Top 8 Programming Languages for AI Development in 2024

On the other hand, if you already know Java or C++, it’s entirely possible to create excellent AI applications in those languages — it will be just a little more complicated. Let’s look at the best language for AI, other popular AI coding languages, and how you can get started today. Lucero is a programmer and entrepreneur with a feel for Python, data science and DevOps. Raised in Buenos Aires, Argentina, he’s a musician who loves languages (those you use to talk to people) and dancing. In last year’s version of this article, I mentioned that Swift was a language to keep an eye on.

With AI, your business can save time and money by automating and optimizing typically routine processes. Once AI is in place, you can be sure that those tasks will be handled faster and with more accuracy and reliability than can be achieved by a human being. As BairesDev CTO, Justice Erolin translates BairesDev’s vision into technical roadmaps through the planning and coordinating of engineering teams. This prevalence has created a fantastic playing ground for companies looking to develop more AI solutions. Drive your projects beyond expectations and surpass your business objectives.

C++ is a low-level programming language that has been around for a long time. C++ works well with hardware and machines but not with modern conceptual software. Developers use this language for most development platforms because it has a customized virtual machine. Chat PG This post lists the ten best programming languages for AI development in 2022. With formerly Facebook coming up with new technological innovations like Meta, it’s worth exploring how artificial intelligence will impact the future of software development.

AI programming languages play a crucial role in the development of AI applications. They enable custom software developers to create software that can analyze and interpret data, learn from experience, make decisions, and solve complex problems. By choosing the right programming language, developers can efficiently implement AI algorithms and build sophisticated AI systems. Yes, R can be used for AI programming, especially in the field of data analysis and statistics. R has a rich ecosystem of packages for statistical analysis, machine learning, and data visualization, making it a great choice for AI projects that involve heavy data analysis. However, R may not be as versatile as Python or Java when it comes to building complex AI systems.

Other AI programming options

It excels in pattern matching and automatic backtracking, which are essential in AI algorithms. It is a statically-typed, object-oriented programming language that is known for its portability and scalability. Java’s strong typing helps to prevent errors, making it a reliable choice for complex AI systems. It also has a wide range of libraries and tools for AI and machine learning, such as Weka and Deeplearning4j. Furthermore, Java’s platform independence means that AI applications developed in Java can run on any device that supports the Java runtime environment. Python, with its simplicity and extensive ecosystem, is a powerhouse for AI development.

It is widely used in various AI applications and offers powerful frameworks like TensorFlow and PyTorch. Java, on the other hand, is a versatile language with scalability and integration capabilities, making it a preferred choice in enterprise environments. JavaScript, the most popular language for web development, is also used in web-based AI applications, chatbots, and data visualization. R is another heavy hitter in the AI space, particularly for statistical analysis and data visualization, which are vital components of machine learning.

Similarly, when working on NLP, you’d prefer a language that excels at string processing and has strong natural language understanding capabilities. For hiring managers looking to future-proof their tech departments, and for developers ready to broaden their skill sets, understanding AI is no longer optional — it’s essential. The heartbeat of AI, though, lies within its programming languages. Without these, the incredible algorithms and intricate networks that fuel AI would be nothing more than theoretical concepts. Although it isn’t always ideal for AI-centered projects, it’s powerful when used in conjunction with other AI programming languages. With the scale of big data and the iterative nature of training AI, C++ can be a fantastic tool in speeding things up.

It’s used for advanced development such as data processing and distributed computing. The programming world is undergoing a significant shift, and learning artificial intelligence (AI) programming languages appears more important than ever. In 2023, technological research firm Gartner revealed that up to 80 percent of organizations will use AI in some way by 2026, up from just 5 percent in 2023 [1]. There are many popular AI programming languages, including Python, Java, Julia, Haskell, and Lisp.

Deploying one of the languages above in your tech stack is only a minor part of building competent AI software. Julia’s wide range of quintessential features also includes direct support for C functions, a dynamic type system, and parallel and distributed computing. Although its community is small at the moment, Julia still ends up on most lists for being one of the best languages for artificial intelligence. Come to think of it, many of the most notorious machine learning libraries were built with C++. Mobile app developers are well-aware that artificial intelligence is a profitable application development trend.

