10 Programming Languages You Need to Know For Artificial Intelligence Development

“Artificial intelligence” as a term in a field of academic discipline was first coined by John McCarthy, a computer scientist, in 1956. Since the late 1950s, AI has seen many stages, some colored by disappointment and a fall in the tech community, others colored by renewed interest and a rise in funding and development.

Now, especially post-pandemic, AI has become a top priority for businesses. This is because artificial intelligence offers enhanced customer experience, resilience, and reliability. Moreover, with machine learning advancements, as well as conversational AI, data science, and data analytics, companies and institutions now consider it paramount and affordable to deploy AI tools that allow them to solve problems and increase efficiency.

Building an AI solution requires two key things. The first is a clear set of requirements, and the other is the relevant selection of technologies and programming languages that make AI development practically possible and smooth. To make the selection process easy and functional, we will discuss in this article the relationship between AI and programming languages and some of the best to deploy in the advancement of your project.

Understanding AI

AI is an essential element of many software development projects that allows developers to create software that can perceive, learn, reason, and solve problems. You can liken it to the way the human brain operates or the way that our minds function. As a result, learning how to apply artificial intelligence is critical for many job roles and the general advancement of computer technology, especially for those interested in pursuing a career in programming or those who’ve already found themselves doing so.

Essentially, AI is all about intelligent machines. The best is used to create tools, orchestrate campaigns, and gather insights to improve general technological effectiveness. AI can be seen everywhere and is all around us, even in Marketing automation.

If you use digital devices, then you’ve had AI at the tips of your fingers. For example, that robot voice you speak to on your phone, make calls, send texts on your behalf, and answer your questions, is artificial intelligence.

The world has been changed in a lot of ways by artificial intelligence. The younger generation may not be overly noticeable because it may feel as if AI has been around forever. However, most algorithms have only come around in recent decades, from humanoid robots to personalized social and Internet recommendations. These are examples of the many global impacts that artificial intelligence has brought along in its wake.

And while there are many languages out there that could be deployed in advancing your artificial intelligence projects, you should know which one would do the job best. But first, you must be well versed, or at the very minimum, reasonably knowledgeable on artificial intelligence and its relationship with programming languages.

AI and Programming Languages

Artificial intelligence is not a universal, one-size-fits-all solution. Your choice of AI programming language will be decided by a lot of things, including and especially the exact results you hope to get out of your project and the route through which you hope to achieve this result.

For example, if your project involves extensive data analysis, you might look to the R programming language. This is because R is designed to process big numbers with ease.

In the same breath, R is not suited for deploying machine learning models in production. In such an instance, Python would be a much wiser choice.

The development of artificial intelligence goes hand in hand with programming. One cannot be said to advance without the other. Programming or computer programming is about encoding an algorithm into a computer program by using programming languages (the language that the computer understands and then proceeds to execute).

1. All programming languages contain a set of instructions that a computer uses to execute a set of tasks. Essentially, programming is a complex, technical, and continuous process. You must design an algorithm, code it in a programming language, and continue to design, build, and upgrade it as needed.

10 Programming Languages For Artificial Intelligence Development

In recent years, businesses and individuals have inclined more toward AI development. With benefits like enhanced customer experience, smart decision-making, automation, minimum errors, and data analytics, AI development seems to be a perfect choice.

However, after you have made a choice to utilize AI in your job, business, project, and so on, there is another decision to make. That is, choosing the best programming language for AI development.

When it comes to artificial intelligence programming, each and every programming language has unique functions to offer. For example, some do better with natural language processing, while others are designed to execute the numerical analysis. Below, we outline ten of the best programming languages suitable for AI development.

Java

Unarguably, Java is one of the most widely used programming languages in the world. It is also a cross-platform programming language that boasts many high-powered functions. It is great for maintaining, charts easy usability, and offers the function of fluid debugging. It also gives room for AI-driven projects to be deployed across many devices.

Java has a built-in collector. This allows it to erase dead weight and improves graphics and visuals. Its other features include a widget toolkit (graphical and standard) and Swing. It also offers simple syntax. With a proven track record in software development, mobile app development, and now even AI development, Java has won developers the world over.

Java programming language is well known for its scalability. It shows platform independence and has a faster and easier productive coding process. It also shows the presence of JVM, which allows it to run on all AI platforms.

This programming language is utilized in android application development, AI and machine learning apps, neural networks, search algorithms, data analysis, and many more. Companies like Google, Netflix, Uber, Spotify, and Slack use Java to run their applications. Java also boasts good visuals, making graphics in AI relatively easy.

Easy to implement on many platforms, Java is loved by many. It has a rich and active community that allows developers to learn from each other and think up new AI solutions faster.

Python

Python is the ideal coding language used for machine learning, NLP, and neural network connections. In addition, it can be utilized in instances such as entry-level AI development.

