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10 Most Important Programming Language for AI

Artificial intelligence or AI is a branch of computer science that deals with building smart machines capable of performing tasks that require human intelligence. AI studies of rational agents to make decisions related to a person, firm, machine or software. Considering the past and present perceptual inputs of an agent at a particular instant, AI carries out any task with the best outcome possible.

Artificial intelligence has a wide range of applications. It has helped to achieve digital transformation and automation easily for industries. It is used to provide best business solutions with maximum precision. It helps to increase efficiency, agility, and reduce customer service response time and costs.

AI is a potential business developer and can amplify human intelligence in the future. For this reason, it becomes important to understand about Artificial Intelligence and its basic mechanisms for any industry to run its large scale projects or day to day business operations efficiently. It is also important to have knowledge about at least one programming language for running the AI systems.

Programming Languages in AI

Programming languages are a basic necessity for Artificial Intelligence to function properly when monitoring a system, triggering commands, displaying content etc. Hence, it is important that the programmers know about the best programming languages for AI. The popular programming languages that can be used in Artificial Intelligence include Python, Java, C++, R, Prolog, Lisp, Haskell, Wolfram, Smalltalk, and Rust.

Each of the programming languages have their own set of advantages and disadvantages. One can select one of these popular languages based on the specific features it provides. Sometimes, AI development may also use a combination of languages and tools to develop a solution for fulfilling different objectives and requirements.

The ten programming languages that are backed by and given proper credit by a Pharo community are as follows:

1. Python

Python is the easiest and the most ideal coding language used in machine learning, Natural Language Processing (NLP) and Neural Network connections. It is an interpreted, high-level, and general-purpose programming language. level

Application of Python:

It is useful in machine learning apps, data science applications, web apps, desktop apps, networking apps, and scientific computing. It is an important artificial intelligence programming language that is used in deep learning, AI, network security, numerical data processing, and scientific calculations. It is a language of choice for companies like Google, NASA, Amazon, Instagram, Reddit, JP Morgan Chase, Intel, IBM, Netflix, Facebook, Pinterest for running their businesses.

Characteristics:

PROS:

It provides specialized range of libraries such as Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow that are used in AI apps.
It has rich text processing tools with simple syntax.
It shows platform independence and extensive framework for Deep Learning and Machine Learning along with user-friendliness and flexibility.
It allows scripting with modular architecture.
Code in python is readable, concise and clear, which save time as well as money.

CONS:

It is prone to runtime-errors and hence code needs to be rechecked again and again.
It is difficult to access databases in case of Python.
It is not much memory- efficient in nature.

2. Java

Java, one of the top programming languages, is a high-level, class-based, and object-oriented programming language that uses write once, run anywhere (WORA) principle. This popular language was developed by James Gosling at Sun Microsystems in 1995.

Application of Java:

It s used in client-server web applications, Android apps, AI and machine learning apps, search algorithms, multi-robot systems, server-side programming, and neural networks. Companies like Google, Netflix, Uber, Airbnb, Instagram, Spotify, Amazon, Slack, Instagram use Java to run their applications.

Charaterictics:

PROS:

Java is scalable and is known for its scalability.
It shows platform independence.
Java has an easier, faster and more productive coding process.
It shows presence of JVM that allows Java to run on al platforms on which JVM is installed without recompilation.
AI libraries and AI-focused frameworks used by Java include Neuroph, Apache OpenNLP, Java Machine Learning Library, Deep Java Library, MLlib, etc.
It provides good visuals and hence, graphics in AI is relatively easy in Java.

CONS:

On of the biggest disadvantage in case of Java is its slow processing speed, making it less suitable than others for AI processes.
It takes up a lot of memory space.

3. C++

C++, an extension of C programming language, is a general-purpose popular programming language created by Bjarne Stroustrup which consists of object-oriented, generic, and functional features. Companies like Google, Lyft, Walmart, Accenture, Twitch, Telegram use this language.

Application of C++:

C++ is used in high-performance applications, mathematical functions, graphics-centric apps, games, embedded devices, and faster calculations and helps in developing operating systems, games, browsers, competitive programming, embedded systems, and AI apps.

Characteristics:

PROS:

Popular libraries for C++ consist of TensorFlow, Caffe, Microsoft Cognitive Toolkit (CNTK), mlpack Library, DyNet, FANN, OpenNN, Shogun that can be used for AI-ML.
It is cost-efficient.
Its greatest benefit is the fact that it has a very high speed.

CONS:

It has a complex syntax.
It has a low-level memory control feature.
It has security issues.

4. R Programming Language

R is a combination of S programming language with lexical scoping semantics. It is one of the easiest languages and is suitable for Artificial Intelligence. It is suitable for many domains like biology, sociology, finance, accounting, medicine, telecom, education, life science, pharmaceuticals, etc.

Application of R Programming Language:

It is used for data analysis and statistical computing and by data miners and statisticians for statistical software and numerical analysis. Companies like Facebook, Google, Twitter, Uber, Microsoft, Airbnb use this language.

Characteristics:

PROS:

It utilizes deep learning library functions such as TensorFlow, Keras, and MXNet.
Packages can be created by users for statistical techniques, import/export capabilities, tools, and graphical devices.
It has built-in support for data modeling and graphics which is useful to the programmers for building critical deep learning models easily.

CONS:

It needs to be paired with other reliable tools to produce well-rounded software and a productive workflow for a business.
It is a tricky language to learn.
It has poor security system and low support conditions.

