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AI programmers today are expected to master many languages as they work in an interdisciplinary environment and not in silos. The list goes as Python, R, Java, Lisp, Prolog, Julia, etc.
Though the current generation loves Python more, a frontend developer must know JavaScript, Python, and R for machine learning applications.A lead developer for process automation at a well-renowned organization knows R, Java, Nia, and .Net and writes code for AI, machine learning, chatbots, automation systems, etc.However, larger enterprise systems continue to write in Java. PHP was used by one of my developer friends while writing a pre-Siri communication tool. R, Matlab, Weka are necessary for machine learning and data mining.
Yes, there is no single AI language to consider as the best programming language.
So, let's discuss a few popular languages that are on-trend and usage in AI projects across the globe.
1) Python:
It ranks first among all other languages for AI, ML, and data analytics. It is advisable to use when you need rapid coding.
Python is a favorite choice because:
It has a great library ecosystem. Some of the commonly used libraries are SciKit-learn to handle basic ML algorithms, Pandas for high-level data structures and analysis, Keras and TensorFlow for deep learning, Matplotlib for visualization. Keras and TensorFlow are used by most of the AI engineers today.
The learning curve is much easier for AI developers.
The Python language is flexible and it decreases the possibilities of errors, and also programmers can combine Python with other languages.
As python is platform-independent, it saves money and time used for testing on various platforms. It makes the overall platforms simple and convenient.
2) Lisp:
Lisp is the favorite language for developers to solve a problem in inductive logic projects while building AI solutions. Apart from Lisp, Prolog, Scheme, Haskell is good to go for logic-based AI projects.
Lisp is used for AI because of the following reasons:
Supports symbolic programming.
Does not distinguish data and codes which makes the language more powerful.
Extensible than other programming languages.
Read-Eval-Print Loop eases interactive programming.
It comes with a good environment like debugging, incremental compilers, etc.
3) Prolog:
Prolog comes with unique features like pattern matching and automatic backtracking. This feature is used in AI development for advanced solutions. It is a logic programming language and favored by AI programmers.
The Prolog program consists of:
Declaration of the facts of the relations.
Declaration of rules concerning relations.
Formulations of questions.
So, prolog can analyze the relationship between an act, a statement and a conditional statement, which is helpful in artificial intelligence.
4) Matlab:
It is suggested to use Matlab while implementing complex mathematical operations. Matlab provides AI capabilities like Caffe and TensorFlow. It lets you integrate AI into the complete workflow. In a way, with MATLAB, you are ready for AI even without a machine learning experience. You have the provision to use apps and try out different approaches quickly.
C++:
It is recommended by AI engineers for projects like search engines, computer games, building neural networks as it facilitates faster execution of complex algorithms. As C++ supports re-use of programs due to inheritance and data hiding, it saves time and costs.
C++ is recommended because:
It is easy to find solutions for complex AI problems
It is rich in library functions and programming tools
It supports object-oriented principles and can be used to achieve organized data
5) Java:
Java is used for neural networks and works well with search algorithms. It can be used for larger projects.
Some of the benefits Java produces include:
Easy use
Easy debugging
Package services
Graphical representation of data
Better user interaction
Sophisticated graphics and interfaces with Swing and the Standard Widget Toolkit
So, each language comes with specific features and benefits.
