The Best AI Programming Languages to Learn in 2024

That said, it’s also a high-performing and widely used programming language, capable of complicated processes for all kinds of tasks and platforms. Python is the most popular language for AI because it’s easy to understand and has lots of helpful tools. You can easily work with data and make cool graphs with libraries like NumPy and Pandas.

Over the years, due to advancement, many of these features have migrated into many other languages thereby affecting the uniqueness of Lisp. The language has more than 6,000 built-in functions for symbolic computation, functional programming, and rule-based programming. Developers use this language for most development platforms because it has a customized virtual machine. This post lists the ten best programming languages for AI development in 2022.

Python also has a large supportive community, with many users, collaborators and fans. Hiren is CTO at Simform with an extensive experience in helping enterprises and startups streamline their business performance through data-driven innovation. Its ability to rewrite its own code also makes Lisp adaptable for automated programming applications. 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. Scala took the Java Virtual Machine (JVM) environment and developed a better solution for programming intelligent software.

MATLAB is particularly useful for prototyping and algorithm development, but it may not be the best choice for deploying AI applications in production. Lisp (also introduced by John McCarthy in 1958) is a family of programming languages with a long history and a distinctive, parenthesis-based syntax. Today, Lisp is used in a variety of applications, including scripting and system administration. 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.

Lisp’s syntax is unusual compared to modern computer languages, making it harder to interpret. Relevant libraries are also limited, not to mention programmers to advise you. Programming languages are notoriously versatile, each capable of great feats in the right hands. AI (artificial intelligence) technology also relies on them to function properly when monitoring a system, triggering commands, displaying content, and so on. Python’s versatility, easy-to-understand code, and cross-platform compatibility all contribute to its status as the top choice for beginners in AI programming. Plus, there are tons of people who use Python for AI, so you can find answers to your questions online.

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. When choosing a programming language for AI, there are several key factors to consider.

Plus, since Scala works with the Java Virtual Machine (JVM), it can interact with Java. This compatibility gives you access to many libraries and frameworks in the Java world. While learning C++ can be more challenging than other languages, its power and flexibility make up for it. This makes C++ a worthy tool for developers working on AI applications where performance is critical.

AI coding assistants are also a subset of the broader category of AI development tools, which might include tools that specialize in testing and documentation. For this article, we’ll be focusing on AI assistants that cover a wider range of activities. These AI coding tools aim to enhance the productivity and efficiency of developers, providing assistance in various aspects of the coding process. Ian Pointer is a senior big data and deep learning architect, working with Apache Spark and PyTorch.

While Lisp isn’t as popular as it once was, it continues to be relevant, particularly in specialized fields like research and academia. Its skill in managing symbolic reasoning tasks keeps it in use for AI projects where this skill is needed. Each programming language has unique features that affect how easy it is to develop AI and how well the AI performs.

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Thirdly, the language should be scalable and efficient in handling large amounts of data. Lastly, it’s beneficial if the language is easy to learn and use, especially if you’re a beginner. Prolog (general core, modules) is a logic programming language from the early ’70s that’s particularly well suited for artificial intelligence applications. Its declarative nature makes it easy to express complex relationships between data. Prolog is also used for natural language processing and knowledge representation. If you’re interested in pursuing a career in artificial intelligence (AI), you’ll need to know how to code.

C++ is generally used for robotics and embedded systems, On the other hand Python is used for traning models and performing high-level tasks. Because of its capacity to execute challenging mathematical operations and lengthy natural language processing functions, Wolfram is popular as a computer algebraic language. R is a popular language for AI among both aspiring and experienced statisticians.

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. Which programming language should you learn to plumb the depths of AI? You’ll want a language with many good machine learning and deep learning libraries, of course. 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.

Alison: Prompt Engineering for AI Applications

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. If you want pure functionality above all else, Haskell is a good programming language to learn. Getting the hang of it for AI development can take a while, due in part to limited support. I do my best to create qualified and useful content to help our website visitors to understand more about software development, modern IT tendencies and practices.

Plus, the general democratization of AI will mean that programmers will benefit from staying at the forefront of emerging technologies like AI coding assistants as they try to remain competitive. 2024 continues to be the year of AI, with 77% of developers in favor of AI tools and around 44% already using AI tools in their daily routines. And as you progress beyond that and become a programmer in your own right, AI coding assistants can speed up your workflow. ChatGPT is a good all-around AI coding assistant that can help you not just with your actual code but with deciding what to learn, applying for jobs, etc. Another fan favorite among real coders, Aider is a ChatGPT-powered coding tool that lives in your terminal. Cursor is an AI-powered code editor where you can ask questions about your code if you run into an error and it makes it easy to find solutions.

It’s designed for numerical computing and has simple syntax, yet it’s powerful and flexible. R has many packages designed for data work, statistics, and visualization, which is great for AI projects focused on data analysis. Important packages like ggplot2 for visualization and caret for machine learning gives you the tools to get valuable insights from data. Scala thus combines advanced language capabilities for productivity with access to an extensive technology stack.

