6 best programming languages for AI development

AI is an essential part of the modern development process, and knowing suitable AI programming languages can help you succeed in the job market. Explore popular coding languages and other details that will be helpful in 2024. The best programming languages for artificial intelligence include Python, R, Javascript, and Java.

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. CodeGPT is an AI-powered development platform that offers a marketplace of specialized AI Assistants, designed to enhance coding efficiency, automate tasks, and improve overall development workflows.

With features like code suggestions, auto-completion, documentation insight, and support for multiple languages, Copilot offers everything you’d expect from an AI coding assistant. Whether you’re a student, a beginner developer, or an experienced pro, we’ve included AI coding assistants to help developers at all skill levels, including free and paid options. If you’re reading cutting-edge deep learning research on arXiv, then you will find the majority of studies that offer source code do so in Python. While IPython has become Jupyter Notebook, and less Python-centric, you will still find that most Jupyter Notebook users, and most of the notebooks shared online, use Python.

In that case, it may be easier to develop AI applications in one of those languages instead of learning a new one. Ultimately, the best AI language for you is the one that is easiest for you to learn. Additionally, AI programming requires more than just using a language. You also need frameworks and code editors to design algorithms and create computer models.

Artificial intelligence (AI) is a rapidly growing field in software development, with the AI market expected to grow at a CAGR of 37.3% from 2023 to 2030 to reach USD 1,811.8 billion by 2030. This statistic underscores the critical importance of selecting the appropriate programming language. Developers must carefully consider languages such as Python, Java, JavaScript, or R, renowned for their suitability in AI and machine learning applications. By aligning with the right programming language, developers can effectively harness the power of AI, unlocking innovative solutions and maintaining competitiveness in this rapidly evolving landscape. Julia is a high-performance programming language that is focused on numerical computing, which makes it a good fit in the math-heavy world of AI.

The Python community is lively and supportive, with many developers and experts ready to help those working on AI. The strong Python community offers knowledge, support, and inspiration to AI developers. So, analyze your needs, use multiple other languages for artificial intelligence if necessary, and prioritize interoperability. Make informed decisions aligned with your strategic roadmap and focus on sound architectural principles and prototyping for future-ready AI development. Choosing the best AI programming language comes down to understanding your specific goals and use case, as different languages serve different purposes.

It has a lot of libraries and frameworks, like BigDL, Breeze, Smile and Apache Spark, some of which also work with Java. The languages you learn will be dependent on your project needs and will often need to be used in conjunction with others. Few codebases and integrations are available best coding languages for ai for C++ because developers don’t use C++ as frequently as Python for AI development. In fact, Python has become the "language of AI development" over the last decade—most AI systems are now developed in Python. These are generally niche languages or languages that are too low-level.

We’ve also taken the time to answer the question “what is an AI coding assistant? ”, along with a detailed breakdown of how they can help students, beginner developers, and experienced professionals. As a collaboration between GitHub, OpenAI, and Microsoft, Copilot is the most popular AI coding assistant available in 2024, with free, personal and business plans.

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. Having an AI coding assistant can also be like coding with a tutor or another programmer by your side. If you’re using a tool like ChatGPT for programming, you can ask it specific questions to help you solve problems or get unstuck. They can also introduce you to common coding patterns and help you learn new programming languages. A few years ago, Lua was riding high in the world of artificial intelligence due to the Torch framework, one of the most popular machine learning libraries for both research and production needs.

While some specific projects may not need coding, it’s the language that AI uses to speak and interact with data. 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. 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. Java has a steep yet quick learning curve, but it’s incredibly powerful with a simple syntax and ease of debugging.

Comparison of AI Programing Languages

Trained on extensive footage of Doom gameplay, the model can effectively predict the next “gaming state” when a player “controls” the character in the simulation. Generative AI coding startups Cognition, Poolside and Anysphere have closed mammoth rounds in the past year — and GitHub’s AI coding tool Copilot has over 1.8 million paying users. The productivity gains the tools could deliver have been sufficient to convince investors — and customers — to ignore their flaws. According to one survey, the average dev spends close to 20% of their workweek maintaining existing code rather than writing anything new.

