Languages used in Artificial Intelligence

Artificial Intelligence has become an important part of human life as we are now highly dependent on machines. Artificial Intelligence is a very important technology to develop and build new computer programs and systems, which can be used to simulate various intelligence processes like learning, reasoning, etc.

• Python
• R
• Lisp
• Java
• C++
• Julia
• Prolog

1. Python

Python is one of the most powerful and easy programming languages that anyone can start to learn. Python is initially developed in the early stage of 1991. Most of the developers and programmers choose Python as their favourite programming language for developing Artificial Intelligence solutions. Python is worldwide popular among all developers and experts because it has more career opportunities than any other programming language.

Python also comes with some default sets of standards libraries and also provides better community support to its users. Further, Python is a platform-independent language and also provides an extensive framework for Deep Learning, Machine Learning, and Artificial Intelligence.

Python is also a portable language as it is used on various platforms such as Linux, Windows, Mac OS, and UNIX.

Features of Python

• It is easy to learn than any other programming language.
• It is also a dynamically-typed language.
• Python is an Object-oriented language.
• It provides extensive community support and a framework for ML and DL.
• Open-source.
• Large standard sets of libraries.
• Interpreted language.

Python is an ideal programming language used for Machine Language, Natural Processing Language (NLP), and Neural networks, etc. Due to the flexible nature of Python, it can be used for AI development. It contains various pre-existing libraries such as Pandas, SciPy and nltk, etc. Further, Python also contains simple syntax and easy coding, which makes Python the first choice of AI developers and programmers.

There are some standard Libraries in Python used for Artificial Intelligence as follows:

1. Tensor Flow Python
2. Keras Python
3. Theano Python
4. Scikit-Learn Python
5. PyTorch Python
6. NumPy Python
7. Python Pandas
8. Seaborn Python

2. Java

Java is also the most widely used programming language by all developers and programmers to develop machine learning solutions and enterprise development. Similar to Python, Java is also a platform-independent language as it can also be easily implemented on various platforms. Further, Java is an object-oriented and scalable programming language. Java allows virtual machine technology that helps to create a single version of the app and provides support to your business. The best thing about Java is once it is written and compiled on one platform, then you do not need to compile it again and again. This is known as WORA (Once Written Read/Run Anywhere) principle.

Features of Java

Java has so many features which make Java best in industry and to develop artificial intelligence applications:

• Portability
• Cross-platform.
• Easy to learn and use.
• Easy-to-code Algorithms.
• Built-in garbage collector.
• Swing and Standard Widget Toolkit.
• Simplified work with large-scale projects.
• Better user interaction.
• Easy to debug.

3. Prolog

Prolog is one of the oldest programming languages used for Artificial Intelligence solutions. Prolog stands for "Programming in Logic", which was developed by French scientist Alain Colmerauer in 1970.

For AI programming in Prolog, developers need to define the rules, facts, and the end goal. After defining these three, the prolog tries to discover the connection between them. Programming in AI using Prolog is different and has several advantages and disadvantages.

It may seem like a bizarre language to learn for those programmers who are from a C++ background.

Prolog may not be a great programming language to build something big, but it's a great language to study and think about problems in more logical ways rather than procedural.

Features of Prolog

• Supports basic mechanisms such as
• Pattern Matching,
• Tree-based data structuring, and
• Automatic backtracking.
• Prolog is a declarative language rather than imperative.

4. Lisp

Lisp has been around for a very long time and has been widely used for scientific research in the fields of natural languages, theorem proofs, and to solve artificial intelligence problems. Lisp was originally created as a practical mathematical notation for programs but eventually became a top choice of developers in the field of AI.

Although Lisp programming language is the second oldest language after Fortran, it is still being used because of its crucial features. The inventor of LISP programming was John McCarthy, who coined the term Artificial Intelligence.

LISP is one of the most efficient programming languages for solving specific problems. Currently, it is mainly used for machine learning and inductive logic problems. It has also influenced the creation of other programming languages for AI, and some worth examples are R and Julia.

However, though being so flexible, it has various deficiencies, such as lack of well-known libraries, not so-human-friendly syntax, etc. Due to this reason, it is not preferred by the programmers.

Features of LISP

• The program can be easily modified, similar to data.
• Make use of recursion for control structure rather than iteration.
• Garbage Collection is necessary.
• We can easily execute data structures as programs.
• An object can be created dynamically.

5. R

R is one of the great languages for statistical processing in programming. However, R supports free, open-source programming language for data analysis purposes. It may not be the perfect language for AI, but it provides great performance while dealing with large numbers.

Some inbuilt features such as built-in functional programming, object-oriented nature, and vectorial computation make it a worthwhile programming language for AI.

R contains several packages that are specially designed for AI, which are:

• gmodels - This package provides different tools for the model fitting task.
• TM - It is a great framework that is used for text mining applications.
• RODBC - It is an ODBC interface.
• OneR - This package is used to implement the One Rule Machine Learning classification algorithm.

Features of R programming

• R is an open-source programming language, which is free of cost, and also you can add packages for other functionalities.
• R provides strong & interactive graphics capability to users.
• It enables you to perform complex statistical calculations.
• It is widely used in machine learning and AI due to its high-performance capabilities.

6. Julia

Julia is one of the newer languages on the list and was created to focus on performance computing in scientific and technical fields. Julia includes several features that directly apply to AI programming.

Julia is a comparatively new language, which is mainly suited for numerical analysis and computational science. It contains several features that can be very helpful in AI programming.

Features of Julia

• Common numeric data types.
• Arbitrary precision values.
• Robust mathematical functions.
• Tuples, dictionaries, and code introspection.
• Built-in package manager.
• Dynamic type system.
• Ability to work for both parallel and distributed computing.
• Macros and metaprogramming capabilities.
• Support for multiple dispatches.
• Support for C functions.

7. C++

C++ language has been present for so long around, but still being a top and popular programming language among developers. It provides better handling for AI models while developing.

Although C++ may not be the first choice of developers for AI programming, various machine learning and deep learning libraries are written in the C++ language.

Features of C++

• C++ is one of the fastest languages, and it can be used in statistical techniques.
• It can be used with ML algorithms for fast execution.
• Most of the libraries and packages available for Machine learning and AI are written in C++.
• It is a user friendly and simple language.