The 10 Best Programming Languages for AI Development

Java, C++, and JavaScript are other top contenders, but Python is probably the best all-around option for AI development. Although C++ may not be the first choice of developers for AI programming, various machine learning and deep learning libraries are written in the C++ language. Its notable feature is concise code, more readable and easier to write than other languages ​​like Java. The language integrates with Java and JavaScript and provides error-free coding with easy debugging, ensuring a fast and convenient development process. You have several programming languages for AI development to choose from, depending on how easy or technical you want your process to be. Another factor to consider is what system works best for the software you’re designing.

• C++ comes with limited but highly effective machine learning and deep learning libraries written in C++.
• Others are built to perform numerical analysis, while some are excellent at natural language processing.
• Its best quality makes it one of the top candidates for AI development, such as easy debugging and simple syntax.
• OpenCV offers an in-depth documentation guide to help programmers get up to speed with how to use C++ in your artificial intelligence projects.
• Prolog might not be as versatile or easy to use as Python or Java, but it can provide an invaluable service.
• When these packages are coupled with a wide range of libraries, the capabilities of the R programming language reach new heights.

So if you run a line of code, you can see the output immediately. Unlike R and Python, Julia is compiled, like C or Fortran, so it’s fast. You’ve probably seen what artificial intelligence (AI) can do in science fiction movies like Terminator or Her. Though it may come as a surprise, software engineering and writing go hand-in-hand.

Best Programming Languages for AI Development in 2023

From voice assistants to self-driving cars, AI and Deep Learning are already transforming the world as we know it. Its abstraction readiness mitigates the need for spending large amounts of time debugging errors. In Prolog, instead of using coded sequences, you simply chart out a basic set of facts, rules, metadialog.com goals, and queries. You can chalk its innocent fame up to its dynamic interface and arresting graphics for data visualization. This means C++ works well with hardware and machines but not so well for the more theoretical side of software. C++ has been around for quite some time and is admittedly low-level.

• It is easy to implement on various platforms, which is why it is so loved.
• The answer to this question depends on the scope of your AI project and your personal preferences as a programmer.
• This course covers variables, loops, functions, and even a little bit about debugging tools are covered in this course.
• In this article, we’ve gathered the most common languages for AI and machine learning algorithms development.
• If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor.
• Rust’s syntax is comparable to C++’s, but Rust also provides memory safety and forgoes garbage collection.

Python development is one of the
most popular programming languages globally and a top choice for AI
developers. However, after you have made this decision, you must also make a difficult choice about a programming language for AI development. You should know which language works best for your project from the many options. We’ve compiled a list of programming languages for artificial intelligence to help you make an informed choice. With ready-to-use libraries, object-oriented programming, easy testing, and faster development, Python is your go-to language for customized AI solutions.

The Best Programming Languages for AI

It makes the calculations by utilizing the parallel programming feature by distributing the calculations to the cores. Julia can also be integrated with TensorFlow.jl, MLBase.jl, and MXNet.jl. In addition, Python is one of the reasons AI has become so popular in recent years. The most used libraries for AI such as Scikit-Learn, Keras, TensorFlow, and PyTorch were written in Python. Originally published on Towards AI the World’s Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor.

• It is also difficult to learn compared to modern programming languages, and it lacks the community support and user interaction that Python and R have.
• It is the ease of using such packages that drives the popularity of R as the best programming language for data science.
• The best feature of Julia is the dynamic type system, which allows you to use the language for literally any process.
• Eventually, the type of project you will be working on determines your programming language of choice.
• It helps you build prototypes, create dynamic objects, and expand the possibilities of character processing.
• One way to tackle the question is by looking at the popular apps already around.

You can represent the data in graphics and offer better user interaction. This is a fantastic course for learning C++, and it’s available for free on Udemy. This course will teach you how to write in the powerful, fast, and widely used C++ programming language from the ground up, requiring just a basic understanding of computers. Another benefit to consider is the unending assistance from libraries and online communities. Imagine what you can do with machine learning libraries like NumPy and SciPy to construct desktop programs in Python using the Tkinter GUI framework. It is, however, a high-performance and widely used programming language capable of complex procedures for a wide range of activities and platforms.

AI Programming With Java

The java community is rich and active, allowing plenty of support for new developers and creative enrichment for seasoned developers across the world. Artificial Intelligence is increasingly a part of your everyday life, whether you notice or not. Gartner predicts that AI software will be worth $62 billion in 2022 alone, increasing 21% from 2021.

For example, Pharo has a numerical package called PolyMath that is almost equal to NumPy of Python. Additionally, Java can develop anything ranging from deep learning to data analysis, machine learning, and natural language processing. New developers can also get reliable guidance and assistance from the rich and active Java community that hosts seasoned developers worldwide. Java is a highly popular programming language with distinct qualities required for a successful AI development process. It is an object-oriented language with straightforward debugging and syntax.

