The 20 Best Programming Languages to Learn in 2024

Java and JavaScript are some of the most widely used and multipurpose programming languages out there. Most websites are created using these languages, so using them in machine learning makes the integration process much simpler. 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. If you go delving in the history of deep learning models, you’ll often find copious references to Torch and plenty of Lua source code in old GitHub repositories.

While related, each of these terms has its own distinct meaning, and they’re more than just buzzwords used to describe self-driving cars. One compelling reason to dive into JavaScript is its vast ecosystem and community support. With JavaScript frameworks and libraries like React, Angular, and Vue.js, developers can rapidly prototype and deploy complex applications and JavaScript projects. This post provides insights into the most effective languages for creating advanced artificial intelligence systems. 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.

These provide a high level of abstraction and tend to offer less direct hardware control. One upside, however, is that these often include features like automatic memory management, dynamic typing, and type-checking. When it comes to statistical computing, data analysis, and data visualizations in 2024, you’ll probably find yourself deciding between Python or R. You should also know that Ruby’s versatility extends beyond web development, finding applications in data processing, prototyping, and automation scripts, among other tasks. When combined with Kotlin’s expressive syntax and safety features, it’s fair to say that Kotlin is a forward-looking language that’s trying to align well with the future of software development.

What are the key factors to consider when choosing a programming language for AI?

Python is often recommended as the best programming language for AI due to its simplicity and flexibility. It has a syntax that is easy to learn and use, making it ideal for beginners. Python also has a wide range of libraries that are specifically designed for AI and machine learning, such as TensorFlow and Keras. These libraries provide pre-written code that can be used to create neural networks, machine learning models, and other AI components. Python is also highly scalable and can handle large amounts of data, which is crucial in AI development. JavaScript is one of the most popular programming languages and is also used for artificial intelligence (AI) development.

Each language has its unique features and capabilities that make it suitable for different AI applications, such as NLP, computer vision, and robotics. As such, choosing the best programming languages for AI will be entirely dependent upon the specific software development that the AI engineers are undertaking. Prolog is a logic programming language that plays a significant role in artificial intelligence. Its declarative nature and use of logical inference make it well-suited for developing AI applications such as expert systems, natural language processing, and robotic control.

Synaptic.js is another neural network library that focuses on modular and efficient neural network design. AI is a broad field encompassing a range of technologies, including machine learning, natural language processing, computer vision, and robotics. 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. One example of a tool that uses C++ for AI-focused applications is the library OpenCV.

And if you want to develop iOS apps in 2024, you need to learn Swift via an iOS development course. Learning Swift in 2024 is essential if you want to develop cutting-edge mobile and desktop applications in the Apple ecosystem. This is particularly invaluable in DevOps practices, where the integration and automation of development and operations processes are paramount.

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. 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.

Advanced algorithms optimized for rapid data processing make its high-speed performance possible. WordPress developers might find CodeWP.ai a helpful way to create and store code snippets to boost their sites, but it’s not built into your site like Divi AI is. SQLAI is great for those new to SQL who want to chat with their databases to mine the data within. It’s already creating massive efficiencies for individual developers and teams across tech stacks and programming languages.

Java has been used in several successful AI projects, such as the Weka machine learning library and the Stanford Natural Language Processing (NLP) library. Weka is a popular ML library that provides a wide range best programming languages for ai of algorithms for data mining and predictive modeling. The Stanford NLP library is a suite of tools for natural language processing that includes parsers, part-of-speech taggers, and named entity recognizers.

This provides access to an extensive array of libraries and frameworks, such as ASP.NET for web development, Xamarin for mobile app development, and Entity Framework for data access. That said, it’s also important to point out that C# is the language of choice for the Unity game engine, making it a bonafide language for professional game developers. Of course, we won’t get into the Unity vs Unreal debate here, but still, this is quite the feather in its cap.

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. Developed by Apple and the open-source community, Swift was released in 2014 to replace Objective-C, with many modern languages as inspiration. Lisp is difficult to read and has a smaller community of users, leading to fewer packages. A flexible and symbolic language, learning Lisp can help in understanding the foundations of AI, a skill that is sure to be of great value for AI programming. 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.

