Artificial intelligence is evolving very rapidly and its applications are becoming more and more numerous. AI tends to become indispensable for organizations and people in the future. This is an area that many developers are passionate about and there are many tools to contribute to its development. Among the programming languages for AI are Python, C, Lisp, Prolog… What programming language will you choose to learn in 2020 to design AI products? Here is a selection of the best AI programming languages.
This is new to anyone, Python is one of the most popular programming languages in the field of AI. It is considered by many in the community to be the first programming language of artificial intelligence because of its simplicity. The python syntax is very simple and is easily assimilated. This is why many artificial intelligence algorithms can be easily implemented in Python. It requires a very short development time compared to other languages such as Java, C- or Ruby.
It supports object-oriented, functional and procedural programming styles. There are many python libraries, making it easier for programmers. Developed in the early 1990s, Python has become one of the fastest growing programming languages due to its adaptability and ease of learning. Finally, in a recent survey of Developpez.com on the best languages for AI development, he came first with just over 55% of the vote, followed by C-TM (45.71 per cent). Java (18.57).
It is one of the fastest programming languages, and its speed is a great help for time-sensitive AI programming projects. C-C can be used for statistical AI approaches such as those found in neural networks. With a very fast turnaround time and the principles of the POO, the C is a good choice for AI programs. In fact, a large part of machine learning and deep learning libraries are written in C/C and offer APIs for the same.
In addition, they also offer wrappers for other programming languages. If you want to keep control over running time and performance, the C is obviously a good choice. Another important point is that it is a language that allows the reuse of developing programs through inheritance ownership and data masking, saving time and money.
Lisp is one of the oldest languages best suited to the development of AI. It is the work of John McCarthy, who is also known as one of the fathers of artificial intelligence. Its very first version dates back to 1958. Lisp has the ability to deal effectively with symbolic information. It was originally developed for Lambda computing, and since its development, it has evolved a lot over the years while bringing many ideas to computing. Dynamic typing, recursion, higher-order functions, etc. can be distinguished.
There is also automatic storage management, the self-hosted compiler and the tree structure of the data. Lisp has a development cycle that allows for interactive evaluation of expressions and recompilation of functions or files while the program is still running. Later, many of his functions were copied by other languages. Between 1970 and 80, it became the default language for artificial intelligence research and has since been used in AI programs that calculate very well with symbols.
Lisp’s symbolic expression and calculation with these symbols are his strengths. Similarly, Lisp consists of a macro system, a well-developed compiler that can produce effective code, and a library of collection types, including hash tables and dynamic size lists.
R is a computer language interpreted and typed dynamically. If you’ve already programmed in another language, you can quickly understand how R-language works. In addition to being a versatile language, R has a number of software packages used in the field of machine learning. These software packages make it easier to implement machine learning algorithms to solve business-related problems.
R is one of the programming languages of artificial intelligence. It is also one of the most effective environments for analyzing and manipulating data for statistical purposes. Many large companies use R language for data analysis, big data modeling and visualization. Some of them are Google, Uber, the New York Times. R is widely used in the banking sector, particularly for the forecasting of various risks.
Prolog is quoted alongside Lisp when it comes to development in the field of AI. Prolog offers several features, including efficient model matching, automatic feedback, and tree-shaped data structuring. These features provide a surprisingly powerful and flexible programming framework. It is entirely logic-based and is a language widely used for theorem demonstration, non-digital programming, natural language processing and AI in general.
Prolog is particularly suited to problems that involve structured objects and relationships between them. The nature of Prolog makes the implementation of facts and rules simple and straightforward. It supports the development of graphical user interface, administration and network applications. It is very well suited to projects such as voice control systems and model filling.
Java language can also be considered a very good choice for AI development. The best thing about this language is its JVM (Java virtual machine) which allows you to create a unique version of an application, which will work on all platforms, which means that the program is independent of the platform. Its strengths are transparency, maintainability and portability. It has several advantages, including ease of use, ease of debugging, packet services, simplification of work on large-scale projects.
There is also graphic representation of the data and better interaction with the user. It also includes Swing and SWT (the standard widget toolbox). These tools can make graphics and interfaces more attractive and sophisticated.
A few years ago, Lua was in the world of artificial intelligence. With the Torch framework, Lua was one of the most popular languages for the development of deep learning. And there is always a lot of deep learning work on GitHub that defines the models with Lua/Torch. With the advent of frameworks such as TensorFlow and PyTorch, the use of Lua has decreased considerably.
Julia is a high-level, high-performance and dynamic programming language for scientific computing, with a syntax familiar to users of other similar development environments (MATLAB, R, Scilab, Python, etc.). This makes it a good choice in the mathematical world of AI. Although it’s not very popular as a language choice right now. Wrappers like TensorFlow.jl and Mocha (heavily influenced by Caffe) offer good deep learning support.
Chris Lattner, creator of the LLVM compiler and Swift programming language, announced Swift for TensorFlow. Swift for TensorFlow allows you to import Python libraries such as NumPy and use them in the Swift code almost as with any other library.
MATLAB makes difficult parts of machine learning easy with point-and-click applications to train and compare models, advanced signal processing and feature extraction techniques, feature selection to optimize model performance, the ability to use the same code to extend processing to big data and clusters, etc.
TensorFlow.js is still in its infancy. For now, it works in the browser, but not in Node.js. It does not yet implement the full TensorFlow API.
So what AI programming language are you going to learn in 2020?
How about you?
Do you already master one of these programming languages for AI? What are the reasons for your choice?
What other languages would you like to learn and why?
Which programming languages do you think are better for AI? I don’t know why.