Some must-use Python libraries for machine learning and AI are Pandas, Tensor Flow, SciPy, NumPy, and Keras. JavaScript is widely used in the development of chatbots and natural language processing (NLP) applications. With libraries like TensorFlow.js and Natural, developers can implement machine learning models and NLP algorithms directly in the browser. JavaScript’s versatility and ability to handle user interactions make it an excellent choice for creating conversational AI experiences.

How to Learn Artificial Intelligence: Top Resources

Whether you’re a hiring manager assembling a world-class AI team, or a developer eager to add cutting-edge skills to your repertoire, this guide is your roadmap to the key languages powering AI. Julia is a newer language with a small yet rapidly growing user base that’s centered in academic computing. It’s fast and flexible, which allows quick iterations, ideal for AI. Julia tends to be easy to learn, with a syntax similar to more common languages while also working with those languages’ libraries. Scala is a user-friendly and dependable language with a large community but can still be complex to learn.

Julia’s mathematical syntax and high performance make it great for AI tasks that involve a lot of numerical and statistical computing. Its relative newness means there’s not as extensive a library ecosystem or community support as for more established languages, though this is rapidly improving. Libraries like Weka, Deeplearning4j, and MOA (Massive Online Analysis) aid in developing AI solutions in Java. However, Java may be overkill for small-scale projects and it doesn’t boast as many AI-specific libraries as Python or R. For instance, when dealing with ML algorithms, you might prioritize languages that offer excellent libraries and frameworks for statistical analysis.

Because of those elements, C++ excels when used in complex AI applications, particularly those that require extensive resources. It’s a compiled, general-purpose language that’s excellent for building AI infrastructure and working in autonomous vehicles. The most notable drawback of Python is its speed — Python is an interpreted language. But for AI and machine learning applications, rapid development is often more important than raw performance. C++ is a powerful, high-performance language that is often used in AI for tasks that require intensive computations and precise control over memory management. However, C++ has a steeper learning curve compared to languages like Python and Java.

• When you learn Scala for AI, you’ll have access to Scaladex, a database of all Scala libraries, including the ones for artificial intelligence.
• If you want to learn JavaScript for artificial intelligence and rapid prototyping, some popular libraries you should take note of are MindJS, Stdlibjs, BrainJS, and ConvNetJS.
• Although it isn’t always ideal for AI-centered projects, it’s powerful when used in conjunction with other AI programming languages.
• However, Swift’s use in AI is currently more limited compared to languages like Python and Java.
• The key thing that will stand to you is to have a command of the essentials of coding.
• Its interoperability makes it an excellent tool for implementing machine learning algorithms and applying them to real-world problems.

If your company is looking to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer’s toolkit. Haskell has various sophisticated features, including type classes, which permit type-safe operator overloading. In most cases, R is better than Python when it comes to statistics. Developed in 1958, best programming language for artificial intelligence Lisp is named after ‘List Processing,’ one of its first applications. By 1962, Lisp had progressed to the point where it could address artificial intelligence challenges. Nowadays, cloud technology makes it so chatbots have a whole store of data to access new and old information, meaning chatbots are worlds more intelligent than in the time of Prolog.

In AI development, data is crucial, so if you want to analyze and represent data accurately, things are going to get a bit mathematical. So the infamous FaceApp in addition to the utilitarian Google Assistant both serve as examples of Android apps with artificial intelligence built-in through Java. Its key feature is that you can use Java almost anywhere, on any platform, through its virtual machine. By 1962 and with the aid of creator John McCarthy, the language worked its way up to being capable of addressing problems of artificial intelligence. Machine learning is a subset of AI that involves using algorithms to train machines.

It should also feature good runtime performance, good tools support, a large community of programmers, and a healthy ecosystem of supporting packages. That’s a long list of requirements, but there are still plenty of good options. It’s one of the most frequently used programming languages, with applications in AI, machine learning, data science, web apps, desktop apps, networking apps, and scientific computing.

We’ll cover everything you need to know about which dynamic programming language is best for different tasks like dynamic object creation, probabilistic programming, and graphical representation. We’ll also help you get a clear picture of what artificial intelligence and programming languages are and go over the programming languages used by the different types of AI professionals. C++’s low-level programming capabilities make it ideal for managing simple AI models.

