services.
Conclusion: what to choose?
The choice of language and tools depends on the tasks, budget, and team. For getting started and most application solutions, Python is the best choice. In cases where high performance is important, C++ is worth considering. And for complex and scalable solutions in a corporate environment, Java is the way to go.
Yevhen Kasyanenko, head of Kiss.software, emphasizes:
“There is no universal language for AI. The main thing is to choose a technology that suits the specific goals of the project and entrust its implementation to a professional team.”
