Best Programming Languages for AI

Best Programming Languages for AI

In recent times, Artificial Intelligence has become one of the most crucial & important technologies on which every tech giant and startups are focusing. It is a very vast topic lying in the very range that begins from basic calculators or self-steering technology to the industrial robots. This technology is really defining the future technology.

“According to an American Business Tycoon and Investor, ‘AI will make the world’s first trillionaire’. The person who will masters the AI plus all its derivatives & implements would be the first trillionaire.”

The programming of Artificial Intelligence has raised the efficiency of technology which has given a lot of benefits to different company operations and also in the life of people. In addition to making things automatic,  it has brought the next level of smart technology in industries of every sector such as Automobiles, Textiles, IT Sector, Mobiles & Computers, and Communications.

If you are a newcomer who is baffled in choosing the coding language that you should learn for your next project or to make a career in, you are reading the right article. Here we have mentioned, the best languages for AI development.

Python

Python begins this list by coming at the first position in AI development because of its simplicity. The syntax of python is very easy to implement and learn. “It was developed in the 1990s and become of the fastest growing programming languages”. The reason is its scalability, easy learning, and adaptability. It mainly focuses on Rapid Application Development. There are hundreds of libraries in Python by which you can make any type of project, whether it is a web application, mobile app, data science project or artificial intelligence.

Examples: Numpy library of Python is for scientific computations, Pybrain is for machine learning and many others.

The development time of Python is also very less as compared to other languages than Java, Ruby or C++.

In addition, to implement any idea in Python, you just need to type 30-40 lines of code.

R

The second comes in the list is R language. If any of your future projects require data science or make use of data then R is the foremost language to learn. However, its speed is not its prominent advantage but it does almost every AI-related task.

There are several packages in R which you can use in the field of machine learning. These packages are effective for solving business-related problems and makes the implementation of algorithms easy. Also, the plot design by R is of good quality plus the mathematical symbols and formulae.

“R is very reliable for creating clean datasets and splits the Big data sets into training sets and tests sets.”

Prolog

Prolog is having good value in Artificial Intelligence for its pre-designed search mechanism, recursive nature, abstraction, nondeterminism, backtracking mechanism, and pattern matching.

It is a logic a semantic inference engine, plus computational linguistics and AI. It is quite flexible thus, can use for programming require the non-numerical procedure, theorem proving and NLP (Natural Language Processing).

Prolog is best for structure objects programming and to represent the relations between them. For e.g. in Prolog, you can easily represent the spatial relationships between objects, like a blue square behind the green one, and to represent the general rules, like if block A is closer to a person than B and B is closer than C, then A is also closer than C.

LISP

Dr. John McCarthy, who coined the term Artificial Intelligence, created the LISP in 1958, which is currently the oldest language in IT development.

When it was developed it is used for Lambda Calculus Calculation, but it evolves a lot. Ideas such as recursion, dynamic typing, Automatic storage management, High-order functions, self-hosting compiler, and tree data structure.

LISP is very important in Artificial Intelligence Development because it supports symbol computation programming very well.

Java

Java provides many benefits such as simplified work with large projects, searching algorithms, neural networks, and genetic programming. “It is easy to use, easy debugging, package services, plus the graphical representation of data.” It includes the incorporation of Swing and Standard Widget Toolkit.

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