It’s excellent for tasks involving complex logic and rule-based systems due to its declarative nature and the fact that it operates on the principle of symbolic representation. However, Prolog is not well-suited for tasks outside its specific use cases and is less commonly used than the languages listed above. 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++.

With the scale of big data and the iterative nature of training AI, C++ can be a fantastic tool in speeding things up. 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. Swift, the programming language developed by Apple, can be used for AI programming, particularly in the context of Apple devices.

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. As for deploying models, the advent of microservice architectures and technologies such as Seldon Core mean that it’s very easy to deploy Python models in production these days. With frameworks like React Native, JavaScript aids in building AI-driven interfaces across the web, Android, and iOS from a single codebase.

Lisp is a programming language that has been around since the late 1950s. Its name stands for «list processing», which reflects its unique feature of treating code as data. This ability to manipulate code as easily as data makes Lisp a popular choice for artificial intelligence (AI) programming. Python’s popularity and versatility have made it the programming language of choice for many AI developers. Its simplicity, extensive library ecosystem, and use in successful AI projects make it an excellent choice for anyone interested in AI development.

R stands out for its ability to handle complex statistical analysis tasks with ease. It provides a vast ecosystem of libraries and packages tailored specifically for statistical modeling, hypothesis testing, regression analysis, and data exploration. These capabilities enable AI professionals to extract meaningful insights from large datasets, identify patterns, and make accurate predictions.

WPCode is a great AI coding assistant for beginners and professional developers alike. It provides an easy way to add code snippets without having to dig down into the weeds to add them manually. Its easy plug-and-play design is attractive for people who understand code but need more skills to implement it in core WordPress theme files without using a child theme. SinCode offers a free plan with limited access to basic features, such as Marve (GPT 3.5) and limited image generation. Word credits can be purchased for $4.50 per 3,000 words, including 10 images, GPT-4, GPT 3.5 Turbo, and Marve Chat. The Starter plan for $20 monthly provides 50,000 words, 50 generated images, support for over 30 languages, and one brand voice.

It’s designed to be gradually adopted, allowing developers to start benefiting from its features with minimal disruption. Now, depending on your point of view, this is either amazing or very irritating! But, hear me out, yes it can be nice to work with dynamically typed languages, but this addition brings a new level of reliability and maintainability to large-scale applications.

What is Lisp used for in AI?

As Porter notes, «We believe LLMs lower the barrier for understanding how to program [2].» Although the execution isn’t flawless, AI-assisted coding eliminates human-generated syntax errors like missed commas and brackets. Porter believes that the future of coding will be a combination of AI and human interaction, as AI will allow humans to focus on the high-level coding skills needed for successful AI programming. You also need frameworks and code editors to design algorithms and create computer models. Many Python libraries were designed to classify and analyze large data sets, which makes it a valuable language in both AI and machine learning.

• Many programmers also choose to learn Python as it’s fundamental for the industry and is required for finding a job.
• Python is a general-purpose, object-oriented programming language that has always been a favorite among programmers.
• This platform can rapidly generate valid code for tasks such as creating custom post types, developing plugins, and extending the core function of your favorite WordPress products.

It has grown into a complete Google Tag Manager replacement and has added the ability to generate WordPress-specific code snippets and store them across websites. Github Copilot is a great tool that allows developers to increase their productivity, improve code quality, and provide excellent collaboration opportunities when working with a team. During testing, Copilot successfully completed the code, suggested alternate snippets, and saved us a ton of time.

For example, a Machine Learning Engineer might create an algorithm that the computer uses to recognize patterns within data and then decide what the next part of the pattern should be. In last year’s version of this article, I mentioned that Swift was a language to keep an eye on. A fully-typed, cruft-free binding of the latest and greatest features of TensorFlow, and dark magic that allows you to import Python libraries as if you were using Python in the first place. As we head into 2020, the issue of Python 2.x versus Python 3.x is becoming moot as almost every major library supports Python 3.x and is dropping Python 2.x support as soon as they possibly can. In other words, you can finally take advantage of all the new language features in earnest.

For most of its history, AI research has been divided into subfields that often fail to communicate with each other.

