Following the pandemic, AI has risen to prominence as a priority for businesses due to its potential to improve customer experience, resilience, and reliability. In addition, machine learning, data analytics, and conversational AI advancements have made it feasible and affordable for businesses to deploy AI tools that enable them to solve problems and increase efficiency. According to Fortune Business Insights, “the global artificial intelligence market was worth USD 27.23 billion in 2019 and is expected to reach USD 266.92 billion by 2027, growing at a 33.2 percent compound annual growth rate over the forecast period.”
Here is a list of prevalent artificial intelligence programming languages widely used to develop AI applications across various industry sectors.
Java
Java is the language of choice for all developers for various applications, including client-server web applications, Android applications, AI and machine learning applications, search algorithms, multi-robot systems, server-side programming, and neural networks. Neuroph, Apache OpenNLP, Java Machine Learning Library, Deep Java Library, and MLlib are just a few popular Java AI libraries and frameworks.
Google, Netflix, Uber, Airbnb, Instagram, Spotify, Amazon, Slack, and Instagram are a few Fortune 500 companies that use Java.
Python
Python is one of the most widely used programming languages, with AI and machine learning applications, data science, web apps, desktop apps, networking apps, and scientific computing. Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow are a few of the specialized libraries available, making them an excellent choice for AI apps.
Python is best suited for natural language processing (NLP) and AI apps because of its rich text processing tools, simple syntax, and scripting with modular architecture. However, Python is also widely used in apps and programmes for deep learning, AI, network security, numerical data processing, and scientific calculations.
C++
C++ is well-suited for developing operating systems, games, browsers, competitive programming, embedded systems, and AI applications. Several popular AI-ML libraries for C++ include TensorFlow, Caffe, Microsoft Cognitive Toolkit (CNTK), mlpack Library, DyNet, FANN, OpenNN, and Shogun. In addition, Google, Lyft, Walmart, Accenture, Twitch, and Telegram all use C++.
Javascript
JavaScript, like Java, is an excellent choice for AI development. JavaScript’s AI capabilities allow it to interact and work seamlessly with other source codes such as HTML and CSS. In Javascript, AI development becomes more effective with libraries like jQuery, React.js, and Underscore.js. In addition, you can use JavaScript to manage both frontend and backend functions, including multimedia, buttons, and data storage.
R programming
Data miners and statisticians use R extensively in their statistical software and numerical analysis. Additionally, it supports libraries for deep learning such as TensorFlow, Keras, and MXNet. Furthermore, r includes built-in support for data modeling and graphics, allowing programmers to quickly build critical deep learning models. Additionally, Facebook, Google, Twitter, Uber, Microsoft, and Airbnb use R, as do a variety of other domains such as finance, telecommunications, education, life science, and pharmaceuticals.
Lisp
Lisp is one of the earliest programming languages for AI development. Successful Lisp projects include Routinic, Grammarly, and DART. Moreover, Lisp is a promising programming language for developing artificial intelligence despite its shortcomings. CLML, mgl, Antik, and LLA are some of the most famous Lisp libraries for AI and machine learning.
Haskell
Haskell is primarily for educational and research purposes and industrial applications. Haskell features lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism. It has also influenced many programming languages and is an excellent language for developing and researching AI.
Rust
Rust is a high-level, multi-paradigm, general-purpose programming language. It provides performance, speed, security, and concurrency that is safe. Firefox, Dropbox, Yelp, npm, Cloudfare, Azure, Deno, Discord, Polkadot, and many other companies use it. As an alternative to C/C++, Google announced support for Rust in the Android Open Source Project.
Scala
Scala accelerates, simplifies, and significantly increases the productivity of the coding process. Scaladex’s Scala libraries and resources index enables developers to create high-quality applications. Furthermore, Scala is one of the best options for developing AI, and it has impressed developers in that field.
Prolog
Prolog is an acronym for “Programming in Logic.” It’s a declarative language with applications in symbolic reasoning, database applications, language parsing, and natural language processing. Prolog’s potential, however, was overlooked and rarely used in commercial applications. However, IBM Watson and some NLP applications still use Prolog.
Conclusion
AI keeps pushing the envelope with data, tools, and programming languages. These are the best programming languages for AI projects. It’s simply a matter of selecting a project that best meets your needs. With a basic understanding of the project, you can easily select the most appropriate language.
Source: indiaai.gov.in
