Programming Languages For Artificial Intelligence

With all the breathless hype of transitioning science fiction to a real-world where intelligence has been researched, tested and so far developed so much giving out the technological breakthroughs and the current state-of-art  of deep learning using artificial neural networks, the field of artificial intelligence in the field of the hard-working computer geeks, engineers and researchers. While all the milestones like Deep Blue defeating the world chess champion Garry Kasparov to machines learning to identify cats to self-driving cars hitting the streets and the lavish lists of all the research and development, the major foundation is the soul of algorithms being implemented through the programming languages.  Here’s the rundown of top programming languages for artificial intelligence be it machine learning or deep learning.

1. Python

Python being an open-source programming language boasts the most leverage proven pre-built libraries and community support in the field of artificial intelligence, especially in the context of machine learning and deep learning. The major libraries are SciKit-learn to auto-handle the plethora of algorithms, NumPy, SciPy for mathematical manipulations, Matplotlib for data visualization, Pandas for high-level data structure and analysis, nltk for natural language and speech processing, Keras and TensorFlow for deep learning. These libraries are portable across platforms like UNIX, Windows, and Macintosh. Besides, it provides the simplest streamlined syntax and helps solve the less code syndrome to developers with readable and intuitive codes. 

Caption:  Python

Source: nakedsecurity

2.  LISP

Logic-based inductions and processing are one of the important paradigms of artificial intelligence, for which LISP is considered the best programming language. It supports the effective processing of symbolic information. The major advantages of LISP is it offers rapid prototyping capabilities with libraries of collection types. The dynamic creation of new objects with automatic garbage collection is also offered by LISP. During runtime, the developer can easily evaluate expressions and recompile functions or files parallelly. These beneficial features have been adopted by other programming languages today.

Caption:LISP

Source:reddit

 3. Prolog

Prolog is considered the oldest and primary programming language for artificial intelligence, which is also built for logic-based inductions and processing. It is a rule-based declarative language where facts and rules are basically stated dictating artificial intelligence. It offers pattern matching, tree-based data structuring, and automatic backtracking which are important in artificial intelligence during the implementation of state-space tree, adversarial, and other backtracking algorithms. Prolog provides a plethora of support to a logical programmer in the field of artificial intelligence.

Caption: Prolog

Source:redbubble

 4.  Java

Java being a multi-paradigm language supporting object-oriented programming and the principle of Once Written Read/Run Anywhere (WORA) as it runs on any platform without the need for recompilation, it is also used as AI programming language.  It is appropriate for NLP (Natural Language and Speech Processing), search algorithms and neural networks in the field of artificial intelligence.

Caption: Java

Source:Forbes

In case, you want to know more, Click here  Artificial Intelligence

 5. R

R is an open-source programming language as well as an environment for statistical computing and graphics, which has become a de facto programming language as it rises in popularity with the data science and machine learning communities.  In languages with larger scientific computing communities such as python, the functionalities have to be duplicated using several third-party libraries to represent data like NumPy. But in R, such functionalities have been directly built as functions. Also, the fundamental data type in R is a vector which makes all the mathematical and statistical analysis easier.  The major pitfall that lies with R is that it does not scale well with large data. So, industries like google use it as their data sandbox for experimenting with the algorithms and machine learning development. Further, if the tests are good in R, further the designs are replicated using a better appropriate language for scaling up to a larger data.

Caption: R Programming language

Source:Fiverr

LISP, prolog is the primary programming language for artificial intelligence for logic-based projects, but they have been less used as these features with additional new development has incorporated with new languages like JULIA, Haskell, etc. Beside R is being used by statisticians and mathematicians widely, but still a large-scale artificial product is not being produced in R. Beside C++, java have been widely used in large scale development after the tests are fruitful in R. All the learners find it easier to go the way down python typically while running down the usage of languages over AI.