These are the top AI programming languages

As a compiled language where developers control memory, C++ can execute machine learning programs quickly using very little memory. Java is used in AI systems that need to integrate with existing business systems and runtimes. R has a range of statistical machine learning use cases like Naive Bayes and random forest models. In data mining, R generates association rules, clusters data, and reduces dimensions for insights. R excels in time series forecasting using ARIMA and GARCH models or multivariate regression analysis.

It is popular for full-stack development and AI features integration into website interactions. Bring your unique software vision to life with Flatirons’ custom software development services, offering tailored solutions that fit your specific business requirements. You also need frameworks and code editors to design algorithms and create computer models.

The Best AI Programming Languages to Learn in 2024

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. 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. AI programming languages play a crucial role in the development of AI applications.

Different languages have different strengths and are suited to different tasks. For example, Python is great for prototyping and data analysis, while C++ is better for performance-intensive tasks. By learning multiple languages, you can choose the best tool for each job. MATLAB is a high-level language and interactive environment that is widely used in academia and industry for numerical computation, visualization, and programming.

There may be some fields that tangentially touch AI that don’t require coding. Lisp is the second-oldest programming language, used to develop much of computer science and modern programming languages, many of which have gone on to replace it. JavaScript is one of the best languages for web development but isn’t particularly well known for machine learning and AI. There is increasing interest in using JavaScript for Data Science, but many believe that this is due to the popularity of the language rather than its suitability. But to employ artificial intelligence in your company’s systems and services, you’re going to need software engineers who are up to the task.

Learning these languages will not only boost your AI skills but also enable you to contribute to the advancements of AI technology. JavaScript’s prominence in web development makes it an ideal language for implementing AI applications on the web. Web-based AI applications rely on JavaScript to process user input, generate output, and provide interactive experiences. From recommendation systems to sentiment analysis, JavaScript allows developers to create dynamic and engaging AI applications that can reach a broad audience.

R ranked sixth on the 2024 Programming Language Index out of 265 programming languages. The programming language is widely recognized and extensively used in various domains of artificial intelligence, including statistical analysis, data science, and machine learning. Its rich set of statistical capabilities, powerful data manipulation tools, and advanced data visualization libraries make it an ideal choice for researchers and practitioners in the field. On the other hand, Java provides scalability and integration capabilities, making it a preferred language for enterprise-level AI projects. As AI continues to shape our world, learning the best programming languages is essential for anyone interested in artificial intelligence development. By mastering the top programming languages such as Python, Java, JavaScript, and R, you can enhance your AI skills and stay competitive in the industry.

For more advanced probabilistic reasoning, ProbLog allows encoding logic with uncertainty measures. You can use libraries like DeepLogic that blend classic Prolog with differentiable components to integrate deep neural networks with symbolic strengths. When it comes to key dialects and ecosystems, Clojure allows the use of Lisp capabilities on Java virtual machines. By interfacing with TensorFlow, Lisp expands to modern statistical techniques like neural networks while retaining its symbolic strengths. With frameworks like React Native, JavaScript aids in building AI-driven interfaces across the web, Android, and iOS from a single codebase. Additionally, R is a statistical powerhouse that excels in data analysis, machine learning, and research.

What makes Lisp and Prolog suitable for AI development?

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. These are generally niche languages or languages that are too low-level. Not only are AI-related jobs growing in leaps and bounds, but many technical jobs now request AI knowledge as well. Plus, any C++ code can be compiled into standalone executable programs that predictably tap high performance across all operating systems and chips like Intel and AMD. It allows complex AI software to deploy reliably with hardware acceleration anywhere.

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. It has a smaller community than Python, but AI developers often turn to Java for its automatic deletion of useless data, security, and maintainability. This powerful object-oriented language also offers simple debugging and use on multiple platforms. Java’s libraries include essential machine learning tools and frameworks that make creating machine learning models easier, executing deep learning functions, and handling large data sets.

Other popular AI programming languages include Julia, Haskell, Lisp, R, JavaScript, C++, Prolog, and Scala. As for the libraries, the TensorFlow C++ interface allows direct plugging into TensorFlow’s machine-learning abilities. ONNX defines a standard way of exchanging neural networks for easily transitioning models between tools.

• It represents information naturally as code and data symbols, intuitively encoding concepts and rules that drive AI applications.
• MATLAB is particularly useful for prototyping and algorithm development, but it may not be the best choice for deploying AI applications in production.
• As Porter notes, «We believe LLMs lower the barrier for understanding how to program [2].»
• Also, Lisp’s code syntax of nested lists makes it easy to analyze and process, which modern machine learning relies heavily on.

ChatGPT has thrusted AI into the cultural spotlight, drawing fresh developers’ interest in learning AI programming languages. C++ is a general-purpose programming language with a bias towards systems programming, and was designed with portability, efficiency and flexibility of use in mind. Polls, surveys of data miners, and studies of scholarly literature databases show that R has an active user base of about two million people worldwide.

#1 Python

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. Julia is a newer language that’s gaining popularity for its speed and efficiency. And if you’re looking to develop low-level systems or applications with tight performance constraints, then C++ or C# may be your best bet.

