In the world of artificial intelligence (AI), the selection of programming language plays an important role in making the project successful. The diverse demands of AI applications, from machine learning to natural language processing, require programming languages to build AI apps with specific strengths. If you are planning to integrate Artificial Intelligence, then there are a few languages one must consider adding to their toolkit. Also learning and understanding programming languages is important for developers to write efficient and effective code, along with collaborating with other team members on various projects.
To help you make the right decision, we have discussed the top programming languages to build AI apps, each contributing uniquely to the development of intelligent applications.
Python
Python has emerged as the powerhouse for AI development. Its readability, extensive libraries, and community support make it an ideal language for building AI applications. Widely used in data science and machine learning, Python boasts frameworks like TensorFlow and PyTorch, simplifying the implementation of complex AI algorithms.
Python can easily be downloaded from the Python Software Foundation’s website for free while numerous third-party Python packages are also accessible via the website.
The most important reason why Python always remained at the top is that it consists of AI-specific frameworks that were created for the language. One of the most popular is TensorFlow, which is an open-source library created for machine learning and is used for training and inference of deep neural networks.
Java
Needless to say, Java is an important language for AI. Java’s platform independence and scalability make it a versatile choice for AI applications. With Java, developers can write code that runs seamlessly across different platforms. Its stability and maturity make it suitable for deploying AI solutions in various environments, contributing to its popularity in enterprise-level AI development.
Java consists of Deep Java Library, a library built by Amazon to create deep learning abilities. Java, which is among the top programming languages to build AI apps also makes use of simplified debugging, and its easy-to-use syntax offers graphical data presentation and incorporates both WORA and Object-Oriented patterns.
C++
Renowned for its performance and speed, C++ is preferred in AI applications that demand computational efficiency. Its ability to handle resource-intensive tasks makes it a go-to language for implementing AI algorithms. Libraries like TensorFlow leverage C++ to optimise the performance of machine learning models. Despite not being the first choice for AI engineers, it can’t be ignored that the majority of the deep and machine learning libraries are written in C++.
And C++ converts user code to machine-readable code, making it incredibly efficient and performant. C++ language has AI speech recognition implementation, and deep learning libraries like MapReduce, mlpack, and MongoDB.
R
R has established itself as the leading programming language to build AI apps for statistical computing and data analysis. Widely used by data scientists, R excels in handling large datasets and performing advanced statistical analyses, making it a crucial tool for AI applications in data-driven industries. R might not be an ideal language for AI, but it’s fantastic at crunching very large numbers, which makes it better than Python at a bigger scale.
With R’s inbuilt functional programming, vectorial computation, and Object-Oriented Nature, it becomes a viable language for Artificial Intelligence.
Lisp
Lisp also holds a decent place in AI history as one of the earliest languages designed explicitly for artificial intelligence. Its unique features, like symbolic expressions, makes it invaluable for tasks involving symbolic reasoning and manipulation. Lisp, which was originally created as a practical mathematical notation for programs, later became the top choice of developers in the field of AI.
Even though Lisp is the second oldest programming language in use, it includes several features like dynamic object creation, mandatory garbage collection, modification of programs, etc. that are important for projects’ success.
Prolog
Prolog, a logic programming language, is well-suited for AI applications involving rule-based systems and symbolic reasoning. Its strengths lie in representing knowledge and solving problems through logical inference, making it suitable for expert systems and knowledge-based AI. Prolong is preferable in cases where developers have to lay emphasis on problems as the language can execute the program using its search tools. Some of the facets and tools that make Prolong a dependable AI language include declarative nature, intelligent database retrieval, natural language processing, tree-based data structuring, etc.
Final Thoughts
In the dynamic landscape of AI, the selection of programming languages is important. Each of the programming languages to build AI apps brings its unique strengths, catering to different aspects of AI development. Whether prioritising simplicity, performance, or platform independence, developers have a diverse toolkit of languages to choose from. As AI continues to evolve, these languages will continue to play a pivotal role in shaping the future of intelligent applications, pushing the boundaries of what is possible in the realm of artificial intelligence.
If your company requires Artificial Intelligence development services, you must start integrating one or more of these languages. With the right development team by your side, there is no limit to how AI can accelerate the growth of your company.
For more info, connect with the professionals at Kodehash Technologies who are highly experienced in programming languages to build AI apps and help enhance overall productivity.