If your company aims to incorporate Artificial Intelligence (AI), it’s crucial to consider specific programming languages for your developers.
AI has become a top priority for businesses seeking to enhance growth and streamline operations. By automating routine tasks, AI can save time and money while ensuring faster and more accurate outcomes.
Furthermore, AI excels at making data-driven business decisions, serving as chatbots, powering mobile and web applications, and optimizing processes through pattern recognition. To implement AI effectively, skilled software engineers proficient in the right programming languages are essential. In this article, we will explore the best languages available for Artificial Intelligence.
What Is Artificial Intelligence?
Artificial intelligence (AI) refers to intelligent machines that can learn and work on their own. It aims to create computer systems that can perform tasks and predict outcomes without explicit programming.
AI is already transforming various industries and is utilized in everyday devices like smartphones. Despite popular misconceptions, the idea of killer robots threatening humanity is unlikely. AI technology brings numerous benefits, including its integration into marketing automation tools.
5 Best Programming Languages for AI Development
Python is a popular high-level general-purpose programming language, known for its simplicity and object-oriented approach to code maintenance. Despite its user-friendly nature, Python is capable of handling large and complex projects effectively. It finds extensive use in data engineering and machine learning applications. Machine learning, a subset of AI, involves training machines using algorithms.
Python’s leading framework for AI development is TensorFlow, an open-source library for training deep neural networks.
Additionally, Python offers various pre-existing libraries and frameworks like Pandas, SciPy, nltk, PyTorch, Keras, Theano, and scikit-learn, which enable tasks such as natural language processing (NLP) and advanced mathematical evaluations.
NLP is utilized by applications like Google and Alexa to comprehend and respond appropriately to user input. Furthermore, Python stands out as one of the easiest languages to learn, despite its impressive capabilities.
Simple and Readable Syntax: Python has a straightforward syntax that emphasizes readability, reducing the cost of program maintenance and making it easy to understand and write code.
Support for Modules and Packages: Python supports modular programming by offering modules and packages, which promote code reuse and enhance program modularity.
Extensive Standard Library: Python provides an extensive standard library, available for all major platforms in both source and binary form, offering a wide range of pre-built modules and functions without any additional cost.
Python’s suitability for AI is reinforced by the following factors:
Rich Data Analysis Libraries: Python offers a wealth of libraries, including Pandas, that facilitate data analysis and manipulation, making it convenient to work with datasets.
Dedicated Machine Learning Libraries: Python provides specialized libraries like TensorFlow and Keras, designed explicitly for machine learning tasks, enabling developers to build and train sophisticated models.
Solid Scientific and Computation Libraries: Python features robust scientific and computation libraries such as sci-kit-learn and NumPy, which provide powerful tools for scientific computing and numerical operations.
Versatility in Microcontroller Programming: Python extends its reach beyond traditional software development by supporting microcontroller programming through projects like MicroPython, CircuitPython, and Raspberry Pi, enabling AI integration into embedded systems.
Java is a highly valued server-side language known for its versatility across platforms through its virtual machine. While Kotlin is an alternative for Android developers, Java remains the native language for Android app development.
The rise of artificial intelligence in app development has been recognized by mobile app developers. Notable examples include FaceApp and Google Assistant, which incorporate artificial intelligence using Java.
Beyond mobile apps, Java offers several machine-learning libraries for deep learning and natural language processing. Some noteworthy options include Deep Java Library, Kubeflow, OpenNLP, Java Machine Learning Library, and Neuroph. Moreover, Java’s object-oriented design enhances its usability, making it a suitable choice for AI projects.
Object-Oriented: Java is built on an object-oriented programming paradigm, supporting concepts like encapsulation, inheritance, and polymorphism, which promote code organization and reusability.
Platform-Independent: Java’s bytecode can be executed on any platform equipped with a Java Virtual Machine (JVM), making it highly portable and ensuring compatibility across different operating systems.
Statically Typed: Java enforces static typing, which guarantees type safety during compilation, reducing the likelihood of runtime errors and enhancing program stability.
Large Standard Library: Java offers a comprehensive standard library that provides a wide range of pre-built classes and functions, simplifying the development process by offering ready-made solutions for common tasks.
Exception Handling and Multi-Threading Support: Java facilitates graceful error handling through its exception handling mechanism, allowing developers to manage errors and exceptional conditions effectively. Additionally, Java’s built-in support for multi-threading enables concurrent execution of multiple parts of a program, enhancing performance and responsiveness.
Java’s suitability for AI is reinforced by the following factors:
Speed, Reliability, and Tooling Support: Java is renowned for its performance, reliability, and excellent tooling support, providing developers with efficient means to develop complex AI applications quickly and effectively.
Battle-Tested in Mission-Critical Applications: Java’s extensive use in mission-critical applications demonstrates its reliability and stability, making it a trusted choice for AI implementations that require robust and dependable solutions.
Versatility in Application Development: Java can be utilized for developing both desktop and mobile applications, with the latter made possible through frameworks like Android Studio, allowing seamless integration of AI into various platforms.
Lisp, a programming language dating back to the 1960s, has found extensive use in scientific research, particularly in natural language processing, theorem proving, and solving artificial intelligence problems. Originally designed as a mathematical notation, Lisp gradually became a favored choice among AI developers.
Despite being the second oldest programming language still in active use.
Rapid Prototyping: Lisp’s flexibility enables quick and iterative development, making it well-suited for prototyping and experimentation in AI.
Dynamic Object Creation: Lisp supports dynamic object creation, allowing developers to manipulate and create new objects at runtime, providing versatility in AI programming.
