11 of the Best AI Programming Languages: A Beginners Guide

Java has a steep yet quick learning curve, but it’s incredibly powerful with a simple syntax and ease of debugging. Okay, here’s where C++ can shine, as most games use C++ for AI development. That’s because it’s a fast language that can be used to code high-performance applications. However, there are also games that use other languages for AI development, such as Java.

But although Python seems friendly, it’s well-equipped to handle large and complex projects. And recent research suggests that the majority of artificial intelligence projects are market-oriented. Aside from the 2001 science fiction film with Haley Joel Osment, artificial intelligence is a complex and profound subject area.

It is widely used in various AI applications and offers powerful frameworks like TensorFlow and PyTorch. Java, on the other hand, is a versatile language with scalability and integration capabilities, making it a preferred choice in enterprise environments. JavaScript, the most popular language for web development, is also used in web-based AI applications, chatbots, and data visualization.

R Applications in AI

If you want to deploy an AI model into a low-latency production environment, C++ is your option. As a compiled language where developers control memory, C++ can execute machine learning programs quickly using very little memory. This makes it good for AI projects that need lots of processing power. 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.

In a business capacity, artificial intelligence has a number of benefits. Really, if you’ve ever worked with a digital device that didn’t know how to tell up from down or do a simple task, you’d probably quite like artificial intelligence. At its core, artificial intelligence (AI) refers to intelligent machines. If you think that artificial intelligence makes for some scary alternate realities, you’re not alone. Doing so will free human developers and programmers to focus on the high-level tasks and the creative side of their work.

But it remains uniquely suited to expert systems and decision-making logic dependent on symbolic reasoning rather than data models. 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. 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.

Swift has a high-performance deep learning AI library called Swift 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. Go was designed by Google and the open-source community to meet issues found in C++ while maintaining its efficiency.

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. Now that we’ve laid out what makes a programming language well-suited for AI, let’s explore the most important AI programming languages that you should keep on your radar. 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.

Node.js allows easy hosting and running of machine learning models using serverless architectures. 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.

Staying knowledgeable about cutting-edge AI programming languages allows developers to stay competitive and deliver innovative AI solutions. 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. 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.

Scala, a language that combines functional programming with object-oriented programming, offers a unique toolset for AI development. Its ability to handle complex data types and support for concurrent programming makes Scala an excellent choice for building robust, scalable AI systems. The language’s interoperability with Java means that it can leverage the vast ecosystem of Java libraries, including those related to AI and machine learning, such as Deeplearning4j. 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. R is another heavy hitter in the AI space, particularly for statistical analysis and data visualization, which are vital components of machine learning.

This is how the best tools create and orchestrate campaigns and gather insights to improve your effectiveness as a brand. And once you know how to develop artificial intelligence, you can do it all. It should be self-explanatory as to why these projects would appeal to a growing business such as yours. In marketing alone, employing artificial intelligence can make a grand difference. Coding will remain an in-demand skill—both in AI and traditional settings—for years to come.

Although Julia’s community is still small, it consistently ranks as one of the premier languages for artificial intelligence. According to IDC, the AI market will surpass $500 billion by 2024 with a five-year CAGR of 17.5 percent and total revenue of $554.3 billion. However, the first step towards creating efficient solutions is choosing the best programming languages for AI software. 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 Best AI Programming Languages to Learn in 2024

However, Java is a robust language that does provide better performance. If you already know Java, you may find it easier to program AI in Java than learn a new language. If you’re just learning to program for AI now, there are many advantages to beginning with Python. Technically, you can use any language for AI programming — some just make it easier than others.

The Weka machine learning library collects classification, regression, and clustering algorithms, while Mallet offers natural language processing capabilities for AI systems. 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.

Its declarative approach helps intuitively model rich logical constraints while supporting automation through logic programming. The language is syntactically identical to C++, but it provides memory safety without garbage collection and allows optional reference best coding languages for ai counting. This post lists the ten best programming languages for AI development in 2022. 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.

Modern versions keep Lisp’s foundations but add helpful automation like memory management. Julia is rapidly adopted for data science prototyping, with results then productionized in Python. Julia’s mathematical maturity and high performance suit the needs of engineers, scientists, and analysts.

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++. It has a steep learning curve and requires a solid understanding of computer science concepts. https://chat.openai.com/ JavaScript is currently the most popular programming language used worldwide (69.7%) by more than 16.4 million developers. While it may not be suitable for computationally intensive tasks, JavaScript is widely used in web-based AI applications, data visualization, chatbots, and natural language processing.

Created for statistics, R is used widely in academia, data analysis, and data mining. 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.

What are the best programming languages for AI development?

It is easy to learn, has a large community of developers, and has an extensive collection of frameworks, libraries, and codebases. You can foun additiona information about ai customer service and artificial intelligence and NLP. However, Python has some criticisms—it can be slow, and its loose syntax may teach programmers bad habits. There are many popular AI programming languages, including Python, Java, Julia, Haskell, and Lisp. A good AI programming language should be easy to learn, read, and deploy. 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.

