Top 10 AI Best Programming Languages for 2024

Nowadays, artificial intelligence is becoming popular and mostly used for businesses of different classes. AI is used for different operations in companies to enhance and flourish. So, multiple software development companies have started developing AI solutions for services. To use this service, the developers in your company would need to learn some AI programming languages. You’ll need software engineers who know how to code AI using the best languages. 

In this blog, we’ll briefly describe the top programming languages for AI that will be useful in 2024.

What Programming Language Is Used For AI

There are several that can help you add AI capabilities to your project. We have put together a list of the 10 best AI programming languages.

  1. Python

Python is one of the most popular AI programming languages used for Artificial Intelligence. The large number of existing libraries and frameworks makes it a great choice for AI development. It includes well-known tools like Tensor, PyTorch, and Scikit-learn.

These tools have different uses:

  • TensorFlow is a powerful machine learning framework that is used widely to build and train deep learning models, mostly in the application of neural networks.
  • PyTorch is a deep learning framework that allows a user to build and train neural networks, mostly for assisting in research and experimentation.
  • Scikit-learn is a machine-learning library for analyzing data and building models. It can do tasks like classification, regression, clustering, and reducing dimensions.


  • Has a large collection of libraries and frameworks
  • Big and active community support
  • Code is readable and easy to maintain


  • With so many capabilities, Python has a steep learning curve
  • The syntax can be wordy, making code complex
  1. Lisp

Lisp is the second oldest programming language. It has been used for AI development for a long time. It is known for its ability to reason with symbols and its flexibility. Lisp can turn ideas into real programs easily.

Some key features of Lisp are:

  • Creating objects on the fly
  • Building prototypes quickly
  • Making programs using data structures
  • Automatic garbage collection (cleaning up unused data)

Lisp can be used for:

  • Web development with tools like Hunchentoot and Weblocks
  • Artificial Intelligence and reasoning tasks
  • Building complex business applications that use rules


  • Good for AI tasks that involve rules
  • Very flexible programming


  • Unusual syntax that takes time to learn
  • Smaller community and fewer learning resources
  1. Java

Java is one of the most popular programming languages for server-side applications. Its ability to run on different systems makes it a good choice for developing AI applications. There are well-known libraries and frameworks for AI development in Java, including Apache OpenNLP and Deeplearning4j.

Java can work with various AI libraries and frameworks, including TensorFlow.

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


  • Can run on many different platforms
  • Java’s object-oriented approach makes it easier to use
  • Widely used in business environments


  • More wordy compared to newer programming languages
  • Uses a lot of computer memory
  1. C++

C++ is a programming language known for its high performance. Its flexibility makes it well-suited for applications that require a lot of resources. C++’s low-level programming abilities make it great for handling AI models. Many libraries like TensorFlow and OpenCV provide ways to build machine learning and computer vision applications with C++.

C++ can convert user code into machine-readable code, leading to efficient and high-performing programs.

  • Different deep learning libraries are available, such as MapReduce, mlpack, and MongoDB.
  • C++ Builder provides an environment for developing applications quickly.
  • C++ can be used for AI speech recognition.


  • Highly efficient and performs well, ideal for computationally intensive AI tasks
  • Gives developers control over resource management


  • Has a steep learning curve for beginners
  • Can lead to memory errors if not handled carefully
  1. R

R is widely known for statistical computing and data analysis. It may not be the best programming language for AI, but it is good at crunching numbers. Some features like object-oriented programming, vector computations, and functional programming make R a suitable choice for Artificial Intelligence.

You might find these R packages helpful:

  • Gmodels package provides tools for fitting models.
  • Tm is a framework well-suited for text mining applications.
  • OneR algorithm is used for One Rule Machine Learning classification.


  • Designed for statistical computing, so good for data analysis and statistical modeling
  • Has powerful libraries for creating interactive visualizations
  • Can process data for AI applications


  • Not very well-supported
  • R can be slow and has a steep learning curve
  1. Julia

Julia is one of the newest programming languages for developing AI. Its dynamic interface and great data visualization graphics make it a popular choice for developers. Features like memory management, debugging, and metaprogramming also make Julia appealing. 

Some key features of Julia are:

  • Parallel and distributed computing
  • Dynamic type system
  • Support for C functions


  • High-performance numerical computing and good machine-learning support
  • Focus on ease of use for numerical and scientific computing


  • Steep learning curve
  • New language with limited community support
  1. Haskell

Haskell is a general-purpose, statically typed, and purely functional programming language. Its comprehensive abilities make it a good choice for developing AI applications.

Some key features of Haskell are:

  • Statically typed
  • Every function is mathematical and purely functional
  • No need to explicitly declare types in a program
  • Well-suited for concurrent programming due to explicit effect handling
  • Large collection of packages available


  • Emphasizes code correctness
  • Commonly used in teaching and research


  • Challenging to learn and can be confusing
  1. Prolog

Prolog is known for logic-based programming. It is associated with computational linguistics and artificial intelligence. This programming language is commonly used for symbolic reasoning and rule-based systems.

