Top 10 Best Programming Languages for Artificial Intelligence

Excerpt: The fact that formerly Facebook has developed revolutionary technological innovations like Meta makes it worthwhile to consider how artificial intelligence will impact software development in the future. Programming languages are notoriously versatile, each skill of great feats in the right hands.

Introduction

AI (artificial intelligence) technology also relies on them to perform properly when monitoring a system, triggering commands, displaying content, and so onwards. Artificial intelligence extends far beyond the science fiction film with Haley Joel Osment from 2001. Machines can be intelligent beings, which opens up a world of possibilities. The majority of artificial intelligence projects are oriented toward the market, according to recent research.

Utilizing artificial intelligence in marketing alone can make a profound difference. As a growing business such as yours, it should be apparent why these projects are appealing to you. However, artificial intelligence isn’t limited to that. As soon as you learn how to develop artificial intelligence, you can do anything. In this lesson, we will learn ten programming languages for tackling artificial intelligence projects:

• Python 
• C++ 
• Lisp 
• Java 
• Prolog
• R 
• Julia
• Haskell 
• Javascript 
• Scala 

Candidates who want to advance their career, Artificial Intelligence training is the best option

Keep reading to learn more about the benefits these languages can offer!

Table of contents:

• What is Artificial Intelligence?
• 10 best programming languages for AI Development
• Conslusion

What Is Artificial Intelligence?

If you consider that artificial intelligence makes for some dangerous alternate the next update realities, you’re not alone. Anyone who’s seen Terminator knows right and well that when you start sharing machine’s human traits, things go south — quick! The chance of killer robots threatening your livelihood anytime shortly is quite small. Essentially, artificial intelligence (AI) refers to intelligent machines. Genuinely, if you’ve ever performed with a digital device that didn’t know how to tell up from down or do a simple task, you’d probably completely like artificial intelligence. Artificial intelligence is already being used by you. Remember that robot voice you have on your phone? 

Using AI technology daily. In a business capability, artificial intelligence has a numeral of benefits. One example would be marketing automation tools that rely heavily on AI. Through the use of the right tools, you can create and orchestrate campaigns and collect insights for your brand. Let’s take this example as an example. Artificial intelligence is bound to appear in numerous scenarios as this era of digital transformation continues, working alongside humans and providing proactive solutions to everyday problems. 

AI development is not for the faint of the core. The ability to build artificial intelligence into your software requires a particular skillset and, on that note, an adjoining tech stack to ensure proper development.

Python 

Python stands as a high-level, general-purpose programming language. Designers cherish Python for its uncomplicated syntax and object-oriented process to code maintainability. But although Python seems pleasant, it’s well-equipped to handle large and complicated projects. Data engineering stays a frequent use case for utilizing Python, as it accomplishes machine learning. A subset of artificial intelligence, machine learning involves using algorithms to train machines. TensorFlow is Python’s most prevalent framework for AI development. It’s an open-source machine understanding library where you can train deep neural networks. 

Other pre-existing libraries and frameworks have:

• Pandas
• SciPy
• nltk
• PyTorch
• Keras
• Theano
• scikit-learn

Additionally, these technologies can be used to evaluate advanced mathematical expressions and perform natural language processing (NLP). NLP is used by smart assistants such as Google and Alexa to understand what you are saying and respond appropriately. Another amazing thing almost of Python is that it is one of the most comfortable languages to learn, despit40.e what it’s qualified for!

C++ 

It’s well known that C++ is a low-level programming language. Consequently, C++ is well adjustable for the hardware and machine side of software, but it is not as capable for the theoretical side. Nevertheless, C++’s capacity to handle high-level programming makes it perfect for managing AI models in production. Using C++, you can build neural networks from scratch and translate user code into machine language. C++ is commonly used to create machine learning libraries, some of which are notorious. For these explanations, C++ still gets a lot of engagement in the artificial intelligence world. Don’t undervalue what C++ can accomplish!

Lisp 

One of the oldest languages used in AI development is Lisp (historically stylized as LISP). The name Lisp comes from one of its original applications, list processing, which was created in 1958. John McCarthy aided the language in becoming capable of addressing artificial intelligence problems by 1962. Despite the language’s capabilities today, developers rarely begin their work with Lisp because of the language’s complicated syntax and complicated libraries. 

As a result, Lisp’s expertise could be beneficial in a variety of AI projects, including but not limited to:

• Rapid prototyping
• Dynamic object creation
• Executing data structures as programs
• Modifying programs as data
• Mandatory garbage collection

Historically, Lisp has been hailed as a language that is flexible and able to turn ideas into reality.

Java

Java is useful as a server-side language. Java’s key characteristic is that through its virtual machine, you can use it almost anywhere, on any platform. Though Android developers can use Kotlin as well, Java is Android’s native programming language. Artificial intelligence is beneficial for the application to the growing or development trends that mobile app developers are well aware of.

Therefore, both FaceApp and Google Assistant are Android apps that incorporate artificial intelligence through the Java programming language. In addition to mobile apps, Java has many machine-learning libraries for deep learning and natural language processing.

Check out the followership if you plan to utilize Java for your machine learning needs:

• Deep Java Library
• Kubeflow
• OpenNLP
• Java Machine Learning Library
• Neuroph

The language’s object-oriented design makes it even easier to work with, and it’s sure to come in handy in AI projects.