In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page. When you purchase the course, you’ll have access to all course materials, including videos, activities, readings, and graded assessments. It makes sense, then, that developing a strong understanding of how to use the technology could give you a competitive edge in a variety of industries.

Build AI skills on Coursera

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. Python is an interpreted, high-level, general-purpose programming language with dynamic semantics. In just 6 hours, you’ll gain foundational knowledge about AI terminology, strategy, and the workflow of machine learning projects. Taia is recommended for legal professionals and financial institutions who want to combine AI translation with human translators to ensure accuracy. It specializes in legal and financial document translation, offers advanced language processing capabilities, and ensures compliance with industry regulations.

Finally, the Advanced plan provides a whopping 300,000 GPT-4 tokens, 2 million 3.5 tokens, customizable data dashboards, and connections to outside data sources for $19 monthly. In addition to creating SQL queries, SQLAI explains and optimizes them, so you can rest assured your queries will work as intended. It also supports several OpenAI models, such as GPT-4, and uses a built-in version of the VS Code editor, so if you’re a fan of VS Code, you’ll feel right at home. By leveraging Sourcegraph’s code graph and LLM, Cody provides context-aware answers, whether you’re locating a piece of code, creating new functions, or debugging.

It’s also very helpful that Dart has the ability to compile to both ARM and x86 native code, offering high performance on mobile devices, as well as transpiling to JavaScript for web applications. Rails also accelerates web application development by providing default structures for databases, web services, and web pages, along with a wealth of libraries (gems) that extend its functionality. The Ruby ecosystem is also renowned for its robust web development framework, Ruby on Rails (Rails), which popularized the convention over configuration (CoC) paradigm and the don’t repeat yourself (DRY) principle. One of Ruby’s hallmarks is its expressive syntax that allows developers to do more with less code, enhancing readability and maintainability. That said, Ruby is still a very useful and popular language in 2024, and it’s still widely celebrated for its elegance, simplicity, and the principle of developer happiness.

As AI becomes increasingly embedded in modern technology, the roles of developers — and the skills needed to succeed in this field — will continue to evolve. From Python and R to Prolog and Lisp, these languages have proven critical in developing artificial intelligence and will continue to play a key role in the future. 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. Without these, the incredible algorithms and intricate networks that fuel AI would be nothing more than theoretical concepts.

This extensive library ecosystem has made Python the go-to language for AI programmers. To sum up, five of the top programming languages for AI development are Python, R, Java, C++, and Julia, with each language offering unique advantages for building AI applications. This is just the tip of the iceberg, as there are many languages commonly used in AI programming which you may like to explore.

Software using it follow a basic set of facts, rules, goals, and queries instead of sequences of coded instructions. Despite its flaws, Lisp is still in use and worth looking into for what it can offer your AI projects. Coding will remain an in-demand skill—both in AI and traditional settings—for years to come. Build your coding skills with online courses like Python for Data Science, AI, & Development from IBM or Princeton University’s Algorithms, Part 1, which will help you gain experience with Java.

AI is written in Python, though project needs will determine which language you’ll use. You can foun additiona information about ai customer service and artificial intelligence and NLP. Currently, Python is the most popular coding language in AI programming because of its prevalence in general programming projects, its ease of learning, and its vast number of libraries and frameworks. Scala is a user-friendly and dependable language with a large community but can still be complex to learn. It’s used for advanced development such as data processing and distributed computing. In this best language for artificial intelligence, sophisticated data description techniques based on associative arrays and extendable semantics are combined with straightforward procedural syntax.

With a clean and expressive syntax, Swift places a strong emphasis on safety and performance. As the preferred language for developing iOS, macOS, watchOS, and tvOS applications, Swift opens the door to the vast and lucrative world of Apple products and services. Overall, TypeScript’s compatibility with https://chat.openai.com/ JavaScript libraries and frameworks, along with its support from major development environments, ensures a smooth transition and a productive development experience. Plus, TypeScript’s seamless integration with JavaScript means that adopting it doesn’t require a complete overhaul of existing projects.