For example, developers utilize C++ to create neural networks from the ground up and translate user programming into machine-readable codes. Additionally, R is a statistical powerhouse that excels in data analysis, machine learning, and research. Learning these languages will not only boost your AI skills but also enable you to contribute to the advancements of AI technology.

However, one thing we haven’t really seen since the launch of TensorFlow.js is a huge influx of JavaScript developers flooding into the AI space. I think that might be due to the surrounding JavaScript ecosystem not having the depth of available libraries in comparison to languages like Python. Breaking through the hype around machine learning and artificial intelligence, our panel talks through the definitions and implications of the technology. If you are ready to start your career in tech, learning artificial intelligence is a great step in the right direction. The industry is still in its early stages and there are lots of opportunities to learn and contribute. The fact that artificial intelligence engineers are among the highest-paid workers in the country is another strong motivation to break into the industry.

Java is more user-friendly while C++ is a fast language best for resource-constrained uses. Php, Ruby, C, Perl, and Fortran are some examples of languages that wouldn’t be ideal for AI programming. It has a simple and readable syntax that runs faster than most readable languages. It works well in conjunction with other languages, especially Objective-C. C++ is a fast and efficient language widely used in game development, robotics, and other resource-constrained applications.

AI engineers use JavaScript to integrate AI software into the World Wide Web. An AI Java application is more intelligent than traditional web applications like search algorithms. If you want to learn JavaScript for artificial intelligence and rapid prototyping, some popular libraries you should take note of are MindJS, Stdlibjs, BrainJS, and ConvNetJS. This kind of language provides a major advantage over others for the automation of tasks that usually require human intervention and the writing of learning algorithms. Scripting programming languages are usually interpreted into machine-readable languages that are not compiled.

Why is object-oriented programming important in AI development?

It features adaptable source code and works on various operating systems. Developers often use it for AI projects that require handling large volumes of data or developing models in machine learning. Python is undeniably one of the most sought-after artificial intelligence programming languages, used by 41.6% of developers surveyed worldwide. Its simplicity and versatility, paired with its extensive ecosystem of libraries and frameworks, have made it the language of choice for countless AI engineers. That being said, Python is generally considered to be one of the best AI programming languages, thanks to its ease of use, vast libraries, and active community. R is also a good choice for AI development, particularly if you’re looking to develop statistical models.

• It’s favored because of its simple learning curve, extensive community of support, and variety of uses.
• C++ is a powerful, high-performance language that is often used in AI for tasks that require intensive computations and precise control over memory management.
• In the rapidly evolving field of AI, developers need to keep up with the latest advancements and trends.
• It’s designed to combine the performance of C with the ease and simplicity of Python.
• These languages have been identified based on their popularity, versatility, and extensive ecosystem of libraries and frameworks.

C++ is a flexible programming language based on object oriented principles, meaning it can be used for AI. The syntax of the programming language is not easy to understand, however, making it hard to learn, especially for beginners. Yes, it is possible to pick the wrong programming language for artificial intelligence.

What makes Lisp and Prolog suitable for AI development?

You can learn artificial intelligence by getting a computer science degree and specializing in artificial intelligence. You can also learn artificial intelligence in a coding bootcamp, teach yourself through online courses, or secure an apprenticeship at a company that deals with artificial intelligence. Each of these offers a different learning style, so pick the one that feels right for you. You can foun additiona information about ai customer service and artificial intelligence and NLP. Many programming languages are commonly used for AI, but there is a handful that are not suitable for it. Perl is one example of a programming language that is typically not used for AI because it is a scripting language. The language has more than 6,000 built-in functions for symbolic computation, functional programming, and rule-based programming.

Writing an AI application in Java may feel a touch boring, but it can get the job done—and you can use all your existing Java infrastructure for development, deployment, and monitoring. Some great courses for learning computer programming are “Computer Programming for Beginners” by Udemy and “Python for Everybody” by Coursera. On top of that, AI is exponentially faster at making business decisions based on input from various sources (such as customer input or collected data). AI can serve as chatbots, in mobile and web applications, in analytic tools to identify patterns that can serve to optimize solutions for any given process and the list goes on. Join a network of the world’s best developers and get long-term remote software jobs with better compensation and career growth. Because of its capacity to execute challenging mathematical operations and lengthy natural language processing functions, Wolfram is popular as a computer algebraic language.