This is because Python is flexible and comes preloaded with pre-existing libraries like Pandas, SciPy, and nltk. In addition, Python language is lauded for its simple syntax and minimal codes.

An independent and flexible language, Python is compatible with multiple platforms and with minimum tweaks too. Moreover, developers cherish Python for its object-oriented approach to code maintainability. And while Python might be friendly and easy to use, it is also well-equipped to handle large and complex projects.

Another great thing about Python is that it is one of the easiest languages to learn, in spite of what it’s capable of. Other notable functions of Python are that it provides a specialized range of libraries such as Keras, Pytorch, Scikit-learn, MXNet, Pybrain, and TensorFlow that are used in artificial intelligence apps. Python also has rich text processing tools and allows scripting with modular architecture.

Also, this particular programming language shows platform independence and an extensive framework for deep learning and machine learning, along with user-friendliness. With Python, your code is guaranteed to be coherent, readable, and concise. It is also guaranteed to save time and money.

LISP

LISP (short for “list processing”) was created in 1958 by John McCarthy. Flexible and adaptable, Lisp can be effectively used for machine learning. It is also known for rapid prototyping and the dynamic creation of new objects.

LISP first appeared in the 60s and has long been one of the main tools of AI researchers. In the 80s, it was used in solving applied problems. Its creator, John McCarthy, was a central person in the advancement of the AI field.

The structure of Lisp is simple and consistent, which allows for writing readable and well-ordered code. LISP can also be used in building prototypes, creating dynamic objects, and expanding the possibilities of character processing.

The first language developed for AI and the second oldest programming language after Fortran, Lisp is a trailblazer in computer science and in integrating AI into technology. Many ideas are first ordered into Lisp. Examples of this include recursion, tree data structures, self-hosting computers, higher-order functions, and automatic storage management. List processing’s most regarded learning libraries for AI and machine learning are CLML and Antik.

JavaScript

Javascript is especially great for AI development and for machine learning. This is because Javascript has quick and great development frameworks, along with decent libraries. Essentially, JavaScript can be said to have been built for security.

AI engineers use JavaScript to integrate AI software into the Internet. An AI Java application is far more intelligent than traditional web applications, like search algorithms.

Easy to navigate and object-oriented, Javascript is used to develop dynamic web applications. Java and Javascript are quite similar in terms of their names, syntax, and libraries. When used along with Javascript, Java is very easy to implement.

Scala

Scala (“scalable language”) is a multi-paradigm programming language. Scala language includes features of functional programming and object-oriented programming. A statically typed language, Scala’s source code is compiled into bytecode and executed by Java virtual machine (JVM).

Scala can also use all Java classes and allows you to create your own custom class. Essentially providing a better approach for creating intelligent software, Scala is compatible with Java and JavaScript and makes coding more efficient, faster, and easier.

Boasting powerful features such as high-performance functions, pattern matching, browser tools, and customizable interfaces, Scala is currently regarded as one of the best languages for AI research.

Julia

Although not as popular as Python or R, Julia was made to match the functionality of Python, MATLAB, and R, along with the execution speed of C++ and Java. In terms of AI, Julia is best for deep learning after Python and is best for the quick execution of basic math and science.

Julia is greatly suited for the scientific computing domain. This is because that is often its primary focus. Because of these computing capabilities, Julia is scalable and faster than a lot of programming languages, including R and even Python.

Haskell

Haskell’s inventor, Haskell Curry, was a logician, and this language is named after him. In use for three decades and counting, Haskell is a purely functional programming language. It boasts expressiveness, a strong type system, and elegant syntax.

Haskell’s design means that it can be lazy. Essentially, what this means is that it only evaluates expressions when necessary. Hence, this enhances performance and gives room for more concise code.

One of the key features of Haskell is its type system. This is because it allows for type inference and provides strong guarantees about code utility. Other key features include lazy evaluation, as previously stated, strong static typing, which allows all errors to be caught at compile time; type inference; and functional programming that emphasizes the use of functions to perform operations rather than relying on traditional programming techniques.

Notably, high-order functions are backed by Haskell. These are employed to make your codes more ordered and singular and to channel off patterns that grow too common. In addition, it boasts a rich and vast collection of learning libraries and tools, including the Glasgow Haskell Compiler. This is used by developers to arrange Haskell code. It parallels the code and optimizes it. Haskell has been used in a variety of applications, including web development, data analysis, and scientific computing.

C++

C++ is an extension of the C programming language. It is a general-purpose popular programming language created by Bjarne Stroustrup.

It consists of object-oriented, generic, and functional features. Companies like Google, Lyft, Walmart, and Telegram use this language.

If you are time-pressed, this language is the right choice for your project because it is known for its ability to accelerate the development process. A great choice for apps, high-performance apps, games, and various devices, C++ is rated among the most well-known programming languages after Python, C, and Java. Some AI learning libraries for this programming language include TensorFlow, Caffe, FANN, and OpenNN.

