Criteria for Choosing an AI Programming Language
Today, various general-purpose and specialized programming languages can be used to develop AI algorithms, with Python being the most popular general-purpose Language. AI enthusiasts use Python because of its power to create websites like https://22bet.sn/en, and, at the same time, simple syntax, which makes the language equally accessible to both beginners and experienced users.
R, a specialized programming language used for statistical computing, is also popular for developing AI algorithms. R is well-suited for modern AI, as it allows for easy processing of large amounts of data. In addition, statistics is an important part of developing an AI model, making it the second most common programming language for AI.
Apart from these, programming languages such as C++ and Java are used for programming AI. These languages are used in cases where there is a specific need that can only be solved with their help. Another popular option for AI programming is Scala, an object-oriented programming language.
To decide on an AI programming language, we suggest learning more about each of them.
Python is a leader in AI and machine learning development
Python is the most popular programming language for AI due to its concise syntax and a wide range of specialized libraries: TensorFlow, PyTorch, Scikit-learn, Keras, and OpenCV. It is actively used in the field of deep learning, big data processing, and predictive modeling.
Pros:
Readable syntax and low entry threshold
Large community and quality documentation
Large selection of libraries for statistical and multivariate analysis
Cons:
Slow execution speed due to the interpreted nature
High memory consumption, which can be critical when training large neural networks
Julia is an alternative to Python for high-performance computing
Julia is a dynamically typed language that provides high performance close to C++, but with the convenience of Python. It is ideal for numerical methods, optimization algorithms, and processing multidimensional data arrays.
Pros:
JIT compilation (Just-In-Time) for faster computations
Support for parallel computing on GPUs and distributed systems
Cons:
A limited number of libraries compared to Python
A young community, which complicates finding solutions to non-standard problems
R is a powerful tool for statistical analysis and data visualization
R is a functional programming language specializing in statistical data processing, which makes it popular in bioinformatics, financial analysis, and cognitive computing.
Pros:
A rich set of statistical packages (caret, randomForest, xgboost)
Tools for working with time series and correlation matrices
Cons:
Suboptimal performance when working with large data sets
Limited applicability outside statistics and analytics
Java: scalability and multithreading in AI
Java is widely used in enterprise solutions, AI-driven enterprise solutions, and fintech. The Deeplearning4j framework allows you to implement complex neural network models in Java.
Pros:
Cross-platform and scalable
Support for multi-threaded big data processing
Cons:
Slower execution than C++
High resource requirements when working with neural networks
Many popular libraries are also available in Java, such as Deeplearning4j, an open-source library focused on deep learning applications, and Neuroph, a library for building neural networks in Java. Java also has powerful data processing capabilities, which allow you to fully manage the data pipeline in a corporate environment.
By the way, the average salary of Kata graduates after a year is 221,000 rubles. So if you were planning to start a career in IT, now is the time. Follow the link and find out the details.
C++ — the language for high-performance AI solutions
C++ is widely used in systems that require low-level optimization, such as autonomous robots, game engines, and embedded systems. The TensorFlow and OpenCV frameworks contain a C++ core, making this language indispensable in high-performance computing.
Pros:
High execution speed of machine learning algorithms
Ability to work with hardware resources at a low level
Cons:
Complex syntax and steep learning curve
Long development process compared to Python
JavaScript — AI for web development and browser solutions
JavaScript is becoming a popular tool for implementing machine learning in web applications thanks to the TensorFlow.js and Brain.js libraries.
Pros:
Ability to run neural network models in a browser without server power
Easy integration with front-end applications
Cons:
Limited computing capabilities compared to Python
Lower performance in processing large data sets.
Which programming language to choose for AI?
- Python is the best for general-purpose machine learning and neural networks.
- Julia is the best for high-performance computing and simulations.
- R is ideal for statistics and financial analytics.
- C++ is optimal for robotics and computer vision.
- Java is a robust solution for enterprise systems and AI-driven solutions.
- JavaScript is convenient for integrating AI into web applications.
Final thoughts for techies
AI programming is now more accessible than ever, thanks to the large number of libraries and tutorials on the topic. For popular languages such as Python and Java, tutorials are widely available online, making it even easier for beginners to get started with AI programming.
Creating AI programs also requires knowledge of human cognitive systems, as it will be easier to develop algorithms that work similarly to our brains, allowing for a deeper understanding of the entire procedure. As a rule, Kata graduates easily cope with even the most difficult tasks, including working with AI. The courses provide students with relevant knowledge and practical experience working with real projects.