Scala enables deploying machine learning into production at high performance. Its capabilities include real-time model serving and building streaming analytics pipelines. Plus, it has distributed data processing and robust feature engineering.

Popular in education research, Haskell is useful for Lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism. In addition, because of its versatility and capacity to manage failures, Haskell is considered a safe programming language for AI. Starting with Python is easy because codes are more legible, concise, and straightforward. Python also has a large supportive community, with many users, collaborators and fans.

Here are the most popular languages used in AI development, along with their key features. As it turns out, there’s only a small number of programming languages for AI that are commonly used. If you’re still asking yourself about the best language to choose from, the answer is that it comes down to the nature of your job. Many Machine Learning Engineers have several languages in their tech stacks to diversify their skillset.

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. These languages offer unique features and capabilities for different AI tasks, whether it’s machine learning, natural language processing, or data visualization. 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.

C++ is a powerful, high-performance language that is often used in AI for tasks that require intensive computations and precise control over memory management. However, C++ has a steeper learning curve compared to languages like Python and Java. Lisp (also introduced by John McCarthy in 1958) is a family of programming languages with a long history and a distinctive, parenthesis-based syntax.

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. It excels in pattern matching and automatic backtracking, which are essential in AI algorithms. 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.

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. Like Prolog, Lisp is one of the earliest programming languages, created specifically for AI development.

Julia

Add in memory management, debugging, and metaprogramming to the mix, and you’ll soon understand what all the hype’s about. You can chalk its innocent fame up to its dynamic interface and arresting graphics for data visualization. Yet, in practice, C++’s capacity for low-level programming makes it perfect for handling Chat PG AI models in production. Plus, Java’s object-oriented design makes the language that much easier to work with, and it’s sure to be of use in AI projects. So the infamous FaceApp in addition to the utilitarian Google Assistant both serve as examples of Android apps with artificial intelligence built-in through Java.

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. Before we delve into the specific languages that are integral to AI, it’s important to comprehend what makes a programming language suitable for working with AI.

But for AI and machine learning applications, rapid development is often more important than raw performance. Python, the most popular and fastest-growing programming language, is an adaptable, versatile, and flexible language with readable syntax and a vast community. ChatGPT has thrusted AI into the cultural spotlight, drawing fresh developers’ interest in learning AI programming languages. Java is a versatile and powerful programming language that enables developers to create robust, high-performance applications.

What AI programming languages should you learn?

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. 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.

• Java is used in AI systems that need to integrate with existing business systems and runtimes.
• This allows the computer to provide the resulting suggestions based on the patterns it identified.
• You can build neural networks from scratch using C++ and translate user code into something machines can understand.
• Developed by Apple and the open-source community, Swift was released in 2014 to replace Objective-C, with many modern languages as inspiration.

Even outside of mobile apps, Java has quite a few machine learning libraries for deep learning and natural language processing. 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.

Lisp (historically stylized as LISP) is one of the most widely used programming languages for AI. In addition, Python works best for natural language processing (NLP) and AI programs because of its rich text processing features, simple syntax, and scripting with a modular design. As new trends and technologies emerge, other languages may rise in importance. For developers and hiring managers alike, keeping abreast of these changes and continuously updating skills and knowledge are vital. Every language has its strengths and weaknesses, and the choice between them depends on the specifics of your AI project.

One of Julia’s best features is that it works nicely with existing Python and R code. This lets you interact with mature Python and R libraries and enjoy Julia’s strengths. Advancements like OpenAI’s Dall-E generating images from text prompts and DeepMind using AI for protein structure prediction show the technology’s incredible potential. Natural language processing breakthroughs are even enabling more intelligent chatbots and search engines. Join a network of the world’s best developers and get long-term remote software jobs with better compensation and career growth.

We’ll discuss key factors to pick the best AI programming language for your next project. The language has more than 6,000 built-in functions for symbolic computation, functional programming, and rule-based programming. In this post, we’re going to dive deep into the world of AI programming languages. We’ll break down which ones matter most, what makes them important, and how you can leverage them to your advantage. The best programming languages for artificial intelligence include Python, R, Javascript, and Java. In the rapidly evolving field of AI, developers need to keep up with the latest advancements and trends.

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. Data visualization is a crucial aspect of AI applications, enabling users to gain insights and make informed decisions. JavaScript offers a range of powerful libraries, such as D3.js and Chart.js, that facilitate the creation of visually appealing and interactive data visualizations. By leveraging JavaScript’s capabilities, developers can effectively communicate complex data through engaging visual representations.

It’s a powerful language — especially if you’re dealing with large volumes of statistical data. If you’re interested in learning one of the most popular and easy-to-learn programming languages, check out our Python courses. 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.