10 Ideal Languages to Build AI Apps and Which One to Choose

While AI has become indispensable in all workflows that involve computers, smartphones, and other devices, we need to understand that it isn’t made from magic. When you start working with large data sets, you’ll realize that there are various underlying programming languages and frameworks that contribute to its unmatched ability.


Python is one of the most widely preferred programming languages for artificial intelligence development owing to its feature-loaded ecosystem and ease of use. Other frameworks such as Java, Haskell, and C++ are also favored by developers for creating AI-based tools and systems.


In this segment, we explore the most effective AI programming languages and which one you need to choose based on your requirements.


1. 10 Popular Coding Languages for AI Development


Let’s take a look at 10 potent frameworks you should consider while building an AI-based system.



1.1 Python


Python has been hands down the most popular AI development framework in recent times, chosen by artificial intelligence developers and businesses alike. It offers an easy-to-use syntax and is easy to learn and shape your skills.


Python consists of a large network of libraries and frameworks such as Scikit-learn, NumPy, and Matplotlip. These options enable developers to save valuable time and resources as they can import and implement them in your project instead of building everything from the ground up.


It is also a multi-purpose framework with a wide array of applications and use cases. For example, Python is the go-to for building machine learning algorithms and neural networks with libraries such as PyTorch and TensorFlow. You can also use it to analyze big data and identify correlations, trends, and patterns.


A vast and thriving community of coders and the open-source nature of Python are the primary reasons behind its growth and current status as the most popular AI & machine learning development platform around the globe. If you are a newcomer to Python, you can get help and resources from thousands of active coders on platforms like Stack Overflow and GitHub.



1.2 JavaScript


Java Script is mainly used in building intuitive browser-based platforms. In essence, it is a client-side scripting language that can run directly on the coder’s browser. Therefore, you can carry out a range of computation tasks and boost the performance of AI-based systems.


It is highly effective in real-time artificial intelligence technologies such as chatbots, virtual assistants, and recommendation engines. Coders can utilize JavaScript to implement AI solutions using APIs (Application Programming Interfaces).


JavaScript’s client-side programming can enable you to offload tasks from servers to the developer’s browser. This results in more responsive software, especially in the case of real-time exchanges.


Extensions based on JavaScript such as Brain.js, ConvNetJS, and TensorFlow.js, you can easily incorporate machine learning (ML) prowess in web development tasks. These tools allow you to train and deploy machine learning models in your browser and improve the capabilities of AI-based applications.


1.3 Prolog


Prolog is preferred for its robust capability in symbolic reasoning and logic programming. It aids the development of AI applications and systems, especially in the domain of problem-solving and knowledge representation.


Its annotative nature lets you indicate the rationality of computations without the supporting flow of control. Developers can use relationships, facts, and rules in a linear manner. This makes Prolog an ideal choice for building knowledge bases in AI-driven software.


Prolog is also known for its ability to match patterns and lets you go through knowledge bases and scan them swiftly depending on logical relationships. This characteristic comes into play while developing technologies like Natural Language Processing (NLP) where computers can understand, interpret, and generate human-like language. Chatbots and virtual assistants are an apt example of NLP in action.



Read Also: Navigating the Impact of AI in Mobile App Development



1.4 Java


Java is a versatile object-oriented programming language for building desktop, mobile, web, and cloud applications and software. It is independent of the underlying platform and allows code portability. This means that applications built using Java can run smoothly on any system that has the Java Virtual Machine (JVM) installed.


As it is object-oriented, it is much simpler to create high-performing and easily maintainable code. Object-oriented programming (OOP) allows your code to be modular and lets you fragment your applications into smaller and more adaptable files.


Java offers different built-in libraries and modules such as Apache Commons and JUnit help you cut down on development time and create top-notch AI & ML solutions. For example, Java is generally used to build robotics, simulations, and embedded artificial intelligence applications.