Its straightforward syntax and vast library of pre-built functions enable developers to implement complex AI algorithms with relative ease. AI Assistants are advanced tools that use artificial intelligence to help developers write code, debug issues, and optimize their workflow across various programming languages and tasks. The JVM family of languages (Java, Scala, Kotlin, Clojure, etc.) continues to be a great choice for AI application development. Plus you get easy access to big data platforms like Apache Spark and Apache Hadoop.

It will also examine the differences between traditional coding and coding for AI and how AI is changing programming. Likewise, AI jobs are steadily increasing, with in-demand roles like machine learning engineers, data scientists, and software engineers often requiring familiarity with the technology. A programming language well-suited for AI should have strong support for mathematical and statistical operations, as well as be able to handle large datasets and complex algorithms effectively. R’s strong community support and extensive documentation make it an ideal choice for researchers and students in academia.

For instance, DeepLearning4j supports neural network architectures on the JVM. The Weka machine learning library collects classification, regression, and clustering algorithms, while Mallet offers natural language processing capabilities for AI systems. Java is used in AI systems that need to integrate with existing business systems and runtimes.

Programs that focus on AI for code generation are often able to complete your code or write new lines for you to eliminate busywork. To that end, it may be useful to have a working knowledge of the Torch API, which is not too far removed from PyTorch’s basic API. However, if, like most of us, you really don’t need to do a lot of historical research for your applications, you can probably get by without having to wrap our head around Lua’s little quirks.

Selecting the appropriate programming language based on the specific requirements of an AI project is essential for its success. Different programming languages offer different capabilities and libraries that cater to specific AI tasks and challenges. Another popular AI assistant that’s been around for a while is Tabnine. However, other programmers often find R a little confusing, due to its dataframe-centric approach.

Over 2,500 companies and 40% of developers worldwide use HackerRank to hire tech talent and sharpen their skills. 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. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. It’s essentially the process of making a computer system that can learn and work on its own.

Moreover, it complements Python well, allowing for research prototyping and performant deployment. Advancements like OpenAI’s Dall-E generating images from text prompts and DeepMind using https://chat.openai.com/ AI for protein structure prediction show the technology’s incredible potential. Natural language processing breakthroughs are even enabling more intelligent chatbots and search engines.

Frameworks like TensorFlow.js offer user-friendly tools and tutorials, making it easier to jump into web-based AI even if you’re new to coding. Its syntax can differ slightly, and mastering its statistical tools takes practice. Your choice affects your experience, the journey’s ease, and the project’s success. Its low-level memory manipulation lets you tune AI algorithms and applications for optimal performance.

Python: The Powerhouse of AI

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. Scala was designed to address some of the complaints encountered when using Java.

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. Python is a general-purpose, object-oriented programming language that has always been a favorite among programmers.

The early AI pioneers used languages like LISP (List Processing) and Prolog, which were specifically designed for symbolic reasoning and knowledge representation. The programming language Haskell is becoming more and more well-liked in the AI community due to its capacity to manage massive development tasks. Haskell is a great option for creating sophisticated AI algorithms because of its type system and support for parallelism.

So, while there’s no denying the utility and usefulness of these AI tools, it helps to bear this in mind when using AI coding assistants as part of your development workflow. One important point about these tools is that many AI coding assistants are trained on other people’s code. You can always try a free AI coding assistant or sign up for a free trial to see how AI coding tools can plug into your own journey as a programmer. See how it goes, keep a flexible mindset, and you might just find the best AI code generator for you.

Codeium is probably the best AI code generator that’s accessible for free. It predicts entire lines or blocks of code based on the context of what you’re writing. It can see all the code in your project, so it knows (for example) if you’re using React components or TypeScript, etc.

R’s main drawback is that it’s not as versatile as Python and can be challenging to integrate with web applications. Python is often the first language that comes to mind when talking about AI. Its simplicity and readability make it a favorite among beginners and experts alike. Python provides an array of libraries like TensorFlow, Keras, and PyTorch that are instrumental for AI development, especially in areas such as machine learning and deep learning. While Python is not the fastest language, its efficiency lies in its simplicity which often leads to faster development time. However, for scenarios where processing speed is critical, Python may not be the best choice.

That said, the math and stats libraries available in Python are pretty much unparalleled in other languages. NumPy has become so ubiquitous it is almost a standard API for tensor operations, and Pandas brings R’s powerful and flexible dataframes to Python. For natural language processing (NLP), you have the venerable NLTK and the blazingly-fast SpaCy. And when it comes to deep learning, all of the current libraries (TensorFlow, PyTorch, Chainer, Apache MXNet, Theano, etc.) are effectively Python-first projects.

But GameNGen is one of the more impressive game-simulating attempts yet in terms of its performance. The model isn’t without big limitations, namely graphical glitches and an inability to “remember” more than three seconds of gameplay (meaning GameNGen can’t create a functional game, really). But it could be a step toward entirely new sorts of games — like procedurally generated games on steroids. One important note is that this approach means sending data to the LLM provider. And while JetBrains assures confidentiality, this may or may not work for your own data privacy requirements. One of the most interesting things about Copilot is that it’s been trained on public GitHub repositories.