Mandatory Garbage Collection: Lisp includes automatic memory management through mandatory garbage collection, freeing developers from manual memory management concerns and reducing potential errors.
Execution of Data Structures as Programs: Lisp treats data structures as executable code, enabling programs to process and manipulate data directly, enhancing the flexibility and expressiveness of AI algorithms.
Programs as Data: Lisp’s unique feature allows programs to be treated as data, enabling meta-programming and facilitating powerful abstractions and dynamic code modification in AI applications.
Recursive Control Structure: Lisp relies on recursion as a control structure, providing a concise and elegant way to express algorithms and enabling efficient problem-solving techniques in AI.
Symbolic Information Processing: Lisp excels in symbolic information processing, making it well-suited for tasks such as symbolic reasoning, pattern matching, and knowledge representation in AI.
Read-Eval-Print-Loop (REPL): Lisp provides a REPL environment, enabling interactive programming and rapid experimentation, which is particularly valuable in AI development.
Julia emerges as a newer language for AI development, offering compelling reasons to consider it for your next AI project. Despite its small community, Julia consistently appears on lists of the best languages for artificial intelligence.
Julia’s appeal stems from its dynamic interface and impressive data visualization capabilities. It combines memory management, debugging, and metaprogramming, amplifying its allure for AI development.
The language’s quintessential features include direct support for C functions, a dynamic type system, and the ability to leverage parallel and distributed computing.
Regarding AI development specifically, Julia excels with its extensive collection of prebuilt models, algorithmic support, and numerous packages for probabilistic programming. Notable examples include MLJ.jl, Flux.jl, Turing.jl, and Metalhead.
Key features of Julia:
High-performance numerical and scientific computing design.
Easy to use and learn.
Direct integration with C and Fortran libraries without the need for wrappers or interface code.
Support for parallel and distributed computing.
Extensive standard library, including capabilities for differential equations, optimization, and machine learning.
What makes Julia good for AI:
Speed combined with flexibility for rapid experimentation with different models.
Well-developed machine learning libraries, such as Flux, MLJ, and KNet.
R is a favored language among statisticians, offering valuable tools for data analysis. While it may not be the top choice for artificial intelligence, R excels at number crunching and accurate data representation.
In AI development, data plays a vital role, and to analyze and interpret data effectively, a strong mathematical foundation is required. This is where R shines, surpassing Python in terms of accuracy and precision.
Key features of R:
Integrated suite of software facilities for data manipulation, calculation, and graphical display.
Effective data handling and storage facility with a wide range of operators for array, list, vector, and matrix calculations.
Extensive graphical facilities for data analysis and display, including interactive web-based interfaces like Shiny.
A well-developed, simple, and consistent programming language with conditionals, loops, user-defined recursive functions, and input/output capabilities, including connections to relational databases.
What makes R good for AI:
Designed specifically for statisticians, catering to their needs and requirements compared to general-purpose languages like Python.
Offers many AI-relevant features such as linear and nonlinear modeling, time series analysis, classification, and clustering, making it suitable for AI development.
What Programming Languages Aren’t Suitable for AI?
Some languages are not well-suited for AI development due to various reasons. Here are a few examples:
PHP: While PHP is a widely used scripting language for web development, it lacks the flexibility and robustness required for AI development. It is not equipped with the necessary features and libraries for AI tasks.
Ruby: Ruby is another popular scripting language used in web development. However, compared to Python, it is not as efficient for rapid prototyping in AI. Developing a functional AI system may take more time and effort with Ruby.
C: C is a low-level language favoured by system administrators and embedded system developers. However, it is not well-suited for AI development due to its limited high-level features and libraries. AI tasks often require more advanced functionalities that C may lack.
Fortran: Fortran is primarily used in scientific and engineering domains. Although it has a dedicated user base, it is considered difficult to learn and lacks convenient features that facilitate development. This can pose challenges when working on AI projects.
It’s important to note that a skilled programmer can theoretically implement AI in any programming language. However, the choice of language can significantly impact the complexity and efficiency of the AI development process. Opting for a language with appropriate features and libraries can streamline the development and make it more manageable.
AI has become a reality, transforming our lives and presenting new opportunities. Choosing a programming language for AI depends on various factors, such as the task, platform, and personal expertise. Python is widely regarded as the best language for AI due to its user-friendly nature, extensive libraries, and active community. R is a strong choice for statistical modeling. Julia is gaining popularity for its speed and efficiency. For low-level systems or performance-critical applications, C++ or C# are recommended. Ultimately, selecting the right language involves careful consideration of the specific requirements and context of the AI project.
Is C++ better than Python for AI?
No, C++ is not considered better than Python for AI development. Python is generally regarded as the best programming language for AI due to its ease of use and extensive libraries. However, C++ can be utilized for AI if low-level programming or high-performance routines are required.
Is Java or C++ better for AI?
While Python remains the preferred language for AI, both Java and C++ have their advantages in certain use cases. C++ is suitable for coding high-performance routines, while Java is favored for more production-grade software development.
What do the best languages for AI development have in common?
The top programming languages for AI development share a few common characteristics:
They are high-level languages, making them easy to learn and code.
They offer expansive ecosystems of libraries and frameworks for AI development.
They have a strong presence in the AI community, ensuring ample online resources and support.
What programming language do games use for AI?
Most games employ C++ for AI development due to its speed and ability to handle high-performance applications. However, there are also games that utilize other languages like Java for AI programming.
Where should I start if I want to get into AI programming?
If you’re new to AI programming, it is recommended to start with Python. It is the most popular language for AI development and offers a relatively easy learning curve. Once you have a solid understanding of Python, you can explore other languages as needed. You may find the article “5 Ways to Get Started with Machine Learning!” helpful as well.