The past couple of years have truly felt like the beginning of the AI renaissance. Along with the general influx of AI tools for purposes like art and writing, there has been an explosion of AI that can write code. 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.

Undoubtedly, the first place among the most widely used programming languages in AI development is taken by Python. In this particular tech segment, it has undeniable advantages over others and offers the most enticing characteristics for AI developers. Statistics prove that Python is widely used for AI and ML and constantly rapidly gains supporters as the overall number of Python developers in the world exceeded 8 million. In the field of artificial intelligence, this top AI language is frequently utilized for creating simulations, building neural networks as well as machine learning and generic algorithms. From robotic assistants to self-driving automobiles, Java is employed in numerous AI applications, apart from being used for machine learning.

Why Should You Use An AI Coding Assistant?

The active and helpful R community adds to its collection of packages and libraries, offering support and knowledge. This community ensures that R users can access the newest tools and best practices in the field. When it comes to key dialects and ecosystems, Clojure allows the use of Lisp capabilities on Java virtual machines.

R is used in so many different ways that it cannot be restricted to just one task. Developers often use Java for AI applications because of its favorable features as a high-level programming language. The object-oriented nature of Java, which follows the programming principles of encapsulation, inheritance, and polymorphism, makes the creation of AI algorithms simpler. This top AI programming language is ideal for developing different artificial intelligence apps since it is platform-independent and can operate on any platform.

If your professional interests are more focused on data analysis, you might consider learning Julia. This relatively new programming language allows you to conduct multiple processes at once, making it valuable for various uses in AI, including data analysis and building AI apps. 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]. If you are looking for help leveraging programming languages in your AI project, read more about Flatirons’ custom software development services. Additionally, R is a statistical powerhouse that excels in data analysis, machine learning, and research.

R performs better than other languages when handling and analyzing big data, which makes it excellent for AI data processing, modeling, and visualization. Although it’s not ideal for AI, it still has plenty of AI libraries and packages. Haskell does have AI-centered libraries like HLearn, which includes machine learning algorithms. Python, the most popular and fastest-growing programming language, is an adaptable, versatile, and flexible language with readable syntax and a vast community.

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. 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.

By 1962, Lisp had progressed to the point where it could address artificial intelligence challenges. Think of AI as a super-smart computer program that can learn and solve problems independently. It’s like having a super-powered brain helping us in many ways, from making our lives easier to tackling big challenges like climate change and disease. For instance, Python is a safe bet for intelligent AI applications with frameworks like TensorFlow and PyTorch. However, for specialized systems with intense computational demands, consider alternatives like C++, Java, or Julia. The best part is that it evaluates code lazily, which means it only runs calculations when mandatory, boosting efficiency.

It also enables algorithm testing without the need to actually use the algorithms. The qualities that distinguish Python from other programming languages are interactivity, interpretability, modularity, dynamic typing, portability, and high-level programming. Lisp and Prolog are two of the oldest programming languages, and they were specifically designed for AI development. Lisp is known for its symbolic processing ability, which is crucial in AI for handling symbolic information effectively. It also supports procedural, functional, and object-oriented programming paradigms, making it highly flexible. Prolog, on the other hand, is a logic programming language that is ideal for solving complex AI problems.

Because of these, many programmers consider Python ideal both for those new to AI and ML and seasoned experts. 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.

The crux is that newer or more niche languages suffer from a lack of public code examples. For example, if you’re working on a Python project, you’ll probably get better suggestions than with Fortran, as this features much less on GitHub (no disrespect to Fortran; it’s an OG language!). When learning how to use Copilot, you have the option of writing code to get suggestions or writing natural language comments that describe what you’d like your code to do. There’s even a Chat beta feature that allows you to interact directly with Copilot.

What do the best languages for AI development have in common?