R is also used for risk modeling techniques, from generalized linear models to survival analysis. It is valued for bioinformatics applications, such as sequencing analysis and statistical genomics. However, if you want to work in areas such as autonomous cars https://chat.openai.com/ or robotics, learning C++ would be more beneficial since the efficiency and speed of this language make it well-suited for these uses. Doing so will free human developers and programmers to focus on the high-level tasks and the creative side of their work.

In addition, OpenCV provides important computer vision building blocks. Python, R, Java, C++, Julia, MATLAB, Swift, and many other languages are powerful AI development tools in the hands of AI developers. The choice of language depends on your specific project requirements and your familiarity Chat PG with the language. As AI continues to advance, these languages will continue to adapt and thrive, shaping the future of technology and our world. Python can be found almost anywhere, such as developing ChatGPT, probably the most famous natural language learning model of 2023.

The early AI pioneers used languages like LISP (List Processing) and Prolog, which were specifically designed for symbolic reasoning and knowledge representation. 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. 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. Java is also an excellent option for anyone interested in careers that involve implementing machine learning programs or building AI infrastructure.

Comparison of AI Programing Languages

Although it can be used in developing AI, it’s more commonly used in academia to describe algorithms. Without a large community outside of academia, it can be a more difficult language to learn. JavaScript is a pillar in frontend and full-stack web development, powering much of the interactivity found on the modern web. A big perk of this language is that it doesn’t take long to learn JavaScript compared to other AI programming languages. Python, the most popular and fastest-growing programming language, is an adaptable, versatile, and flexible language with readable syntax and a vast community. Lisp has been around since the 60s and has been widely used for scientific research in the fields of natural languages, theorem proofs, and solving artificial intelligence problems.

R’s strong community support and extensive documentation make it an ideal choice for researchers and students in academia. The language is widely used in AI research and education, allowing individuals to leverage its statistical prowess in their studies and experiments. The collaborative nature of the R community fosters knowledge sharing and continuous improvement, ensuring that the language remains at the forefront of statistical AI applications. There’s no one best AI programming language, as each is unique in the way it fits your specific project’s needs. With the ever-expanding nature of generative AI, these programming languages and those that can use them will continue to be in demand. While some specific projects may not need coding, it’s the language that AI uses to speak and interact with data.

Julia’s mathematical maturity and high performance suit the needs of engineers, scientists, and analysts. Moreover, Julia’s key libraries for data manipulation (DataFrames.jl), machine learning (Flux.jl), optimization (JuMP.jl), and data visualization (Plots.jl) continue to mature. The IJulia project conveniently integrates Jupyter Notebook functionality. Java is well-suited for standalone AI agents and analytics embedded into business software. Monitoring and optimization use cases leverage Java for intelligent predictive maintenance or performance tuning agents.

CareerFoundry is an online school for people looking to switch to a rewarding career in tech. Select a program, get paired with an expert mentor and tutor, and become a job-ready designer, developer, or analyst from scratch, or your money back. It’s no surprise, then, that programs such as the CareerFoundry Full-Stack Web Development Program are so popular. Fully mentored and fully online, in less than 10 months you’ll find yourself going from a coding novice to a skilled developer—with a professional-quality portfolio to show for it. Developed by Apple and the open-source community, Swift was released in 2014 to replace Objective-C, with many modern languages as inspiration. Julia isn’t yet used widely in AI, but is growing in use because of its speed and parallelism—a type of computing where many different processes are carried out simultaneously.

Swift, the programming language developed by Apple, can be used for AI programming, particularly in the context of Apple devices. With libraries like Core ML, developers can integrate machine learning models into their iOS, macOS, watchOS, and tvOS apps. However, Swift’s use in AI is currently more limited compared to languages like Python and Java. Lisp is a powerful functional programming language notable for rule-based AI applications and logical reasoning. It represents knowledge as code and data in the same symbolic tree structures and can even modify its own code on the fly through metaprogramming. As for its libraries, TensorFlow.js ports Google’s ML framework to JavaScript for browser and Node.js deployment.

of the Best Programming Languages for AI Development

Python is a general-purpose, object-oriented programming language that has always been a favorite among programmers. It’s favored because of its simple learning curve, extensive community of support, and variety of uses. That same ease of use and Python’s ability to simplify code make it a go-to option for AI programming. 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. Moreover, R offers seamless integration with other programming languages like Python and Java, allowing custom software developers to combine the strengths of multiple languages in their AI projects.

If your company is looking to integrate Artificial Intelligence, there are a few languages you should seriously consider adding to your developer’s toolkit. The term “artificial intelligence” was first coined in 1956 by computer scientist John McCarthy, when the field of artificial intelligence research was founded as an academic discipline. It’s essentially the process of making a computer system that can learn and work on its own. Above all, demonstrating your passion and desire to learn through real-world experience can help you distinguish yourself among the competitive field. While there are many risks, the opportunities for global development and innovation are endless—and likely unstoppable.