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We should point out that we couldn’t find as much online documentation as we would have liked, so we cannot fully discuss the data privacy aspect of this tool. If this is important to you, it might be wise to contact their customer support for more detailed info. Codi is also multilingual, which means it also answers queries in languages like German and Spanish. But like any LLM, results depend on the clarity of your natural language statements. AskCodi is powered by the OpenAI Codex, which it has this in common with our #1 pick, GitHub Copilot.

This can be a double-edged sword, as shown by GitHub stats that indicate only 26% of Copilot’s suggestions were accepted. I guess the clue is in the name here, as it’s literally an AI tool with the sole purpose of assisting you with your dev duties. Whether or not you’re sold on using AI-assisted coding in your own work, it never hurts to have a new option in your arsenal. They can’t and shouldn’t give you all the answers—there are certain things you need to learn by practicing and on your own.

• Few codebases and integrations are available for C++ because developers don’t use C++ as frequently as Python for AI development.
• In function, it’s kind of like when Gmail suggests the rest of your sentence and you can accept it or not.
• The best part is that it evaluates code lazily, which means it only runs calculations when mandatory, boosting efficiency.
• And while JetBrains assures confidentiality, this may or may not work for your own data privacy requirements.

This article will provide you with a high-level overview of the best programming languages and platforms for AI, as well as their key features. To choose which AI programming language to learn, consider your current abilities, skills, and career aspirations. For example, if you’re new to coding, Python can offer an excellent starting point.

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 best coding languages for ai programming languages for AI. Lisp, with its long history intertwined with AI research, stands out as one of the best AI programming languages languages.

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. It is popular for full-stack development and AI features integration into website interactions. Smalltalk is a general-purpose object-oriented programming language, which means that it lacks the primitives and control structures found in procedural languages.

You can use libraries like DeepLogic that blend classic Prolog with differentiable components to integrate deep neural networks with symbolic strengths. 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.

In the years since, AI has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an “AI winter”), followed by new approaches, success and renewed funding. 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.

Compared to other best languages for AI mentioned above, Lua isn’t as popular and widely used. However, in the sector of artificial intelligence development, it serves a specific purpose. It is a powerful, effective, portable scripting language that is commonly appreciated for being highly embeddable which is why it is often used in industrial Chat GPT AI-powered applications. Lua can run cross-platform and supports different programming paradigms including procedural, object-oriented, functional, data-driven, and data description. From our previous article, you already know that, in the AI realm, Haskell is mainly used for writing ML algorithms but its capabilities don’t end there.

Looking to build a unique AI application using different programming languages? Simform’s AI/ML services help you build customized AI solutions based on your use case. In terms of AI capabilities, Julia is great for any machine learning project. Whether you want premade models, help with algorithms, or to play with probabilistic programming, a range of packages await, including MLJ.jl, Flux.jl, Turing.jl, and Metalhead. There’s more coding involved than Python, but Java’s overall results when dealing with artificial intelligence clearly make it one of the best programming languages for this technology.

By learning multiple languages, you can choose the best tool for each job. 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. Other popular AI programming languages include Julia, Haskell, Lisp, R, JavaScript, C++, Prolog, and Scala.

Alison offers a course designed for those new to generative AI and large language models. CodeGPT’s AI Assistants seamlessly integrate with popular IDEs and code editors, allowing you to access their capabilities directly within your preferred development environment. Access curated solutions and expert insights from the world’s largest developer community, enhancing your problem-solving efficiency.

If you’re starting with Python, it’s worth checking out the book The Python Apprentice, by Austin Bingham and Robert Smallshire, as well as other the Python books and courses on SitePoint. 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. 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. Java ranks second after Python as the best language for general-purpose and AI programming.

But for AI and machine learning applications, rapid development is often more important than raw performance. 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. 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.

In a separate study, companies said that excessive code maintenance (including addressing technical debt and fixing poorly performing code) costs them $85 billion per year in lost opportunities. This week in AI, two startups developing tools to generate and suggest code — Magic and Codeium — raised nearly half a billion dollars combined. The rounds were high even by AI sector standards, especially considering that Magic hasn’t launched a product or generated revenue yet. You can foun additiona information about ai customer service and artificial intelligence and NLP. In our opinion, AI will not replace programmers but will continue to be one of the most important technologies that developers will need to work in harmony with.

However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits. 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.

Its object-oriented side helps build complex, well-organized systems. This makes it easier to create AI applications that are scalable, easy to maintain, and efficient. Julia also has a wealth of libraries and frameworks for AI and machine learning.

We also like their use of Jupyter-style workbooks and projects to help with code organization. Python is the language at the forefront of AI research, the one you’ll find the most machine learning and deep learning frameworks for, and the one that almost everybody in the AI world speaks. For these reasons, Python is first among AI programming languages, despite the fact that your author curses the whitespace issues at least once a day. Rust provides performance, speed, security, and concurrency to software development. With expanded use in industry and massive systems, Rust has become one of most popular programming languages for AI.