Some essential elements of Prolog:

  • Facts: Define true statements
  • Rules: Define relationships between facts
  • Variables: Represent values the interpreter can determine
  • Queries: Used to find solutions


  • Declarative language well-suited for AI development
  • Used as a foundation for AI as it is logic-based


  • Steep learning curve
  • Small developer community
  1. Scala

Scala is a modern, high-level programming language that can be used for many purposes. It supports both object-oriented and functional programming. Scala is a good choice for teaching programming to beginners.

Some core features of Scala are:

  • Focus on working well with other languages
  • Allows building safe systems by default
  • Lazy evaluation (delaying computations)
  • Pattern matching
  • Advanced type system


  • Has suitable features for AI development
  • Works well with Java and has many developers
  • Scala on JVM can work with Java code


  • Complex and challenging to learn
  • Mainly used for data processing and distributed computing
  1. JavaScript

JavaScript is among one of the popular computer languages used to add interactive aspects to web pages. With the advent of Node.js, it became useful on the server side for scripting and the creation of many applications, including AI applications.

Some key features of JavaScript include:

  • Event-driven and asynchronous programming
  • Dynamic typing
  • Support for object-oriented and functional programming styles
  • Large ecosystem of libraries and frameworks (e.g., TensorFlow.js, Brain.js)


  • Versatile language suitable for web development, server-side scripting, and AI applications
  • Easy to learn and has a large developer community
  • Runs on various platforms (browsers, servers, devices) with Node.js


  • Can be challenging to write and maintain complex applications
  • Performance limitations compared to lower-level languages
  • Security concerns if not used carefully (e.g., cross-site scripting)


So, choosing the right artificial intelligence coding languages is important for your project needs, right? Well, the developer should keep in mind the project details or the type of software development before choosing the AI coding language.

Now, in this blog, we listed 10 AI coding languages, their features, advantages, and disadvantages. And this can ideally help you make the best choice for your project.

But wait, there’s more! If you know your project requirements, contact us to get custom artificial intelligence development services with suitable AI coding language for your project. 

8 Important NLP Methods to Get Useful Information from Data

Understanding data can often feel like solving a difficult puzzle. But imagine having a special tool that makes it easy! That’s where Natural Language Processing techniques (NLP) come in. It’s giving computers the amazing ability to understand human language naturally. 

Did you know that NLP methods are used in more than half of all AI applications today? The fact shows how important NLP is in turning raw data into useful information. With NLP, it’s as if computers gain a superpower, allowing them to understand the nuances of human language, unlocking a wealth of information hidden in text data. 

In this blog, we will be dealing with the 8 important NLP methods. Here is where these core methods begin to unfold the true potential of your data into valuable insights and informed decision-making. So, get ready to unlock the world of NLP and see for yourself how it can change the game in the way you analyze data.

What is NLP?

Natural Language Processing is a part of Artificial Intelligence and is involved with governing the way computer interaction and human language are related. It gives the computer the ability to understand, interpret, and generate human language in a useful and sensible manner. NLP is in the business of transforming unstructured information, especially text, into structured and actionable data.

NLP techniques are very essential today in organizations that largely depend on data. This growth in digital content has made organizations have huge amounts of unstructured data. NLP is important in deriving insights from the data, helping in making better decisions, improving customer experience, and increasingly enhancing operations in efficiency.

8 NLP Techniques

  1. Tokenization

The process of tokenizing text involves dividing it up into smaller units, like words or phrases. Tokens are the smaller versions of these units. Further text analysis can be carried out by building a base on the tokens themselves. Tokenization thus breaks down the text into bite-sized portions that make it easier to comprehend the structure and meaning of the text. For instance, the sentence “The quick brown fox jumps over the lazy dog” can be broken into tokens, which, in this case, are words: [“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”]. This is a very basic step that is carried out during the execution of several NLP tasks, from text preparation to feature identification and language model development.

  1. Stemming and Lemmatization

Finding the root or base form of words is called stemming and lemmatization. These methods help simplify text and reduce unnecessary data by reducing words to their basic forms. Stemming removes suffixes or prefixes from words to get the root, even if the resulting word may not be a real word in the language. For example, the word “running” may become “run”. Lemmatization considers the word’s context and rules to find the actual base form, ensuring it’s a valid word. For instance, “better” would become “good”. These NLP techniques are important for normalizing text and improving the accuracy of NLP models.

  1. Removing Common Words

Common words that appear frequently in a language, but don’t add much meaning, are called stop words. Examples include “the”, “and”, “is”, and “in”. Removing these stop words from text helps NLP algorithms work better by reducing noise and focusing on the important content-bearing words. This preparation step is essential in tasks like document classification, information retrieval, and sentiment analysis, where stop words can negatively impact the models’ performance.