AI Programming With JavaScript

In addition, because of its versatility and capacity to manage failures, Haskell is considered a safe programming language for AI. Over the years, LISP has lost some of its popularity owing to some of its inherent flaws. However, it did lay the foundation for earl AI development and remains a great language to learn for a primer on how the world of Artificial Intelligence evolved. Julia is a high-performance, flexible language used for scientific and numerical calculations. Julia has been downloaded over 34.8 million times and the Julia community has registered over 6,800 Julia packages for community use.

Dirox’s team of technology consultants, business gurus, software & apps coders, and design visionaries bring you innovative solutions on time, on budget, and on quality. We strive to bring you the best IT outsourcing & offshore services. It serves as a review of teachings and a refresher course for Java specialists.

Java

We have witnessed, used, and gotten used to this area of technology and it is getting better day by day. It’s a great course to learn AI on Coursera, suitable for people from different domain and tech. Artificial Intelligence (AI) and Deep Learning are two of the hottest topics in the field of technology.

Julia is a high-level, high-performance, dynamic programming language well suited for AI solutions that deal with numerical analysis and computational science. Designed by Graydon Hoare in 2010, Rust is multi-paradigm, a general-purpose programming language designed for performance and safety. Even though it is syntactically similar to C++, Rust guarantees memory safety unlike the former. Another benefit of Rust is that it offers memory safety without garbage collection and reference counting is only optional. Java is one of the most used programming languages for client-server web applications.

AI Programming With Julia

Aside from the languages mentioned above, many more compete in developing Artificial Intelligence. Those unmentioned heroes include Go, Matlab, Haskell, Prolog, LISP, Wolfram, and Rust, along with their endless frameworks and libraries that help make AI development https://www.metadialog.com/blog/best-programming-languages-to-choose-for-ai/ a dream come true. While the list goes on, there seems to be a unanimous agreement between firms, developers, and businesses that Python is the best language for AI development. It will improve your chances of finding a job in the field of AI/Machine Learning.

R is a very popular programming language for statistical programming, especially data analysis and statistical computing. The language was created by statisticians Ross Ihaka and Robert Gentleman in 1993. As of March 2022, R is ranked at 11th position in the TIOBE index. Furthermore, Python has enriched text processing features, scripting with modular designs, and simple syntax that functions best for AI programs and NPL (Natural Language Processing). It’s also easy to learn and master the Python language, as it is short, clear, and straightforward. Additionally, it has a vast following of users with an active support base.

Python AI Source Code

It has a rich library of Keras, MXNet, TensorFlow, PyTorch, Pybrain, and Scikit-learn, which equips it for AI development. There aren’t always universal, one-size-fits-all solutions in the realm of artificial intelligence. The needs and scale of your project will determine the AI programming language you use. If your project calls for substantial data analysis, consider using R, which was created to handle large data sets easily. However, Python would be better for implementing machine learning models in production than R. It is one of the most popular and
widely used programming languages for developing AI solutions.

Though Android developers have the option to work with Kotlin as well, Java is a native language for Android app development. Its key feature is that you can use Java almost anywhere, on any platform, through its virtual machine. Lisp (historically stylized as LISP) is one of the oldest languages in circulation for AI development. Machine learning is a subset of AI that involves using algorithms to train machines.

We can call it the most promising tool for businesses in this area. It’s also a lazy programming language, meaning it only evaluates pieces of code when necessary. Even so, the right setup can make Haskell a decent tool for AI developers. It offers several tools for creating a dynamic interface and impressive graphics to visualize your data, for example. There’s also memory management, metaprogramming, and debugging for efficiency. If you’re working with AI that involves analyzing and representing data, R is your go-to programming language.

JAVA is probably the most-used language around the globe for different processes and AI is definitely one of them. The best benefit of using the JAVA programming language is the presence of Virtual Machine Technology. Well, Java Virtual Machine eases the implementation process, which saves your time and energy to compile the program again and again. The work of specialists in these areas is aimed at the same result, but the methods of achieving it are different.

Because of its “write once, run anywhere” programming philosophy, Java, which was initially introduced more than 20 years ago in 1995, is employed by AI programmers. Java is meant to have the fewest dependencies possible, so it can be run on any platform with little effort. Many factors affect the choice of
languages, such as cost, scalability, and maintainability. You can hire an AI developer to take advantage of the best tools for each task instead of forcing everyone down a single path. You can use JavaScript to create
web pages and applications on web browsers and mobile devices like smartphones
and tablets. However, though being so flexible, it has various deficiencies, such as lack of well-known libraries, not so-human-friendly syntax, etc.