Additionally, C++ responds much faster than other languages and plays an important role in algorithms and statistics. The popularity of this language is due to its cost and time-saving features. It is the best choice for a time-sensitive project. This is because it can hasten development processes.

C++ can easily be counted among the most flexible programming languages in the world. When employed in the use of developing search engines, for example, it improves rankings and truncates response time.

Neural networks are also built with C++ language. Programmers appreciate their ability to perform very fast calculations, which is essential in developing any AI. It is a high-control, efficient language, standing out among most others.

Furthermore, it especially makes room for responsiveness and quicker installation. Because of its inimitable functions and well-powered capabilities, C++ is preferred over almost half of the programming languages used in the artificial intelligence development community.

R

The R programming language was created in 1993 by programmers Ross Ihaka and Robert Gentleman. Its initial intention was to implement a language that focuses on statistical computation and graphical modeling. R is used by geneticists, statisticians, economists, biologists, agronomists, and richly in the business world. Now, it is also used to develop AI algorithms. R has also become the best choice for developers who wish to use statistical models for machine learning.

A statistical programming language, R, is one of the most suitable choices for projects where you need statistical computations. It supports learning libraries like TensorFlow and Keras. The language is adopted by many industries like life sciences, education, pharmaceuticals, finance, and telecommunication and boasts utilization by tech giants such as Uber, Microsoft, Google, and Airbnb.

R includes user-created packages like graphical devices, tools, import/export capabilities, statistical techniques, etc. With built-in graphic and data modeling support, R programming language allows developers to work on deep learning without much hassle.

Prolog

Prolog (“programming in logic”) is an old programming language created in the 1970s. It has its own model for working with data that allows for creating databases fast. In addition, Prolog boasts a very logical structure.

In fact, it is known as a logical programming language, as obviously implied in the name. This makes it great for classical machine-learning tasks.

Prolog can be found in medical software and logic-based expert decision-making systems. It introduces you to a separate world of its own, completely designed by logic.

Prolog has an unusual way of approaching an AI solution. First, it requires important factors from your own corner, such as rules, facts, and the desired result. Once these requirements are provided, Prolog proceeds to figure out the link between the three and then designs an AI solution.

Flexible and surprisingly powerful, Prolog’s efficiency proves high in terms of backtracking and data structuring. It is also applied in the use of academic and research purposes for language parsing applications, symbolic reasoning, chatbots, database, graphical user interfaces (GUI), voice assistants, and natural language processing.

Conclusion

Now more than ever, more companies are looking to integrate AI into their operations. Deciding which language suits you best depends on many factors, from what the project encompasses to your background and level of comfort with these languages.

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FAQs

Why do we need Artificial Intelligence?

The goal of Artificial intelligence is to create intelligent machines that can mimic human behavior. We need AI for today’s world to solve complex problems and make our lives more smoothly by automating routine work, saving manpower, and performing many other tasks.

What are the programming languages used for Artificial Intelligence?

Some of the programming languages commonly used for artificial intelligence include Python, R, Lisp, Java, Julia, and Prolog.

What is the difference between strong artificial intelligence and weak artificial intelligence?

Weak AI uses narrow applications with very limited scope. It is only good at specific tasks, and it uses supervised and unsupervised learning to process data. An example of weak AI is Android’s Alexa and Apple’s Siri.

Strong AI, on the other hand, is widely applied with a vast scope. It boasts incredible human-level intelligence and uses clustering and association to process data. An example of strong AI is advanced robotics.

What are some types of AI?

There are many different types of AI. Some include Reactive Machines AI, Limited Memory AI (this is employed in the use of self-driving cars), Theory of Mind AI (this has the ability to understand emotions, people, and other tangible things in the real world), Self-Aware AI (this artificial intelligence possesses human-like consciousness and reactions), Artificial Narrow Intelligence or ANI (this is used in building virtual assistants), Artificial General Intelligence or AGI, and Artificial Superhuman Intelligence also known as ASI, which is equipped with the ability to perform all the regular functions that a human being can do. An example of ASI is the Alpha 2, the first humanoid robot invented.

How important is a programming language to the advancement of AI?

Programming languages, like human languages, are defined through the use of semantic and syntactic regulations to determine the structure, meaning, relevance, and understanding. Programming languages are used to facilitate communication about the task of organizing and manipulating information and expressing algorithms precisely.

The reason that programming is so important is that it directs a computer to complete these commands, so people do not have to perform them repeatedly. Instead, the software can do it automatically and accurately.

Before deciding the best programming language to learn, you must take into consideration what domain piques your interest, acquire the relevant skills, and then opt for one among some of the most popular programming languages that the world has to offer.

If you are thinking of starting a new AI&ML project, contact us now!