Artificial intelligence (AI) is an important element of several software development projects letting developers create software that can perceive, learn, reason, and solve problems in a human-like way. Selecting the right AI programming language helps get a deeper idea about this fast-evolving field. However, many businesses struggle with where to begin. When it comes to the most popular AI programming languages, there are several common AI programming languages that are widely used in the field of artificial intelligence. Each language has its own strengths and areas of application.
To help you get started, we have mentioned some of the most common AI programming languages here:
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Python
Python is one of the most commonly used programming languages for AI and machine learning. It offers a vast ecosystem of libraries and frameworks like TensorFlow, PyTorch, and sci-kit-learn, providing powerful tools for building and training AI models. Python’s simplicity, readability, and extensive community support make it an excellent choice for beginners and experienced developers alike.
The developers use Python for its simple structure and object-oriented approach when it’s to maintaining the codes. Despite being friendly, Python is well-equipped to manage large and complex projects with ease.
Machine learning is a subdivision of AI involving algorithms to train machines. Another important thing about Python is that it is one of the easiest languages to learn, in spite of what it’s capable of!
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R
R is a language purposefully designed for statistical computing and data analysis. This language has a rich collection of packages and libraries making it popular among statisticians and data scientists. R provides comprehensive tools for data manipulation, visualization, and machine learning. It is particularly well-suited for statistical modeling, data exploration, and visualization tasks.
R can be your shining light when all things look dark. In terms of getting your numbers straight, R is actually better than Python. Some of the R packages to look into include Gmodels, Tm, and OneR.
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Java
Java is a general-purpose common AI programming language used in various domains, including AI. Java offers a robust and scalable platform to build enterprise-level AI applications. It has a large community and numerous libraries, such as Deeplearning4j and Weka that provide AI-related functionalities. It is a popular choice for AI applications that require high-performance computing and integration with existing Java-based systems.
Java is a native language for Android app development and mobile app developers know that artificial intelligence is a profitable application development trend and thus use Java.
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C++
C++ is a powerful and efficient programming language that is commonly used in AI programming, especially for computationally intensive tasks. It offers low-level control and high performance, making it suitable for building AI systems that require real-time processing and low latency. C++ is often used in the development of AI frameworks and libraries, such as OpenCV and Caffe.
C++’s capacity for low-level programming makes it an ideal programming language to handle AI models in production. The developers can build neural networks from the start using C++ and translate user code into an easier language.
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Julia
Julia is a relatively new programming language that has gained popularity in the AI community. It combines the ease of use of high-level languages like Python with the performance of low-level languages like C++. Julia is designed for numerical and scientific computing, and it offers built-in support for distributed computing and parallelism. Julia is particularly well-suited for tasks requiring heavy numerical computation and optimization.
Julia’s wide range of standard features consists of direct support for C functions, a lively type system, and much more.
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Prolog
Prolog, a portmanteau of logic programming, is one of the oldest AI programming languages. Despite how old Prolog is, it works well for the right projects. In Prolog, rather than using coded sequences, one can simply chart out a basic set of facts, rules, goals, and queries. Due to all these features, Prolog is apt at pattern matching, especially when NLP is involved.
Being a declarative programming language, Prolog allows developers to specify the rules and facts about a problem domain, and then the same will be used to automatically infer solutions to problems.
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Haskell
Haskell is a flexible and expressive programming language. Haskell’s efficient memory management, type system, and code reusability practices, make it more popular. Altogether, the theme of Haskell’s attractiveness for AI developers is that the language is efficient. The programming language is broadly known for resolving errors, during the compilation process and even before that. But one reason why Haskell is behind other programming languages is its small community and thus it’s still undiscovered by many developers.
Conclusion
These are a few of the common AI programming languages that you should consider applying. However, needless to say, the choice of language often depends on the kind of AI application, existing infrastructure, and personal preferences. Ultimately, before deploying the same, it is important to have a good understanding of multiple languages to leverage their strengths in different AI projects.
Deploying one of the languages mentioned above is just a part of AI implementation, the first step is to find a team that can make your project successful. For this, Kodehash Technologies is here.
Contact the Kodehash team to learn more about what we can do!