It has the capability of processing symbolic information effectively. It is also known for its excellent prototyping capabilities and easy dynamic creation of new objects, with automatic garbage collection. Its development cycle allows interactive evaluation of expressions and recompilation of functions or files while the program is still running.

• Lisp is a powerful functional programming language notable for rule-based AI applications and logical reasoning.
• In this article, we will explore the best programming languages for AI in 2024.
• Also, we have Pybrain, which is for using machine learning in Python.
• Simform’s AI/ML services help you build customized AI solutions based on your use case.
• Its creators wanted to blend the mathematical power of MatLab, the statistical expertise of R, the dynamism of Ruby, the usability of Python, and the speed of C.
• R is another heavy hitter in the AI space, particularly for statistical analysis and data visualization, which are vital components of machine learning.

Now corporations are scrambling to not be left behind in the AI race, opening doors for newer programmers with a solid grasp of the fundamentals as well as knowledge of how to work with generative AI. Our career-change programs are designed to take you from beginner to pro in your tech career—with personalized support every step of the way. According to Payscale, the average salary for a Machine Learning Engineer with Python Skills was $112,178 as of 2022. Not only are AI-related jobs growing in leaps and bounds, but many technical jobs now request AI knowledge as well. Developers could experience a boost in productivity and job satisfaction thanks to AI's assistance. Exploring and developing new AI algorithms, models, and methodologies in academic and educational settings.

Code-generating tools trained on copyrighted code, meanwhile, have been caught regurgitating that code when prompted in a certain way, posing a liability risk to the developers using them. The McKinsey report also found that certain, more complex workloads — like those requiring familiarity with a specific https://chat.openai.com/ programming framework — didn’t necessarily benefit from AI. In fact, it took junior developers longer to finish some tasks with AI versus without, according to the report’s co-authors. Regarding features, the AI considers project-specifics like language and technology when generating code suggestions.

If you can create desktop apps in Python with the Tkinter GUI library, imagine what you can build with the help of machine learning libraries like NumPy and SciPy. Python comes with AI libraries and frameworks that allow beginners to focus on learning AI concepts without getting bogged down in complex syntax. It’s primarily designed to be a declarative programming language, which gives Prolog a set of advantages, in contrast to many other programming languages. A query over these relations is used to perform formulation or computation. Mojo was developed based on Python as its superset but with enhanced features of low-level systems. The main purpose of this best AI programming language is to get around Python's restrictions and issues as well as improve performance.

While you can write performant R code that can be deployed on production servers, it will almost certainly be easier to take that R prototype and recode it in Java or Python. AI (artificial intelligence) opens up a world of possibilities for application developers. You could even build applications that see, hear, and react to situations you never anticipated. In recent years, Artificial Intelligence has seen exponential growth and innovation in the field of technology.

Best AI Coding Assistants In 2024 [Free + Paid]

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. Julia is a relatively new player in the programming world, quickly gaining traction in the artificial intelligence (AI) and scientific computing communities. Launched in 2012, Julia was designed to address the need for a high-performance programming language that is also easy to use. Its creators wanted to blend the mathematical power of MatLab, the statistical expertise of R, the dynamism of Ruby, the usability of Python, and the speed of C. Java is a popular choice for complex AI projects due to its wide use in enterprise environments and scalability.

Like Prolog, Lisp is one of the earliest programming languages, created specifically for AI development. It’s highly flexible and efficient for specific AI tasks such as pattern recognition, machine learning, and NLP. Lisp is not widely used in modern AI applications, largely due to its cryptic syntax and lack of widespread support.

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. Developed by Apple and the open-source community, Swift was released in 2014 to replace Objective-C, with many modern languages as inspiration. Created for statistics, R is used widely in academia, data analysis, and data mining. C++ is a fast and efficient language widely used in game development, robotics, and other resource-constrained applications.