Extensions such as RTSJ (Real-time Specification for Java) enable software developers to build time-sensitive systems like chatbots. Java also allows multithreading and supports AI tasks that need to run in real-time.


Java is also a highly scalable programming language and you can build both primitive programs and large applications that scale based on the demands of the user. In addition, a vast community of Java users grants you access to ample tutorials and resources for learning.



1.5 Lisp


Lisp is a robust programming platform and a favorite among artificial intelligence development services due to its adjustability and compatibility with symbolic extension processing.


Symbolic expressions can stand for both data and code. It can be both elementary data such as numbers and strings and large data structures like arrays and lists. This distinctive capability enables Lisp applications to operate complex computing tasks and manipulations on extensive sets of data.


Lisp allows developers to perform recursive programming and build advanced AI & machine learning algorithms that can process large volumes of data. You can also access automatic garbage collection, assign memory management functions to AI applications, and focus your time on more critical matters.


It is a dynamically typed framework and enables the interpreter to appoint types to variables at runtime. This increases adaptability and facilitates the analysis of complex data sets and structures, which is vital for managing the dynamic nature of artificial intelligence.


Lisp stands among the preeminent languages that enable functional programming and its lucid syntax facilitates extensibility and expressiveness. This allows better levels of experimentation in AI and other fields dealing with data analytics.



1.6 Julia


Although relatively a newcomer in the software development scene, Julia has already grown immensely popular in the AI community due to its optimum performance and quick prototyping abilities. Like Python, Julia has an easy-to-learn syntax, and developers around the world prefer using it.


Julia can also work seamlessly with other programming languages such as Python, C, and R. This allows coders to make use of extensive libraries and dependencies like PyTorch and TensorFlow, which are programmed in different languages.


The out-of-the-box distributed computing and parallelism features of Julia allow Julia to handle more complex workloads and larger amounts of data without a major drop in performance and productivity.


Being an open-source framework, Julia developers can access different community-controlled libraries, conserving time and resources during the development process.


The most useful tools offered by Julia include Gadfly.jl and Plot.jl for data visualization and MLJ.jl for developing machine learning and deep learning models. Julia also offers its own library, Sciktlearn.jl, which provides an interface to similar Python libraries.


However, you should realize that building applications on these platforms requires extensive programming knowledge and experience. Therefore, it is prudent to hire a leading artificial intelligence development company  unless you possess the necessary skills.



1.7 C++


The specialty of C++ lies in making intricate software platforms with increased levels of abstraction. It is popular among artificial intelligence developers. It offers versatility, functionality, and low-level abilities that promote smoother communication between hardware and software components.


It can also access Python libraries like TensorFlow and PyTorch via APIs and bindings and lets users incorporate advanced capabilities into their applications. You can perform parallel computing and multithreading on C++ and level up the performance of your application.


In addition, C++ provides more control to AI developers and a better grip over memory allocation. For instance, you can distribute more memory to functions for complex tasks. This reduces lags, glitches, and other performance concerns.


It provides extensions such as OpenCL and CUDA (Compute Unified Device Architecture) to promote hardware acceleration and enable machine learning algorithms to leverage hardware components like GPUs to drive performance.



1.8 R


R is an ideal programming language for statistical operations and data analytics. It offers various libraries for data visualization such as Shiny, GPTlot2, and Lattice, which let developers effortlessly create charts, graphs, head-to-head comparisons, and other visuals with data. It has in-built functions like ‘Im’ and ‘glm’ that enable you to perform statistical modeling.


It offers a unique ‘Caret’ package that helps you develop, train, and test artificial intelligence and machine learning-based models. It lets you conduct hyperparameter tuning to upscale the performance of your software.


R also offers you performance evaluation tools that let you resample and cross-validate your application. It is adaptable to other frameworks such as Python and Java, creating an active ecosystem with a broad range of libraries for coders.