Prolog

Prolog language is a declarative language where programs are represented in terms of relations, and execution occurs by running queries over these relations. It is particularly useful for symbolic reasoning, database, and language parsing applications. Prolog is widely operated in AI today. Prolog, a portmanteau of logic programming, since 1972 this programming language. Despite its age, Prolog performs well for the right projects.

Prolog does not use coded successions but rather a basic set of facts, rules, goals, and queries. As a result, it is capable of design matching, mainly when NLP is involved. For example, the world’s first chatbot was called ELIZA and was written in Prolog. In ELIZA’s ability to assign significance to words and reorder them into a question, Prolog’s logic method played a critical role.

The basic tenets of Prolog are as observes:’

• facts: it defines as true statements
• rules: it defines as statements with additional conditions 
• goals: it defines where the submitted statements are placed given a knowledge base
• queries: it defines as how to make statements true and the final analysis of facts and rules

Incredible, these fundamentals are the crux of Prolog’s prowess. 

These days, cloud technology creates it so chatbots have a whole stock of data to access new and old knowledge, meaning chatbots are worlds more intelligent than in the time of Prolog. 

R

For budding and professional statisticians alike, R is a popular programming language. However, R is not the most suitable language for artificial intelligence. Nevertheless, it can be useful for crunching numbers. In AI development, data is critical, so when it comes to analyzing and presenting data accurately, things can get a bit mathematical. R can help you shine a light in a tunnel that otherwise seems dark. This is actually better than Python when it comes to keeping your numbers straight. 

You may want to check out the following R packages:

• Gmodels — it provides tools for model fitting
• Tm — Tm used for the framework for text mining applications
• OneR — it used to implement the One Rule Machine Learning classification

Julia

Julia is a newer language used for AI development’. However, you shouldn’t let that stop you from using it in your next AI project. 

Julia still appears on many lists as one of the best languages for artificial intelligence, despite its small community at the moment. 

Julia is popular because of its dynamic interface and captivating graphics for data visualization.’ 

You’ll better understand why it is so popular after learning about memory management, debugging, and metaprogramming.

There are many features in Julia, including direct support for C functions, a dynamic type system, parallel and distributed computing capabilities, and much more.

Julia has predefined models, algorithm support, and many packages for probabilistic programming, including:

• MLJ.jl
• Flux,jl
• Turing,jl
• Metalhead

Haskell 

Haskell programming is an excellent AI programming language. Inert evaluation and the list and LogicT monads make it comfortable to express non-deterministic algorithms, which is often the case. Unlimited data structures are great for search trees. The language’s attributes enable a compositional way of describing the algorithms. The only disadvantage is that functioning with diagrams is a bit harder at first because of purity. Haskell is manually famous for being a statically organized and purely operating programming language.

What does it mean, in overview, is that Haskell is always easygoing and expressive. As a result, conception readiness reduces the necessity of spending a great deal of time debugging errors. And it is efficient memorial management, type system, and code reusability practices, only depend on its appeal. Haskell is characterized by its laziness, which is one of its greatest virtues. Hence, Haskell only for the count’s pieces of code when important. It totally depends on you; how do you look at it? This can be either a pro or a con. In brief, Haskell’s greatest strength for AI designers is the language’s efficiency. Instead of typing, you’ll spend more time believing. 

Javascript 

JavaScript is known for its better programming way, and all the developers can use it. In javascript, the security is built-in. In this, you cannot access your files. According to this, JavaScript is faster than Python. Modern JavaScript always compiles into the machine code. It can use hardware acceleration. Although JavaScript has many of the same pros and cons as Java, it is more frequently used for dynamic and secure websites. It is preferred over Python by programmers who do not mind coding.

Interactivity is its main AI capability, which works with other source codes such as CSS and HTML seamlessly. Front-end and back-end functions can be managed, from buttons and multimedia to data storage.

In addition, JavaScript has a lot of support from programmers and communities. For more ideas, check out React.js, jQuery, and Underscore.js.

Scala

Scala has taken the Java Virtual Machine (JVM) environment and created a better solution for developing smart software. Java and JavaScript are both supported, making the coding process easier, faster, and more efficient.

A responsive language such as Scala is practical for developing distributed systems and conditioning data for future application production that focuses on AI. Due to Scala’s capability to handle complex algorithms and stream data at a considerable scale, it serves as an efficient program for operating complex material. AI paves a way into enterprise environments with large data frameworks such as Spark that’s written in Scala. Its efforts to impress programmers are paying off thanks to Scala’s high-performance functions, flexible interfaces, pattern matching, and browser tools. Scala has become one of the most popular AI development languages.

There is also the Scaladex, which contains a list of all the available Scala libraries as well as their resources.

Conclusion:

AI is not something to be taken lightly. From driving to putting appointments in your digital calendar, you use machines every day to make life easier. Depending on how easy or technical you want your process to be, you can choose from several programming languages for AI development. The system you use for the software you’re designing is another factor to take into account. A prospect like this is thrilling on a personal level.

Even more exciting is what we can expect from a business perspective. In order to build competent AI software, one of the above languages is only a minor component. Suppose your machines knew exactly what you wanted and could meet your needs without prompting? We would be delighted to know how you 10 best programming languages which is amazing to use in the future project. 

Author Bio

Meravath Raju is a Digital Marketer, and a passionate writer, who is working with MindMajix, a top global online training provider. He also holds in-depth knowledge of IT and demanding technologies such as Business Intelligence, Salesforce, Cybersecurity, Software Testing, QA, Data analytics, Project Management and ERP tools, etc.