Ludwig Wittgenstein once very wisely put it, “The limits of my language mean the limits of my world.”
It’s true. Language is the most spectacular discovery made by the human species. This incredible invention empowers us to put together our thoughts, and interact more effectively and clearly with each other. From reasoning abstractly to working our way through complex ideas around our existence, language is singularly the most enticing yet intellectual development in the history of mankind.
This works quite the same in software and technology, especially language artificial intelligence or language AI as it is more commonly called. Since its inception, the primary goal of artificial intelligence has been to build machines that are capable of understanding human language and responding with poise, emotions, and sophistication. In the last few years, researchers have passionately achieved many mind-boggling breakthroughs in language AI, or natural language processing to be more precise. If you too wish to learn the AI language and pursue a career in AI, the first and foremost thing you need to know is how to code.
Recommended: Kaspersky Has Launched a New Online Cybersecurity Training ‘Reverse Engineering 101’
With this article, we are focusing on easy-to-read, flexible, and most common programming languages for AI learners.
Python
Highly interpreted and most widely used, Python is embedded with an extremely easy-to-learn syntax that greatly emphasizes readability, therefore reducing the maintenance cost of the program. As Python supports modules and packages, it further encourages code reuse and program modularity. Python comes with an extensive standard library which is available in both binaries as well as source form. Below are the key highlights of why python works best with AI.
- Python has a host of libraries like Pandas that work exclusively for data analysis, and manipulation, making it easier to work with data.
- Python also has a separate set of libraries, such as TensorFlow and Keras, dedicated to machine learning.
- Besides data and machine learning, Python also comes with robust scientific libraries like scikit-learn and NumPy.
- With projects like MicroPython, CircuitPython, and Raspberry Pi, Python can be used to program microcontrollers.
C++ and C#
Designed keeping in mind profitability and flexibility of use in mind, C++ is a general-purpose programming language that is mainly prevalent in image processing, graphics, and scientific computing. Microsoft’s C #, which has a similar syntax, is its main competitor. Both programs are object-oriented and support data abstraction with the help of objects, classes, and inheritance. C++ can be converted directly into machine code and can be used on your computer while C# runs on your virtual machine, enabling you to use it across different platforms. C++ empowers you with memory management better as compared to C# which implies there’s no space for any kind of error. Here are a few reasons why C++ and C# are good options for beginners.
- Both languages are very popularly used in game development and several games use AI.
- C++ and C# are high-performance languages as they are combined languages and offer reduced when needed.
- C# is bestowed with an array of available libraries and tool support from Visual Support.
Recommended: Introducing the GelSight Mini: A Human Resolution Tactile Sensor for Engineers and Scientists
Java
Dynamic yet versatile, Java allows users to create different applications. With an object-oriented feature, Java supports polymorphism, inheritance, and encapsulation. Ingrained with bytecode, Java is capable of running on any platform, any machine, that comes with a Java virtual machine. It has a massive large library and with its exceptional handling, Java can manage errors and exceptional conditions more elegantly. Java is quick and reliable and with its enormous tooling support, coders can easily develop complex AI apps more efficiently. Since it has been a part of various mission-critical apps, this one is battle tested.
Julia
Launched in 2012, Julia is a high-powered language that works better for technical computing and is extremely easy to learn and adapt to. Julia does not require wrappers or interface code as it can C and Fortran libraries directly. Julia can be used in both distributed and parallel computing. It has a huge standard library which includes machine learning, differential equations, and optimization. As Julia is fast and flexible in nature, it is easier to experiment with different models more swiftly.
Recommended: Variscite Simplifies AI/ML and Multimedia at the Edge with Python API for System on Modules
Conclusion
Ai revolution has arrived and conquered our lives and businesses and with its innovations, it is constantly creating better opportunities for brands as well as individuals. To select the perfect AI programming language, wade your way through different languages which typically depend on a variety of factors like the platform, task along with your preference and expertise.
[To share your insights with us, please write to sghosh@martechseries.com].
Comments are closed.