1.9 Scala


Scala offers commanding object-oriented programming features that help developers write clear and efficient code, which is essential for robust AI applications. It provides you with out-of-the-box immutability, meaning that objects do not alter their state after they have been declared.


It enables programmers to constantly monitor and work with these objects throughout the entire life-cycle of the development process. Just like C++, Scala allows parallel computing and multiprocessing for coders to handle different programming tasks in tandem.


You can also use code written in Scala in Java-based programs with the help of tools like Spark and Apache Hadoop that let you process large volumes of data. Scala’s capability of pattern-matching makes it ideal for building technologies like natural language processing where the software needs to understand queries and generate relevant responses.



1.10 Haskell


Haskell works with the core principles of immutability, high-order functions, and pattern-matching for building first-rate AI-based software, Molded by a strong mathematical foundation, and helps you create intricate algorithms along with machine learning and deep learning models.


Its capability of lazy evaluation guarantees that elements of your code are only evaluated when needed. This prevents the wastage of time, human labor, and computational resources.


Haskell offers a strong type system and type safety that thwarts programmers from allocating incompatible values to variables, increasing the performance and reliability of applications built with it.



Read Also: Integrating AI & ML Solutions In Business: A Definitive Guide



2. How to Choose the Ideal AI Programming Language


There is no universal answer to the question “Which is the best programming language for building AI apps”. Each framework comes with its unique characteristics, strengths, and weaknesses.


What your exact requirements are is the more critical question here. Keep the following factors in mind while selecting a programming language for AI application development.


2.1 Evaluate Your Project Requirements


Assessing and deciding what exactly you want from your artificial intelligence application and how you want it to function are key initial steps. Focus on the structure of your software and the basic features and functions your platform offers.


Different development projects for web applications, business process management software, native apps, machine learning models, chatbots, etc. have separate requirements and you need to base your decision on these factors.


For example, JavaScript excels at building web applications as it is compatible with browser functionalities by default. On the contrary, C++ is a better choice for building robotics-based applications as it allows low-level hardware integration.



2.2 Level of Expertise


Before choosing the most suitable platform, evaluate your skill levels as these programming frameworks have different learning curves. For instance, Python is easier to learn as a beginner, whereas C++ has a more uphill learning curve and takes longer to master.


Learning a programming language and then building an AI application all by yourself is a mammoth task and can take months and even years. Moreover, there’s no guarantee of success when you adopt such an approach.


This is why it is more feasible to outsource your AI development project to a trusted artificial intelligence development company with talented developers who are versed in different programming languages.



2.3 Frameworks and Libraries Offered


It is always favorable to choose a development platform that has a wide array of tools, libraries, and extensions to accelerate your work. These tools allow you to create AI-based software without having to build the core functionalities from scratch. In this regard, Python offers the most extensive set of AI, machine learning, and data analytics extensions that aid artificial intelligence development.



2.4 Stability and Performance


Execution speeds and memory management techniques should also be on your list of metrics when building AI systems. You should prioritize languages that allow multithreading and parallel computation. Consider frameworks such as Python, Java, and C++ for optimum processing performance and efficiency.



2.5 Community and Support Ecosystem


An active and buzzing community can help you with troubleshooting, sharing resources, and improving your skills. This is why you should choose languages with large communities and user bases of artificial intelligence developers.


Besides Python, Javascript, Java, and C++ offer wide user bases and vibrant communities of programmers. You can also consider Julia, a relatively new but rapidly growing AI development platform.



Wrapping Up


Challenging yet highly rewarding, developing AI applications offers a stimulating path to both professional and personal accomplishment. While there are various available options for programming languages, you need to carefully consider your choice based on your requirements, skill levels, and offered extensions and libraries.


If you want to leverage the power of cutting-edge AI & ML solutions and drive your business ahead of the curve, reach out to us at Webgen Technologies. A reputed software development company in Kolkata, we specialize in making your digital dreams real.

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