In the near future, AI will likely be the most transformative technology humans have ever invented. Not only is AI already a part of our everyday lives, but it's also being used to explore space and develop new treatments for cancer. The vast potential of AI is just beginning to be explored.
However, you don't have to wait for the future to take advantage of AI. Gartner estimates that the AI software market will reach $62 billion in 2022. AI is already employed in a wide variety of industries including customer service, e-commerce, marketing, and hospitality.
By harnessing the power of AI, your business can accelerate growth, saving time and money by optimizing your processes and operations. AI can handle some tasks faster and more accurately than humans. You can incorporate AI into tools that will boost your employee's abilities to make on-the-spot decisions based on customer input or data.
Although the potential for AI is vast, you'll need software engineers who are proficient in the best languages for AI in order to realize it. There's no one language that's best for AI, but one may be better than another for your specific project. In this article, we'll go over the languages that are best for AI development and some that you might want to avoid.
What Are AI Coding Languages?
AI coding languages are programming languages that can be used to write code that mimics — and outperforms — human reasoning and problem-solving skills. They're the languages that make technologies and features like speech-to-text, facial recognition, and real-time data analytics possible.
Choosing the right language for your AI project is an art as much as it is a science. You'll need to consider the parameters of your project to determine the best fit. Some languages are best for handling large amounts of data and complicated calculations, while others are strong in natural language planning.
What are the best and most popular programming languages used in AI?
Once you've determined the requirements of your projects, you can look at the strengths and weaknesses of each language to determine which is optimal. These languages have been getting the most use and attention:
Python is one of the most popular AI programming languages due to its versatility and longevity. Python has been around for a long time, having a robust library of resources and community of users. It's easy to learn and can be used for front-end or back-end development. With Python, you'll get clear, streamlined code with simple, easy-to-read syntax.
Python's extensive pre-designed libraries with AI development applications are among its most attractive features. For instance, Python’s Scikit-learn handles regression and classification algorithms. If you're developing a deep learning application, libraries such as Keras, Caffe, and TensorFlow can make the task easier. Some other Python libraries that help with AI development include:
- NumPy, SciPy
Python was developed as a data analysis tool before AI became widely used in business applications, and it has always been widely used in big data. Its simple syntax and powerful libraries allow developers to focus on creating innovative applications while Python's powerful libraries handle complex coding tasks.
Java is another language that's extensively used in AI development. As an object-oriented language with easy debugging and simple syntax, Java has a proven track record in software development. Java's fast execution makes it especially well-suited for applications that require processing large amounts of data. One of Java's biggest advantages is its use of a virtual machine, which allows developers to implement it easily on a variety of platforms.
Once you write and compile your Java program, you can run it on any platform with the write once run anywhere (WORA) methodology. Some of Java's biggest advantages in regard to AI development include:
- Ease of use
- Fast debugging
- Portable memory management
Java also offers a lot of resources, particularly in the form of AI libraries such as:
- Kubeflow, which makes it possible to manage and deploy machine learning stacks
- Deep Java Library, which was built by Amazon to develop deep learning applications
- OpenNLP for processing natural language processing
- Neuroph for designing neural networks
- Java Machine Learning Library for machine learning algorithms
C++ is a flexible language perfectly suited for resource-intensive applications. There are several deep and machine learning libraries built on C++. Because C++ is a low-level language, it converts user code to machine-readable code. It's an efficient language that's a great performer due to its compact code. Other factors that make C++ a good AI development language include its adaptive caching, memory management, and dynamic load balancing.
C++ lacks the simplicity of Python, but as the fastest programming language, it still has a lot to offer AI developers. It also has a steeper learning curve, but its other advantages — including a small but powerful collection of machine learning and deep learning libraries — make it ideal for some projects.
Developed in 2012 at MIT, Julia is a fairly new programming language. It doesn't offer the extensive libraries or support materials that some of the other AI programming languages do, but it was designed with modern AI requirements in mind. It excels at handling broad numerical analysis tasks and extensive data sets.
Julia simplifies AI development with features such as debugging, memory management, and metaprogramming. Some of Julia's features that make it a good choice for AI development include:
- Fast speed
- Powerful computational ability
- Simple syntax
- Arbitrary precision values
- Tuples, dictionaries, and code introspection
- Dynamic type system
If you're looking to integrate with popular tools such as TensorFlow.jl, MLBase.jl, and MXNet.jl, Julia gives you that capability. It also features packages for machine learning such as Metahead, MLJ.JL, Turing.JL, and Flux.JL.
R was developed mainly for statistical computing by statisticians who needed to crunch massive data sets. So it's not surprising that today R is a good language for applications that require massive computations or complex data analysis, including those in artificial intelligence. R supports the use of libraries such as MXNet, TensorFlow, and Keras.
Its ability to handle statistical computations with ease makes R a frequent choice in applications developed for education, finance, medicine, sociology, telecommunication, and pharmaceuticals. It's used by numerous tech giants, including Google, Microsoft, and Facebook.
Some of R's packages that were designed for AI include:
- TM, which is a framework for text mining applications
- OneR, which allows you to use the One Rule Machine Learning classification algorithm
- gmodels, which help with model fitting
Prolog is another mature language. It's been around since 1972 and is used extensively in computational linguistics and AI. Prolog is a good fit for projects that require symbolic reasoning, natural language processing, and databases. While it's not as widely used as some other languages, it's very good at what it does. IBM's Watson uses it to parse natural language when it handles questions generated by humans.
Prolog's ability to pattern match makes it excellent for use cases that involve natural language processing, intelligent database search, and computer vision. It also has powerful metalevel reasoning and excels at list-handling. When programming with Prolog, you specify a list of rules and give it your end goal. Prolog then uses its powerful attributes to find the connection between the two via pattern matching.