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. It’s an open-source tool that can process data, automatically apply it however you want, report patterns and changes, help with predictions, and more. Developed in the 1960s, Lisp is the oldest programming language for AI development. You can foun additiona information about ai customer service and artificial intelligence and NLP. It’s very smart and adaptable, especially good for solving problems, writing code that modifies itself, creating dynamic objects, and rapid prototyping. One key feature is its compatibility across platforms, so you don’t have to rewrite code every time you use a different system.

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. In terms of features, Ghostwriter offers real-time code suggestions in more than 16 languages, although it performs best with popular languages like JavaScript and Python.

CodeGPT is a set of AI-based solutions designed for maximum customization, meeting the highest security standards with its self-hosted solution. CodeGPT features an AI assistant creator (or GPTs), an Agent Marketplace, a Copilot for software engineers, and an API for advanced solutions. AI Assistants are designed to be omnipresent in your development ecosystem.

As a programming language for AI, Rust isn’t as popular as those mentioned above. Therefore, you can’t expect the Python-level of the resources volume. 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. Its interoperability makes it an excellent tool for implementing machine learning algorithms and applying them to real-world problems. AI programming languages play a crucial role in the development of AI applications.

Its syntax can be quite different from other languages, requiring a shift in thinking and a steeper learning curve for beginners. Imagine trying to read a poem in a language you’re not familiar with – it might take some extra effort to understand the beauty and meaning within. Haskell, renowned for its functional programming approach, offers a distinct advantage Chat GPT when writing concise and robust AI code. This approach emphasizes pure functions—functions where the output value is determined only by its input values, without observable side effects. This leads to easier code to test, debug, and reason about, which is particularly beneficial in the complex domain of AI, where algorithms must be reliable and efficient.

Big data applications like facial recognition systems are also powered by AI in Java. The language is also used to build intelligent chatbots that can converse with consumers in a human-like way. Julia is a newer language that has been gaining traction in the AI community. It’s designed to combine the performance of C with the ease and simplicity of Python.

This flexible, versatile programming language is relatively simple to learn, allowing you to create complex applications, which is why many developers start with this language. It also has an extensive community, including a substantial one devoted to using Python for AI. People often praise Scala for its combination of object-oriented and functional programming. This mix allows for writing code that’s both powerful and concise, which is ideal for large AI projects. Scala's features help create AI algorithms that are short and testable.

Lisp's fundamental building blocks are symbols, symbolic expressions, and computing with them. Therefore, Common Lisp (and other Lisp dialects) are excellent for symbolic AI. Java is a versatile and powerful programming language that enables developers to create robust, high-performance applications. Scala is a user-friendly and dependable language with a large community but can still be complex to learn.

Python is considered to be in first place in the list of all AI development languages due to its simplicity. The syntaxes belonging to Python are very simple and can be easily learned. Python takes a short development time in comparison to other languages like Java, C++, or Ruby. Python supports object-oriented, functional as well as procedure-oriented styles of programming. There are plenty of libraries in Python, which make our tasks easier. This post provides insights into the most effective languages for creating advanced artificial intelligence systems.

• One important point about these tools is that many AI coding assistants are trained on other people’s code.
• Still others you only need to know about if you’re interested in historical deep learning architectures and applications.
• AI is written in Python, though project needs will determine which language you’ll use.
• However, R may not be as versatile as Python or Java when it comes to building complex AI systems.

Numerous methods are available for controlling robots and automating jobs in robotics libraries like roscpp (C++ implementation of ROS). Fast runtimes and swifter execution are crucial features when building AI granted to Java users by the distinguishing characteristics of this best AI language. Additionally, it offers amazing production value and smooth integration of important analytical frameworks.

And while it’s lesser known, it still offers the main features you’d expect. You also get contextual code suggestions that aim to match the unique characteristics of your codebase’s style. And, if you have an Enterprise plan, you can use Tabnine Chat for a ChatGPT-like experience for code generation documentation, refactoring, and testing. Regarding privacy, the professional version doesn't use or store content to train its AI model, while the individual version might use user content, such as code snippets, to enhance suggestions.