According to IDC, the AI market will surpass $500 billion by 2024 with a five-year CAGR of 17.5 percent and total revenue of $554.3 billion. However, the first step towards creating efficient solutions is choosing the best programming languages for AI software. Haskell is a statically typed and purely functional programming language. What this means, in summary, is that Haskell is flexible and expressive. It’s an open-source machine learning library where you can train deep neural networks. Now that we’ve laid out what makes a programming language well-suited for AI, let’s explore the most important AI programming languages that you should keep on your radar.

Its straightforward syntax and vast library of pre-built functions enable developers to implement complex AI algorithms with relative ease. In this article, we will explore the best programming languages for AI in 2024. These languages have been identified based on their popularity, versatility, and extensive ecosystem of libraries and frameworks. It’s a preferred choice for AI projects involving time-sensitive computations or when interacting closely with hardware. Libraries such as Shark and mlpack can help in implementing machine learning algorithms in C++.

Plus, Java’s object-oriented design makes the language that much easier to work with, and it’s sure to be of use in AI projects. Though Android developers have the option to work with Kotlin as well, Java is a native language for Android app development. And recent research suggests that the majority of artificial intelligence projects are market-oriented. Processing and analyzing text data, enabling language understanding and sentiment analysis. Swift has a high-performance deep learning AI library called Swift AI. Scala was designed to address some of the complaints encountered when using Java.

Python can be found almost anywhere, such as developing ChatGPT, probably the most famous natural language learning model of 2023. Some real-world examples of Python are web development, robotics, machine learning, and gaming, with the future of AI intersecting with each. It’s no surprise, then, that Python is undoubtedly one of the most popular AI programming languages. For example, if you want to create AI-powered mobile applications, you might consider learning Java, which offers a combination of easy use and simple debugging.

Therefore, the choice of programming language often hinges on the specific goals of the AI project. Julia excels in performing calculations and data science, with benefits that include general use, fast and dynamic performance, and the ability to execute quickly. It’s excellent for use in machine learning, and it offers the speed of C with the simplicity of Python. Julia remains a relatively new programming language, with its first iteration released in 2018.

By learning multiple languages, you can choose the best tool for each job. Despite its roots in web development, JavaScript has emerged as a versatile player in the AI arena, thanks to an active ecosystem and powerful frameworks like TensorFlow.js. Here are my picks for the six best programming languages for AI development, along with two honorable mentions. Some of these languages are on the rise, while others are slipping.

These languages offer unique features and capabilities for different AI tasks, whether it’s machine learning, natural language processing, or data visualization. R is another popular programming language for machine learning that is most popularly used for graphics and statistical computing. The programming language is frequently used by big data analysts and other machine learning experts, like AI engineers. If you are working on complex projects for big data applications, R should be on your list of top programming languages to learn for faster development.

A good programmer can write an AI in nearly any programming language. Technically, you can use any language for AI programming — some just make it easier than others. C++ has also been found useful in widespread domains such as computer graphics, image processing, and scientific computing. Similarly, C# has been used to develop 3D and 2D games, as well as industrial applications. Artificial intelligence is one of the most fascinating and rapidly growing fields in computer science. And it’s as hot a job market as you can get (see Gartner forecasts).

In the rapidly evolving field of AI, developers need to keep up with the latest advancements and trends. Staying knowledgeable about cutting-edge AI programming languages allows developers to stay competitive and deliver innovative AI solutions. Selecting the appropriate programming language based on the specific requirements of an AI project https://chat.openai.com/ is essential for its success. Different programming languages offer different capabilities and libraries that cater to specific AI tasks and challenges. AI is written in Python, though project needs will determine which language you’ll use. If your professional interests are more focused on data analysis, you might consider learning Julia.

R is a popular language for AI among both aspiring and experienced statisticians. Though R isn’t the best programming language for AI, it is great for complex calculations. Lisp (historically stylized as LISP) is one of the most widely used programming languages for AI. But that shouldn’t deter you from making it your language of choice for your next AI project. Bring your unique software vision to life with Flatirons’ custom software development services, offering tailored solutions that fit your specific business requirements.