Though Rust doesn't have the established machine learning ecosystem that some other languages do for machine learning, several ambitious projects have emerged from it. If you're looking for a high-performance, fast, and safe software development language, Rust is a good option. Developers who are familiar with C++ find Rust contains similar syntax, although it offers memory protection and prevents garbage collection.
Rust was used to develop the back end of several well-known systems, including:
Scala supports both object-oriented programming and functional programming. It was designed to express the most common programming patterns in a way that's simple, elegant, and type-safe. Scala avoids the problems of Java, such as its bloating, while keeping its strong points. Because Scala is a compiled language, its executed code works much faster than other languages such as Python.
- High-performing functions
- Flexible interfaces
- Browser tools
- Pattern matching
Learn More: What Is a Scrum Master and What Do They Do
Programming languages not commonly used for AI
These languages, though not as commonly used, can provide great advantages depending on the nature of the projects you have in mind.
Because of its lack of support, Haskell probably won't be your go-to language for most AI projects. However, there are some specific applications where it may be the best choice. Haskell is a good choice for knowledgable developers who need safety and speed. If you're developing AI applications for teaching, research, or industrial applications, Haskell has a lot to offer, including:
- Type-safe operator overloading
- Lambda expressions
- Type classes
- Pattern matching
- Type polymorphism
- List comprehension
- Excellent error handling
- Code reusability
LISP is one of the oldest languages used in AI development. It was developed in the 1960s by John McCarthy, who also coined the term artificial intelligence. It was used to develop ELIZA, a therapist chatbox and one of the earliest AI programs. LISP has several flaws that have inspired a Why We Hate LISP wiki, but it's a smart, adaptable language that can make it a good option when you need to modify code, solve problems, rapidly prototype, and develop dynamically.
Some features included in LISP that make AI development easier are:
- Dynamic object creation
- Mandatory garbage collection
- Ability to execute data structures as programs
- Ability to modify programs as data
- Symbolic information processing
- Ease of interactive program
LISP is undoubtedly a niche language, though it has devoted fans who are willing to work around its shortcomings. However, even if you don't plan to use LISP for developing AI applications, learning it can help you understand how AI has evolved and can make you a better AI developer.
Learn More: Hire Lisp Developers
How to Choose a Programming Language for an AI Project
Before you decide on the best language for your project, there are a lot of things to consider. Even if the type of project is best suited for one language, if you don't have a pool of qualified developers, it won't be the best choice. A programming language is a tool — it doesn't make sense to decide what type of tool you need before you know how it's going to be used.
Some of the major issues you need to consider before you choose a language for your project are:
- The type of project you're building
- The scalability of the project
- The complexity of the project
- Your development budget
- The timeframe
- Available resources
- Application security
The first thing to consider is the birds-eye view of your project. Take into account all of the moving pieces. Knowing all of the components ahead of time will help you choose the best language. Making a sensible choice ahead of time will save you from having to patch things together later and spend more time maintaining, scaling, and securing the project later down the line.
You can write a good AI program in almost any language, so there's no single best language. Some languages will be a better fit than others for some projects. However, sometimes the best choice is simply the language that is the most convenient in terms of support and resources. Before you decide, consider the following:
What type of application is it?
The type of application you're building will have a tremendous impact on the language you choose. In addition to deciding whether it's going to be a web application, a mobile application, an embedded system, or some other type, consider the complexity and scale as well. A simple project that will stand on its own will have different needs than a large, complex project that will be interacting with other systems and networks.
What platform will it run on?
You want your program to look and behave consistently across all platforms. If you're creating an application that will be used on more than one platform, that can affect your choice of language. You may need different platform compilers and executables if you choose one language, but another may be able to handle it in a simpler manner.
What are the maintenance issues?
Maintenance is always going to be an issue you need to consider with your entire tech stack. You'll need to make sure the language, libraries, and vendor support will stay current and updated for the long haul. At some point, you'll likely need to transfer your codebase to another team. Consider how complicated this may be based on the talent pool in your area, and if regional considerations will be a factor.
What factors will affect performance and scalability?
How many users will you need to accommodate, and how resource-intensive is your program? If your development environment doesn't offer much room to scale, your application's performance will be negatively affected. On the other hand, if your project doesn't need to scale, you can decide on a language that is the best fit for other factors.
Application security should be baked into your development process from the get-go. Every application has different security requirements based on its use case. You'll need to consider the security needs of your project before you decide on the best language. While every project will need to be protected from hackers and cyber threats, some projects may have to meet other security requirements as well.
What kind of resources and support will you have?
If you think LISP is the perfect AI programming language for your project, but you only have one software engineer who knows LISP on your team, you're setting yourself up for failure. The skill set of your development team or the talent pool you have access to will be another major factor in choosing a language. You'll probably be better off choosing the language your team is most skilled in — even if it isn't a perfect fit — rather than choosing a more obscure, ideal language your team doesn't know.
Taking the Next Steps in AI Programming
It's not always possible to find a local, skilled development team in the best AI programming language you need. If you're having problems finding the talent you need, working with a talent marketplace like Revelo can make the process easier. Unlike open job boards, Revelo has a pre-vetted pool of AI software developers who are skilled in the languages you need.
Our developers have been rigorously screened for their tech skills as well as their English skills. They're from Latin American countries that have a significant timezone overlap with the U.S., so you don't have to worry about real-time collaboration conflicts. Reach out to our expert team today to find out how Revelo can help you grow your AI development team.
12 Most Popular Programming Languages