Prioritizing ethics and understanding the true implications of AI are also critical. The programming languages that are most relevant to the world of AI today may not be the most important tomorrow. And, even more crucially, they may not be most utilized by your company. JavaScript is used where seamless end-to-end AI integration on web platforms is needed. The goal is to enable AI applications through familiar web programming.

These languages have been identified based on their popularity, versatility, and extensive ecosystem of libraries and frameworks. C++ is another language that has been around for quite some time, but still is a legitimate contender for AI use. One of the reasons for this is how widely flexible the language is, which makes it perfectly suited for resource-intensive applications. C++ is a low-level language that provides better handling for the AI model in production. And although C++ might not be the first choice for AI engineers, it can’t be ignored that many of the deep and machine learning libraries are written in C++. 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.

Get insights from the experts on building and scaling technology teams. Java also makes use of simplified debugging, and its easy-to-use syntax offers graphical data presentation and incorporates both WORA and Object-Oriented patterns. More importantly, the man who created Lisp (John McCarthy) was very influential in the field of AI, so much of his work had been implemented for a long time. There are several that can serve to make your AI integration dreams come true. Let’s dive in and take a look at 9 of the best languages available for Artificial Intelligence. Artificial Intelligence is on everybody’s mind—especially businesses looking to accelerate growth beyond what they’ve previously been able to achieve.

JavaScript toolkits can enable complex ML features in the browser, like analyzing images and speech on the client side without the need for backend calls. Node.js allows easy hosting and running of machine learning models using serverless architectures. Plus, custom data visualizations and professional graphics can be constructed through ggplot2’s flexible layered grammar of graphics concepts. TensorFlow for R package facilitates scalable production-grade deep learning by bridging into TensorFlow’s capabilities.

The language boasts a range of AI-specific libraries and frameworks like scikit-learn, TensorFlow, and PyTorch, covering core machine learning, deep learning, and high-level neural network APIs. In many cases, AI developers often use a combination of languages within a project to leverage the strengths of each language where it is most needed. For example, Python may be used for data preprocessing and high-level machine learning tasks, while C++ is employed for performance-critical sections. If you are looking for help leveraging programming languages in your AI project, read more about Flatirons’ custom software development services. R stands out for its ability to handle complex statistical analysis tasks with ease.

You can easily work with data and make cool graphs with libraries like NumPy and Pandas. Go was designed by Google and the open-source community to meet issues found in C++ while maintaining its efficiency. Go’s popularity has varied widely in the decade since it’s development. Haskell does have AI-centered libraries like HLearn, which includes machine learning algorithms. Java has a steep yet quick learning curve, but it’s incredibly powerful with a simple syntax and ease of debugging.

Python is an interpreted, high-level, general-purpose programming language with dynamic semantics. Here are the most popular languages used in AI development, along with their key features. The graduate in MS Computer Science from the well known CS hub, aka Silicon Valley, is also an editor of the website. She enjoys writing about any tech topic, including programming, algorithms, cloud, data science, and AI. Other top contenders include Java, C++, and JavaScript — but Python is likely the best all-around option for AI development. It can be worth considering specializing in a sub-field aligning with personal interests like natural language processing, computer vision, or robotics, Singh Ahuja says.

This helps accelerate math transformations underlying many machine learning techniques. It also unifies scalable, DevOps-ready AI applications within a single safe language. Haskell is a natural fit for AI systems built on logic and symbolism, such as proving theorems, constraint programming, probabilistic modeling, and combinatorial search. The language meshes well with the ways data scientists technically define AI algorithms. Julia is rapidly adopted for data science prototyping, with results then productionized in Python.

Scala: Bridging Functional and Object-Oriented Programming for AI

It is easy to learn, has a large community of developers, and has an extensive collection of frameworks, libraries, and codebases. However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits. Regarding libraries and frameworks, SWI-Prolog is an optimized open-source implementation preferred by the community.

You can foun additiona information about ai customer service and artificial intelligence and NLP. 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. While Python is still preferred across the board, both Java and C++ can have an edge in some use cases and scenarios.

For example, C++ could be used to code high-performance routines, and Java could be used for more production-grade software development. 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.

Python is well-suited for AI development because of its arsenal of powerful tools and frameworks. TensorFlow and PyTorch, for instance, have revolutionized the way AI projects are built and deployed. These frameworks simplify AI development, enable rapid prototyping, and provide access to a wealth of pre-trained models that developers can leverage to accelerate their AI projects. Like Java, C++ typically requires code at least five times longer than you need for Python. It can be challenging to master but offers fast execution and efficient programming.

A language like Fortran simply doesn’t have many AI packages, while C requires more lines of code to develop a similar project. A scripting or low-level language wouldn’t be well-suited for AI development. It shares the readability of Python, but is much faster with the speed of C, making it ideal for beginner AI development. Its speed makes it great for machine learning, which requires fast computation. Haskell is a functional and readable AI programming language that emphasizes correctness.

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. Likewise, AI jobs are steadily increasing, with in-demand roles like machine learning best ai language engineers, data scientists, and software engineers often requiring familiarity with the technology. Julia is a newer language with a small yet rapidly growing user base that’s centered in academic computing. Julia tends to be easy to learn, with a syntax similar to more common languages while also working with those languages’ libraries.