Few codebases and integrations are available for C++ because developers don’t use C++ as frequently as Python for AI development. If you already know Java, you may find it easier to program AI in Java than learn a new language. Technically, you can use any language for AI programming — some just make it easier than others. The first version of Julia was officially introduced to the programming space in 2018 and has steadily been gaining popularity ever since. According to HPCwire, the number of downloads for the language grew by 87 percent from 2020 to 2021, and the number of available packages for the language grew by 73 percent. It’s no surprise, then, that programs such as the CareerFoundry Full-Stack Web Development Program are so popular.

Julia is another high-end product that just hasn’t achieved the status or community support it deserves. This programming language is useful for general tasks but works best with numbers and data analysis. Here’s another programming language winning over AI programmers with its flexibility, ease of use, and ample support.

How quickly can I learn machine learning?‎

The programming languages may be the same or similar for both environments; however, the purpose of programming for AI differs from traditional coding. With AI, programmers code to create tools and programs that can use data to “learn” and make helpful decisions or develop practical solutions to challenges. In traditional coding, programmers use programming languages to instruct computers and other devices to perform actions. Other popular AI programming languages include Julia, Haskell, Lisp, R, JavaScript, C++, Prolog, and Scala. The language supports parallelism, a type of computing where many different processes are carried out simultaneously. This is an important concept for machine learning and AI-focused applications, meaning that Julia could continue to grow in importance throughout the field.

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

Gemma is a family of open-source language models from Google that were trained on the same resources as Gemini. Gemma comes in two sizes — a 2 billion parameter model and a 7 billion parameter model. Gemma models can be run locally on a personal computer, and surpass similarly sized Llama 2 models on several evaluated benchmarks. Gemini is Google’s family of LLMs that power the company’s chatbot of the same name. The model replaced Palm in powering the chatbot, which was rebranded from Bard to Gemini upon the model switch. Gemini models are multimodal, meaning they can handle images, audio and video as well as text.

StableLM is a series of open source language models developed by Stability AI, the company behind image generator Stable Diffusion. There are 3 billion and 7 billion parameter models available and 15 billion, 30 billion, 65 billion and 175 billion parameter models in progress at time of writing. BERT is a transformer-based model that can convert sequences of data to other sequences of data. BERT’s architecture is a stack of transformer encoders and features 342 million parameters. BERT was pre-trained on a large corpus of data then fine-tuned to perform specific tasks along with natural language inference and sentence text similarity.

We hope this article helped you to find out more about the best programming languages for AI development and revealed more options to choose from. 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 AI-powered applications. Lua can run cross-platform and supports different programming paradigms including procedural, object-oriented, functional, data-driven, and data description.

However, if you want to work in areas such as autonomous cars or robotics, learning C++ would be more beneficial since the efficiency and speed of this language make it well-suited for these uses. If you’re just learning to program for AI now, there are many advantages to beginning with Python. Not only are AI-related jobs growing in leaps and bounds, but many technical jobs now request AI Chat GPT knowledge as well. Bring your unique software vision to life with Flatirons’ custom software development services, offering tailored solutions that fit your specific business requirements. Exploring and developing new AI algorithms, models, and methodologies in academic and educational settings. Processing and analyzing text data, enabling language understanding and sentiment analysis.

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. R was created specifically for data analysis, software application development, and the creation of data mining tools, in contrast to Python. AI initiatives involving natural language processing e.g. text classification, sentiment analysis, and machine translation, can also utilize C++ as one of the best artificial intelligence languages.

Feature Comparison of the Best AI Coding Assistants

Overall, learning Rust in 2024 can position you at the forefront of a movement toward safer, more reliable systems programming. But unlike these older languages, Rust provides a higher level of abstraction and guarantees safety, significantly reducing the risk of security vulnerabilities and runtime errors. Rust also places emphasis on zero-cost abstractions, iterator chains, pattern matching, and type inference which not only promotes safer code but also cleaner and more expressive syntax.

• Although the bot is still in the developmental stage, it’s already proven an excellent tool for developers of all skill levels.
• 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.
• So whether you need to write a plugin for WordPress or generate copy for your next blog post, SinCode has you covered.
• Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow are a few of the specialist libraries available in Python, making it an excellent choice for AI projects.

