AI Programming Languages: Key Technologies

Best Programming Languages for AI

Programming languages are particular computing languages, including instructions/commands that deliver different outputs. They are often classified as specific, imperative, logical, functional, and object-oriented, developed for various requirements and usage.

Programming languages are specially developed to create software programs, scripts, and different sets of instructions that computing devices can execute smoothly. Developers use several types of programming languages, categorized as High-Level, Low-Level, Scripting, Markup, Domain-Specific, etc. Below are some common languages for artificial intelligence widely used by developers, their strengths, and use cases.

1. Python

Python is considered the most straightforward programming language among web developers. It is an object-oriented language with a high-level data structure and built-in libraries.

Strengths

Python is primarily known for its simplicity and readability. It has strong community support and a web of third-party libraries with extensive standard libraries. It is easy, decoded, and has a dynamic semantic language.

Use Cases

Python is widely used for data analysis, web development, scientific computing, artificial intelligence, scripting and automation, data science, and machine learning. It is suitable for rapid application development.

Check out our Python Tutorial for beginners.

2. R

R is a popular, well-developed, simple, and effective programming language. It involves loops, conditionals, user-defined recursive functions, and input/ output facilities. R can be considered a different implementation of the S programming language. It provides an open-source route to participation in statistical methodology research. 

Strengths 

The R programming language is specially designed for data analytics. It has extensive libraries for data visualization and manipulation. It involves producing well-designed publication-quality plots with mathematical formulas and symbols as required. It compiles and runs on various UNIX platforms and similar systems (including FreeBSD and Linux), Windows, and MacOS.

Use Cases 

R is specially used for statistical computing and graphics. It is also used for academic research, data visualization, analysis, and statistical modeling. 

3. Java

Java is considered the number one programming language and development program. It is a prevalent, object-oriented programming language known for its robustness and versatility. Java is a secure language that encourages strong memory management and type-checking.

Strengths

Java is a platform-independent language, so Its code can be run on any platform with a Java virtual machine (JVM). It follows OOP ( object-oriented principles), which creates codes around reusable components called objects. 

It is a mature, stable programming language with a large community and resources. Java also has a rich ecosystem of libraries and frameworks that simplify development tasks.

Use Cases

Java is primarily used for web development, artificial intelligence, Android development, cloud applications, and more. It is also suitable for Enterprise applications, web applications, big data and scientific computing, and desktop applications.

Learn more about Java in our Java Tutorial for Beginners. 

4. Julia

Julia is a quiet new programming language known for its exceptional speed. It is a powerful language gaining attention in data analytics and scientific computing. Julia is a high-performing language that can maintain clear and concise syntax. It is also a high-level and easily readable language specially designed for scientific computing.

Strengths

Java is known for its incredible speed, which allows faster simulations, model training, and data analysis. It supports multiple dispatches, enabling functions to adapt their behavior based on the data types they receive. Julia also has a rich ecosystem of scientific packages involving disciplines like machine learning, bioinformatics, etc.

Use Cases

Julia is suitable for scientific computing, machine learning, data science, and visualization. It is also suitable for finance and quantitative analysis and can handle complicated financial models. 

5. JavaScript

JavaScript, or JS, is a popular fundamental programming language for the web. It combines CSS and HTML technologies to create interactive and dynamic web experiences. It is a valuable scripting language for dynamically updating content, animating images, controlling multimedia, and much more. 

Strengths

The strength of JavaScript is that it can make web pages interactive. It can help you create images, animations, dropdown menus, etc. JavaScript is easy to learn and can make a good starting point for beginner programmers. It is a versatile language used in many use cases, such as mobile app creation, game development, and server-side development with frameworks. 

Use Cases

JavaScript is used in client-side scripting, server-side scripting, game development, and creating full-fledged games. 

Learn JavaScript from scratch in our JavaScript tutorial for beginners.

