Nowadays, artificial intelligence is becoming popular and mostly used for businesses of different classes. AI is used for different operations in companies to enhance and flourish. So, multiple software development companies have started developing AI solutions for services. To use this service, the developers in your company would need to learn some AI programming languages. You’ll need software engineers who know how to code AI using the best languages.
In this blog, we’ll briefly describe the top programming languages for AI that will be useful in 2024.
What Programming Language Is Used For AI
There are several that can help you add AI capabilities to your project. We have put together a list of the 10 best AI programming languages.
- Python
Python is one of the most popular AI programming languages used for Artificial Intelligence. The large number of existing libraries and frameworks makes it a great choice for AI development. It includes well-known tools like Tensor, PyTorch, and Scikit-learn.
These tools have different uses:
- TensorFlow is a powerful machine learning framework that is used widely to build and train deep learning models, mostly in the application of neural networks.
- PyTorch is a deep learning framework that allows a user to build and train neural networks, mostly for assisting in research and experimentation.
- Scikit-learn is a machine-learning library for analyzing data and building models. It can do tasks like classification, regression, clustering, and reducing dimensions.
Advantages:
- Has a large collection of libraries and frameworks
- Big and active community support
- Code is readable and easy to maintain
Disadvantages:
- With so many capabilities, Python has a steep learning curve
- The syntax can be wordy, making code complex
- Lisp
Lisp is the second oldest programming language. It has been used for AI development for a long time. It is known for its ability to reason with symbols and its flexibility. Lisp can turn ideas into real programs easily.
Some key features of Lisp are:
- Creating objects on the fly
- Building prototypes quickly
- Making programs using data structures
- Automatic garbage collection (cleaning up unused data)
Lisp can be used for:
- Web development with tools like Hunchentoot and Weblocks
- Artificial Intelligence and reasoning tasks
- Building complex business applications that use rules
Advantages
- Good for AI tasks that involve rules
- Very flexible programming
Disadvantages
- Unusual syntax that takes time to learn
- Smaller community and fewer learning resources
- Java
Java is one of the most popular programming languages for server-side applications. Its ability to run on different systems makes it a good choice for developing AI applications. There are well-known libraries and frameworks for AI development in Java, including Apache OpenNLP and Deeplearning4j.
Java can work with various AI libraries and frameworks, including TensorFlow.
- Deep Java Library
- Kubeflow
- OpenNLP
- Java Machine Learning Library
- Neuroph
Advantages
- Can run on many different platforms
- Java’s object-oriented approach makes it easier to use
- Widely used in business environments
Disadvantages
- More wordy compared to newer programming languages
- Uses a lot of computer memory
- C++
C++ is a programming language known for its high performance. Its flexibility makes it well-suited for applications that require a lot of resources. C++’s low-level programming abilities make it great for handling AI models. Many libraries like TensorFlow and OpenCV provide ways to build machine learning and computer vision applications with C++.
C++ can convert user code into machine-readable code, leading to efficient and high-performing programs.
- Different deep learning libraries are available, such as MapReduce, mlpack, and MongoDB.
- C++ Builder provides an environment for developing applications quickly.
- C++ can be used for AI speech recognition.
Advantages
- Highly efficient and performs well, ideal for computationally intensive AI tasks
- Gives developers control over resource management
Disadvantages
- Has a steep learning curve for beginners
- Can lead to memory errors if not handled carefully
- R
R is widely known for statistical computing and data analysis. It may not be the best programming language for AI, but it is good at crunching numbers. Some features like object-oriented programming, vector computations, and functional programming make R a suitable choice for Artificial Intelligence.
You might find these R packages helpful:
- Gmodels package provides tools for fitting models.
- Tm is a framework well-suited for text mining applications.
- OneR algorithm is used for One Rule Machine Learning classification.
Advantages
- Designed for statistical computing, so good for data analysis and statistical modeling
- Has powerful libraries for creating interactive visualizations
- Can process data for AI applications
Disadvantages
- Not very well-supported
- R can be slow and has a steep learning curve
- Julia
Julia is one of the newest programming languages for developing AI. Its dynamic interface and great data visualization graphics make it a popular choice for developers. Features like memory management, debugging, and metaprogramming also make Julia appealing.
Some key features of Julia are:
- Parallel and distributed computing
- Dynamic type system
- Support for C functions
Advantages
- High-performance numerical computing and good machine-learning support
- Focus on ease of use for numerical and scientific computing
Disadvantages
- Steep learning curve
- New language with limited community support
- Haskell
Haskell is a general-purpose, statically typed, and purely functional programming language. Its comprehensive abilities make it a good choice for developing AI applications.
Some key features of Haskell are:
- Statically typed
- Every function is mathematical and purely functional
- No need to explicitly declare types in a program
- Well-suited for concurrent programming due to explicit effect handling
- Large collection of packages available
Advantages
- Emphasizes code correctness
- Commonly used in teaching and research
Disadvantages
- Challenging to learn and can be confusing
- Prolog
Prolog is known for logic-based programming. It is associated with computational linguistics and artificial intelligence. This programming language is commonly used for symbolic reasoning and rule-based systems.
Some essential elements of Prolog:
- Facts: Define true statements
- Rules: Define relationships between facts
- Variables: Represent values the interpreter can determine
- Queries: Used to find solutions
Advantages
- Declarative language well-suited for AI development
- Used as a foundation for AI as it is logic-based
Disadvantages
- Steep learning curve
- Small developer community
- Scala
Scala is a modern, high-level programming language that can be used for many purposes. It supports both object-oriented and functional programming. Scala is a good choice for teaching programming to beginners.
Some core features of Scala are:
- Focus on working well with other languages
- Allows building safe systems by default
- Lazy evaluation (delaying computations)
- Pattern matching
- Advanced type system
Advantages
- Has suitable features for AI development
- Works well with Java and has many developers
- Scala on JVM can work with Java code
Disadvantages
- Complex and challenging to learn
- Mainly used for data processing and distributed computing
- JavaScript
JavaScript is among one of the popular computer languages used to add interactive aspects to web pages. With the advent of Node.js, it became useful on the server side for scripting and the creation of many applications, including AI applications.
Some key features of JavaScript include:
- Event-driven and asynchronous programming
- Dynamic typing
- Support for object-oriented and functional programming styles
- Large ecosystem of libraries and frameworks (e.g., TensorFlow.js, Brain.js)
Advantages
- Versatile language suitable for web development, server-side scripting, and AI applications
- Easy to learn and has a large developer community
- Runs on various platforms (browsers, servers, devices) with Node.js
Disadvantages
- Can be challenging to write and maintain complex applications
- Performance limitations compared to lower-level languages
- Security concerns if not used carefully (e.g., cross-site scripting)
Conclusion
So, choosing the right artificial intelligence coding languages is important for your project needs, right? Well, the developer should keep in mind the project details or the type of software development before choosing the AI coding language.
Now, in this blog, we listed 10 AI coding languages, their features, advantages, and disadvantages. And this can ideally help you make the best choice for your project.
But wait, there’s more! If you know your project requirements, contact us to get custom artificial intelligence development services with suitable AI coding language for your project.
