What are the Best Programming Languages for AI App Development?
Planning AI and ML development is inevitable. There is a visible shift in customers’ app usage habits. Spoilt by the likes of Amazon and Netflix, people are turning to apps to improve their productivity, finish tasks and improve their decision-making abilities.
Customers seek personalized app experiences and want the apps to show more intelligence and work on their behalf. Apps have become assistants to the modern user.
Businesses need to determine a suitable strategy to give customers the app they seek and deserve. The planning phase is the most crucial part of AI app development. If you haven’t determined the purpose, the target audience and the tech stack, you cannot focus on delivering a suitable solution.
The technology stack decision determines the app’s ability, performance and developer’s productivity. Programming language is an important part of the tech stack decision. This choice helps realize if you can go cross-platform, deliver to the desired timelines and maintain the security requirements. Python is the most popular programming language for AI and ML development. However, there are other languages too that you can consider.
In this blog, we have mentioned the top 8 programming languages you can use for AI app development. Let's read to the end!
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8 Programming Languages to Consider for AI App Development
The tech stack planning begins with the programming language and tools required to design & develop the application. Several languages have raised the bar to make way for smarter app development and application modernization. They have created libraries, tools and even snippets that can speed up AI development.
#1 Python
Most business owners hire Python developers to get started with AI app development. This is the go-to language for Artificial Intelligence applications. The foremost reason being it has expertise in delivering a suitable solution. It also extends the relevant experience in creating intelligent solutions your business demands.
Over the years, Python has created libraries such as NumPy, Scipy and others to help code AI applications. The data visualization ability makes it easier for Python to interpret the data and use it to enhance app decision-making.
As it comes with a strong community, you can get the support needed when developing the mobile application. It is compatible with several browsers and systems, which makes it a perfect choice for cross-platform app development.
Python allows developers to write a few lines of code to establish an algorithm. As a result, it improves the coding speed and enhances the developer’s productivity. You can launch your app faster and effortlessly with this programming language.
Read Here: How to Master Python to use it for Data Science
#2 Java
Java is a popular and versatile programming language to deliver several mobile applications. The simplicity, extensive libraries and human support allow this programming language to take over your share of AI app development.
Java is open-source and has been present for years. As a result, it has evolved into a natural language for developing intelligent apps.
The ability of this programming to depict or interpret data makes it one of the best languages for Machine Language technology.
The added benefits include ease of debugging, hassle-free scaling to meet new project requirements and improved interaction abilities.
#3 Lisp
If you have heard of Lisp, you would know it is an incredible language for developing your AI applications. It was created by Dr. John MaCarthy, who was also the person who coined the term Artificial Intelligence.
When creating human-like applications, you want them to communicate using language that the human speaks. Lisp connects with symbols, which makes it suitable for AI app development.
You can use this language for experimentation and prototyping because of its flexibility. This programming language can make it easier for you to solve a problem. Apart from powerful implementations, it also provides a range of macro machinery. It also poses as an excellent garbage collector.
The biggest benefit is the fact that it can easily manipulate complex data and simplify it.
#4 JavaScript
This interpreted programming language helps create interactive and experiential web pages. It has been impressive for game development, websites and even mobile apps. Owing to its compatibility, this programming language is used to build cross-platform applications.
JavaScript is full of libraries that improve developers’ productivity. The conventJS library has been specifically developed for deep learning features. You can implement nonlinear neural networks and regression cost functions that are important for deep learning apps. It uses matrix implementation that simplifies creating training data sets. With JavaScript, you can use the in-built codes to deliver the application.
#5 C++
While it is a complex and intensive programming language, you will find that it offers classic optimization abilities. It can enhance the developer’s productivity, improve debugging and ensure bug-free app deployment. You can even improve the code execution time with this programming language.
The OOP principle underlying the C++ language ensures smooth performance and excellent runtime control. The pre-built templates make it easier for you to build sharp and immersive AI applications. With a range of tools, it is quite a delight for AI app development.
#6 R Programming Language
This is one of the finest languages used for statistical and data computing. You can create interesting data visualization and improve the data analytical abilities of your app with this language.
The programming language has evolved since its inception. You can use the language for model training and creating data sets. It is an excellent tool for data evaluation as well.
Data analytics and evaluation are key to developing AI applications for your business. With the eventual interpretation of data, you can create meaningful and insight-driven applications. R is an excellent language if you want to develop AI-powered apps and train your machine to think and be like humans.
The biggest benefit of this language is that it can combine with other tools and languages. It also includes several packages that make integrations easy. If you are creating data-heavy AI applications, you should hire AIML developers with R expertise.
#7 Julia
This dynamic programming language was devised to enable numerical and computational science analysis. It is a relatively new programming language, which is popular owing to its AI-driven capabilities. As this language can easily help improve the algorithm creation abilities, you can find the relevant syntax and AI development tools.
As the language attracts popularity, you will find a growing developer community offering support. This language can integrate with libraries such as TensorFlow, JI and Flux to enhance AI development.
#8 Haskell
If you plan an AI-based mobile application, you need to incorporate abstraction to achieve intelligence. Simultaneously, you are responsible for pure functionality and exceptional flexibility.
As a result, you should use the Haskell coding system. It creates the perfect expression for AI algorithms. HLearn is one abstraction that can create excellent algebraic structures. You can use it for probabilistic programming too.
As the coding language is functional and stateless, you can execute the code on various CPUs.
Read More: Which Coding Language Should I Learn First?
Conclusion
While C# and C++ are great for AI app development, you should avoid using C or PHP programming languages. When you choose an appropriate programming language, you lower the challenges and improve flexibility. It helps you easily create the algorithms, scale your app to meet new requirements and enjoy the choice offered by the pre-built libraries.
When you have defined the tech stack, hiring AIML developers with expertise in the particular language becomes easier. You can choose the AI app development company that meets your tech requirements and helps accomplish detailed goals via research.
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