6. C++

C++ is one of the popular object-oriented programming languages that offers a clear structure to computing programs and allows codes to be reused. It is a powerful and influential programming language with versatility and high-performance productivity. Many web developers view it as the best programming language for creating large-scale applications.

Strengths

C++ is popular and widely used because it compiles directly to machine code, which makes it faster and more efficient than others. It offers fine-grained control over memory allocation and deallocation using pointers. C++ supports the OOP concept (object-oriented Programming) and has rich libraries. It is a portable language where codes can be run on various platforms.

Use Cases

C++ is specially used in System programming, Game development, Performing Critical applications, Graphics and Animation, Desktop Applications, etc.

Check out our C++ Tutorial for Beginners here. 

7. Lisp

Lisp, formally known as LISP, is an abbreviation of “list processing.” It is a powerful, old, and influential programming language with unique looks and advanced features. Lisp is a set of languages known as the second-oldest high-level programming language, and many developers still use it. It uses parentheses and prefix notation.

Strengths

The strength of Lisp programming is that it is a flexible and extensible programming language that allows you to define new syntax. It supports metaprogramming that manipulates other programs. It helps handle symbolic data, computer algebra, and natural language processing.

Use Cases

Lisp programming language is widely used in Artificial intelligence, Machine learning, Natural language processing, Rapid prototyping, Symbolic computation, Academic Research, etc.

8. Haskell

Haskell is a functional, non-strict programming language with expressive syntax and rich built-in architecture. It is a general-purpose, declarative, statically typed programming language with type inference and lazy evaluation. 

Strengths

Haskell programming language has a strong capability for high-level programming with code safety and reliability. It supports expressiveness and readability through functional programming and features like recursion and higher-order functions. Its functions are suitable for parallel programming on multicore processors. 

Use Cases

Haskell is mainly used in Financial applications, Compiler construction, Formal verification, scientific computing, and Domain-specific languages.  

9. Prolog

Prolog is a high-level logic programming language first devised for AI applications. It is a declarative programming language where logic is expressed as relations. Prolog helps define facts and rules to solve problems. It supports declarative programming and relies strongly on pattern matching to find solutions.

Strengths

The primary strength of the Prolog language is its excellent representation of knowledge in a specific domain. Expressing complex relationships and rules is more accessible and has a built-in research engine that effectively explores possible solutions. 

Use Cases

Prolog is specially used in artificial intelligence applications, computational linguistics, natural language processing, bioinformatics, robotics and expert systems, theorem proving, logic puzzles, etc. 

The Future of AI Programming Languages

While the world is experiencing digital transformation and evolution, several key trends and factors are expected to shape the future of programming languages. Developers will focus on advanced technologies like artificial intelligence and quantum computing, especially Security and efficiency, cross-platform compatibility, accessibility and democratization, and Domain-Specific Languages. 

Above all, the future of the programming language depends on continuous adaptation and innovation. 

Conclusion

AI coding languages are the backbones of any successful digital operation. Programming languages must be chosen correctly according to the project requirements. Several leading and emerging programming languages are available that can effectively address the challenges and opportunities presented by advanced technologies.

If you are looking to enhance your AI programming skills further, then we would highly recommend you to check Simplilearn’s Artificial Intelligence Engineer program. This course can help you hone the right AI skills and make you job-ready in no time. 

FAQs

1. Does AI need Coding?

Yes, Coding is the basic process or requirement for AI development, including building brains, training, handling data, and many other uses.

2. How do I choose the correct programming language for my AI project?

Choosing the correct programming language for AI projects depends on many factors. The type of Project, Team expertise, Project scale, performance requirements, Deployment, and Integration are some significant factors that can affect the choice of programming language.

3. Do I need to learn multiple programming languages for AI development?

Learning multiple programming languages is generally optional if you are a beginner or want to start from scratch. Learning one core language like Python is enough for many AI projects. You can also learn other languages where an additional language might be helpful to or is required by your specific job role or project.