AI has some remarkable applications in countless areas, including marketing, customer service, education, eCommerce, travel, hospitality, etc.
According to Gartner, AI software revenue would surpass $60 billion in 2022, increasing steadily from 21% in 2021.
Having an intuitive AI system gives users a brilliant experience – something that hinges on the choice of an appropriate programming language. As of today, there are many AI programming languages, each having its strengths and weaknesses. With a vast majority of firms using AI to deliver a high-quality digital experiences to their customers, the choice of language certainly matters.
And this choice depends on a specific criterion. A language that suits your business needs should:
- Be easier to learn and program applications
- Make excellent tools, frameworks, and libraries available
- Feature community and documentation for support
To that end, let’s explore the top five programming languages for AI development.
Top 5 Choices
AI technology relies heavily on programming languages to ensure proper functions while triggering commands, monitoring systems, displaying content, etc. For most developers, Python is considered the best for AI development. However, more all-around options also deliver seamless performance and execution.
Python tops the list as the most preferred AI programming language. A high-level and general-purpose programming language, developers revere Python for distinct reasons like an object-oriented approach for code maintenance and simple syntax.
Python is a user-friendly programming language equipped to handle complex and large projects. Both Machine Learning (ML) and Data Engineering can be labeled as the use cases of Python.
Python’s popular AI framework for development is TensorFlow. It is an open-source ML library used for training deep neural networks. Other pre-existing frameworks and libraries include Pandas, nltk, SciPy, PyTorch Theano, Keras, and scikit-learn.
- Easy to learn and read
- Well integrated
- Well documented
- Platform independent
- Outstanding visualization tools
- Interpreted language
- A bit slower
JS is popular because of its multi-paradigm approach, flexibility, and full-stack features. It is also easier to master the basics of JS. As such, it’s preferred for mobile and web app development, game development, and desktop app development.
JS is primarily used to develop more dynamic and secure websites. While Python is for developers who prefer limited coding, JS is for those who wouldn’t mind coding extensively.
Moreover, JS has AI capabilities that make interaction easier and work smoother with source codes such as CSS and HTML. JS also features a community of competent developers supporting the development process. Not to forget that JS libraries like React.js, Underscore.js, and jQuery make AI development more effective.
- JS ensures high performance, less development time, and better security
- Manages both frontend as well as backend functions
- Fast and scalable
- Works efficiently with independent applications
- Easy to learn and use
- Inadequate libraries
- Not for beginners
- More challenging and complex
Julia is a relatively new programming language for AI development. With smaller community support, Julia still effectively manages large datasets and extensive numerical analysis.
MIT engineers developed Julia in 2012 to meet the demands of AI development. It demonstrates remarkable speed, brilliant computational capacity, scripted syntax, and several other features to ensure developers can deliver outstanding results.
Julia offers many tools to create impressive graphics and a dynamic interface for data visualization. It also features metaprogramming, memory management, and debugging.
Besides, Julia is excellent for ML projects. Whether developers want assistance with algorithms, premade models, or explore probabilistic programming, they can use packages like Flux.jl, MLJ.jl, Metalhead, and Turing.jl.
- Translates research paper algorithms into code directly
- Reduces model risks
- Increases safety
- High-performance programming language for AI designed for modern applications
- Solid ML support
- Young programming language
- Lacks community support
- Difficult to learn
R is a typical statistical programming language. It is one of the most suited options for projects that involve statistical computation. Since data is essential for AI development, developers can analyze as well as represent data accurately with R.
AI applications include big data with crunching. R was developed for this purpose. It performs computation seamlessly and crunches massive datasets swiftly. Today, R is the most powerful AI programming language used for ML and AI applications involving data analysis or computation.
Considering AI development involves expansive data analysis, R is the best choice for domains like medicine, finance, sociology, etc. It supports vast libraries like MXNet, TensorFlow, Keras, etc. It also leverages CARAT for regression training and classification and Random Forest to generate decision trees, among others.
- Statistical programming language
- Integrates with other programming languages
- Several packages available
- Powerful AI programming language with strong data processing
- Sharp learning curve
- R is slow
- Does not have enough support
C++ is a consistent object-oriented programming language that works efficiently with machines and hardware. However, it does not work well with the theoretical aspect of the software. Nonetheless, this low-level programming language is ideal for managing AI models in the production phase.
Using C++, developers can create neural networks right from scratch and translate the code to ensure machines understand. Most of the notorious ML libraries were developed using C++. As a result, this programming language still garners attention in the AI world.
TensorFlow, one of the key ML frameworks, is built with C++. By far, it is the most flexible and powerful tool for software design.
- Rapid speed of processing
- Useful attributes
- Generates compact code to run modules efficiently
- Dynamic load balancing
- Memory management
- Adaptive caching
- Phenomenally successful ML and DL libraries
- Difficult to learn and adapt
- Lacks quality-of-life features
- Manual intervention required
Note: Programming Languages to Avoid for AI Development
Some programming languages are not at all suitable for AI development. To that end, some low-level niche languages to avoid are PHP, Ruby, Fortran, and C.
From the above discussion, it is evident that developers have a variety of language options to choose from. Nevertheless, there are some preferred options worth considering based on the type of project.
All in all, deploying the apt programming language in the tech stack is just a small step in building competent software. To realize the full potential of AI, businesses must consider the size, impact, and objectives of the project.
As such, they must connect with a competent team to ensure the project’s success. At Ascentt, we take charge of building the team and delivering robust AI solutions.
Contact us to know more.