Leverage generative AI tools to speed up work tasks and boost your productivity. Examine the important role humans play in the effective use of AI, and understand the types of workplace tasks you can augment with AI. By the end of this module, you will be able to determine if AI is right for a given task and how to use AI to accelerate workflows. The major ranking changes this month are C++’s month-over-month change from 9.53% to 10.03% and C’s month-over-month change from 9.98% to 9.23%. The programming language Go increased in popularity to position seven, doubling its rank (14) from this time last year.

The tool guarantees timely and accurate translations, boasting an impressive client satisfaction rate of 99.4%. Additionally, it provides long-term project support for clients requiring multiple translations. Sonix is a web-based platform that uses AI to convert audio and video content into text. Afterward, it uses advanced machine translation to deliver precise, accurate translations of that text in over 40 languages. It streamlines the entire workflow, saving you time and effort while maintaining impeccable quality. Whether transcribing interviews, translating lectures, or creating multilingual subtitles, it becomes your go-to solution.

Developers, this isn’t your go-to tool but is likely helpful for others who need a range of AI options within reach. Android Studio Bot is the best AI coding assistant for those creating Android apps and wanting to boost their productivity. The platform generates code, finds relevant resources, teaches best practices, and saves time. Although the bot is still in the developmental stage, it’s already proven an excellent tool for developers of all skill levels.

With Python’s usability and C’s performance, Mojo combines the features of both languages to provide more capabilities for AI. For example, Python cannot be utilized for heavy workloads or edge devices due to its lower scalability while other languages, like C++, have the scalability feature. Therefore, till now both languages had to be used in combination for the seamless implementation of AI in the production environment. Now Mojo can replace both languages for AI in such situations as it is designed specifically to solve issues like that. Okay, here’s where C++ can shine, as most games use C++ for AI development.

NLP algorithms are provided by C++ libraries like NLTK, which can be used in AI projects. Sonix sits second on our list as it distinguishes itself with its lightning-fast translation capabilities. Speech recognition technology can transcribe and translate audio files or live conversations in real-time, significantly reducing the time required for language processing tasks.

Its standout feature is the two-step process that ensures maximum accuracy. First, it uses state-of-the-art AI to transcribe audio or video into text. You can then review and edit this text transcript for discrepancies before it’s fed into the translation engine. This human-in-the-loop approach guarantees the most precise translations possible, making this tool ideal for professional settings or when nuance is crucial.

Many AI-focused applications are relatively complex, so using an efficient programming language like C++ can help create programs that run exceptionally well. 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.

The community agrees that Copy.ai has a user-friendly interface and can work as an AI translator. Copy.ai is chosen because it excels in translating and generating creative text formats. While it can translate languages, its true strength lies in adapting translated content into different writing styles, like marketing copy, social media posts, or website content. Sonix doesn’t offer a free version, and its paid plans start at $22 per user per month.

DeepL is best for professional translators who require high accuracy or users dealing with complex language. It is known for superior translation quality, particularly for European languages. Imagine engaging in a fluent dialogue with someone who communicates in a distinct language from your own. With this tool, you can speak or type in your language, and the AI will translate it for the other person and vice versa.

Julia’s AI ecosystem is growing, but isn’t quite as big as some of the options available for other major programming languages. The Flux website lists some of the capabilities and tools available in the library that can be applied to AI projects, including computer vision tools, reinforcement learning tools and more. Many general-purpose programming languages can be used in a variety of situations, including AI applications. If you’re interested in learning more about developing machine learning and artificial intelligence applications, you’ve come to the right place. When it comes to AI-related tasks, Python shines in diverse fields such as machine learning, deep learning, natural language processing, and computer vision. Its straightforward syntax and vast library of pre-built functions enable developers to implement complex AI algorithms with relative ease.

In recent years, especially after last year’s ChatGPT chatbot breakthrough, AI creation secured a pivotal position in overall global tech development. Such a change in the industry has created an ever-increasing demand for qualified AI programmers with excellent skills in required AI languages. Undoubtedly, the knowledge of top programming languages for AI brings developers many job opportunities and opens new routes for professional growth. While it’s possible to specialize in one programming language for AI, learning multiple languages can broaden your perspective and make you a more versatile developer. 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.