5. Prolog:

Prolog stands for 'Programming in Logic' which is a declarative language used in AI and computational linguistics. It was developed in 1972 by Alain Colmerauer and Phillippe Roussel on the basis of a logical formula of a rule-like form.

Application of Prolog:

It is used in academic and research purposes for symbolic reasoning, database, language parsing applications, chatbots, voice assistants, and graphical user interfaces (GUI) and natural language processing. Its application includes expert systems, theorem proving, type systems, and automated planning. IBM Watson and some NLP applications use it.

Characteristics:

PROS:

It is flexible and powerful.
Prolog requires three important factors among side, rules, facts to provide the desired result.
It has high efficiency in terms of backtracking and data structuring.

CONS:

It has a complicated syntax.
It does not provide good visuals and hence, adding graphics in AL is difficult.
It is not versatile and is a little difficult to use.

6. LISP

Lisp is a pioneer in computer science and the first to be used for Artificial Intelligence that was developed by John McCarthy in 1958. It was originally a practical mathematical notation for computer programs.

Characteristics:

PROS:

It allows several features like rapid prototyping, garbage collection, dynamic object creation, flexibility, information process capability, etc. Hence, it allows easy dynamic creation of objects with automatic garbage collection.
It can work in many dialects like Racket, Scheme, Clojure, and Common Lisp.
It uses Read-Eval-Print-Loop to ease interactive programming and shows Great symbolic information processing capabilities.
Features like tree data structures, dynamic typing, recursion, higher-order functions, self-hosting computers, and automatic storage management were first implemented in this language.
Popular AI and ML libraries include CLML, mgl, Antik, and LLA.
LISP is flexible and adapts to the solution.

CONS:

It needs special configurations of software and hardware which is cumbersome to work.
It does not have good libraries for working.
The codes are hard to learn.

7. Haskell

Haskell is a safe programming language that was named after a great logician called Haskell Curry. It is a purely functional language that is good for AI development and research.

Application of Haskell:

Haskell shows teaching, research, and industrial application but its main implementation is in Glasgow Haskell Compiler (GHC).

Characteristics:

PROS:

It consists of advanced features like type classes that have further enabled type-safe operator overloading.
It shows various features like lambda expressions, pattern matching, type classes, list comprehension, and type polymorphism.
It shows flexibility and capability to handle errors and is safe.
It shows fast rectification or resolution of errors during the compilation process.
The ability to reuse code, efficient memory management and type system are all major advantages of Haskell.

CONS:

It is still undiscovered by various businesses and programmers and is not commonly used.
It is hard to learn.
Haskell gets limited support.

8. Wolfram

Wolfram is a general multi-paradigm, functional programming language developed by Wolfram research and designed by Stephen Wolfram as a part of the initial version of Mathematica in 1988 for advanced programming. It is popular as a computer algebraic language.

Characteristics:

PROS:

It shows powerful and high speed computation with advanced level programming.
It consists of 6,000 integrated, built-in functions that can be used for symbolic computation, functional programming, and rule-based programming.
It has the capability to perform arithmetic operations and natural language processing functions.
It can employ arbitrary structures and data.

CONS:

Processing of output is time-consuming.
This language is a bit difficult to understand and learn than others.

9. Smalltalk

Smalltalk is the first graphical language tool that is an object-oriented, dynamically-typed reflective in nature. It is being rapidly used in the field of AI.

Application of Smalltalk:

SmallTalk plays an important role in agile software development, rapid application development (RAD), and software design patterns and can help in GUI, font editors, desktop metaphors, and IDEs.

Characteristics:

PROS:

It is available in the form of many variants and inspired other languages like Objective-C, Java, Python, Ruby, and CLOS.
It allows debugging and code changes in an user-friendly, easy format.
It consists of several libraries that can play an important role in neural networks, NLP, image processing, generic algorithms

CONS:

It has a smaller user community.
It has limited number of libraries and space for working.

10. Rust

Rust, syntactically similar to C++, is a multi-paradigm, high-level general-purpose programming language. Originally designed by Graydon Hoare at Mozilla Research, it is vastly used in Artificial Intelligence and scientific computation because of speed, expressiveness and memory safety.

Application of Rust:

It is increasingly used in industries and large systems. Companies like Firefox, Dropbox, Yelp, npm, Cloudfare, Azure, Deno, Discord, Polkadot and Google uses Rust. Google also supported Rust by using it within Android Open Source Project as an alternative to C/C++

Characteristics:

PROS:

It allows memory safety without garbage collection and reference counting is optional in this case.
It is one of the most loved languages because of its performance, speed, safety, and safe concurrency.

CONS:

It lacks an efficient garbage collection system.
It takes more time to develop codes in Rust than others.
Compilation time is more.

Summing up

This article covers the following points:

Artificial intelligence refers to the study of rational agents to make decisions related to a person, firm, machine or software. AI has helped to achieve digital transformation and automation easily for industries. It helps in providing best business solutions with maximum precision and efficiency.
Programming languages are a basic necessity for Artificial Intelligence to function properly when monitoring a system, triggering commands, displaying content, etc.
The popular programming languages that can be used in Artificial Intelligence include Python, Java, C++, R, Prolog, Lisp, Haskell, Wolfram, Smalltalk, and Rust.

Shreeya Thakur

Sr. Associate Content Writer at Unstop

I am a biotechnologist-turned-content writer and try to add an element of science in my writings wherever possible. Apart from writing, I like to cook, read and travel.