  1. Categorizing Text

Text categorization is the general task of marking text into predefined categories. Categorization is possible for all sorts of texts: spam detection, sentiment analysis, topics, and languages. Text categorization is done by learning text-categorization algorithms to recognize patterns in the next data and to predict which class or category a particular piece of text belongs to. Popular techniques for this are Naive Bayes, Support Vector Machines (SVM), and deep learning models such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN).

  1. Understanding Emotions in Text

Sentiment analysis or opinion mining is the process of identifying the feelings or opinions in text. It helps understand the feedback of a customer, social media, and perception towards a brand. Sentiment analysis enables automatic classification of text into positive, negative, or neutral based on the expressed emotion in them. This may appear to be very useful information for any enterprise that wants to measure customer satisfaction, reputation management, and even the improvement of the product.

  1. Finding Important Topics in Text

Finding the main topics or themes hidden in a bunch of documents is called topic modeling. It is an unsupervised learning technique that helps to find common patterns and links between words. As a matter of fact, it can be applied in organizing and summarizing big volumes of textual data. In practice, this can be performed through Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). Topic modeling finds applications in functions like grouping documents, locating information, and recommending content.

  1. Creating Short Summaries of Text

Creating short versions of longer texts while keeping the most important information is called text summarization. This method is useful for getting the key points and making complex text easier to understand. To do this, there are two basic methods: 

  • Important Sentences Extraction: The process involves selecting and extracting important sentences from the original text, which, when combined together, form a summary. Key sentences are identified based on the importance of the sentences in the text, the relevance of the sentences to the text, and the informativeness of the sentences. In general, extractive summarization uses algorithms that pay attention to word frequency, its positioning, and significance in the text.
  • Rephrase and Combine: It is the method that generates a summary by rephrasing and combining the content of the original text in a new form. Unlike extractive approaches that pick sentences directly, this method rephrases the information in a more concise and clear manner.

Text summarization has many uses across different areas, like summarizing news articles, documents, and recommending content. For example, news sites use summarization to automatically create headlines and short summaries so readers can quickly understand the main points. Content recommendation platforms also use it to show short previews of articles and posts to help users decide what to read.  

  1. Named Entity Recognition (NER)

Identifying and categorizing specific names like people, organizations, locations, dates, and numbers within a text is called Named Entity Recognition (NER). NER is an important challenge for extracting structured details from unstructured text data. It is used in various applications, including finding information, linking entities, and building knowledge graphs. 

NER systems generally recognize and categorize named items within the text using machine learning methods, such as deep learning models and conditional random fields (CRFs). These algorithms analyze the context and structure of words to determine if they represent named entities and, if so, which category they belong to. NER models are trained on labeled datasets that include examples of named entities and their matching categories, allowing them to understand patterns and connections between words and entity kinds.

By employing these key NLP methods, businesses can unlock valuable insights from text data, leading to better decision-making, improved customer experiences, and greater operational efficiency. NLP techniques are essential for generating actionable insights from unstructured textual data, whether the task involves detecting significant named entities within the text or summarizing long works to extract important details.

How do Businesses Use NLP Techniques?

Translating Languages Automatically

Machine translation is the process of automatically translating text from one human language into another. A machine translation system that uses (NLP) natural language processing techniques can analyze the source text and put out a translation representing its scope and meaning. This ability is put to good use with global reach in business communication and operation. Businesses can transcend the barrier of languages by communicating with an audience in a wide range of audiences all over the world.

Gaining Insights from Unstructured Data

NLP techniques are important in market intelligence because they allow companies to examine unstructured data sources like social media posts, customer reviews, and news articles to uncover valuable insights and trends. Methods like sentiment analysis and topic modeling are effective in knowing customer preferences, market dynamics, and competitive landscapes. Such information guides organizations to make decisions based on facts, come up with highly targeted marketing strategies, and move ahead with the market trend.

Understanding User Goals for Personalized Experiences

Intent classification uses NLP algorithms to recognize text data or expressions linked with distinct user intents or objectives. By analyzing user queries and interactions, intent classification systems can accurately determine what the user wants and tailor responses or actions accordingly. This makes it possible for companies to provide individualized experiences, boost user engagement through chatbots, virtual assistants, and customer support platforms, and improve customer service.

Answering User Questions in Natural Language

Systems that can understand and respond to user questions expressed in plain language rely on NLP techniques. These question-answering systems analyze the meaning behind questions and find relevant information from structured or unstructured data sources to generate accurate responses. Applications for answering questions have diverse uses, including customer support, knowledge management, and search engines, where they help users quickly and efficiently find the information they need.

Real-world Examples of Using NLP

OpenAI’s GPT-4

OpenAI GPT-4 is a breakthrough in AI and NLP technology. This extremely talented language model represents the potential for understanding and generating human language at an enormous scale. GPT-4 is enabled for text input through APIs, enabling developers to architect revolutionary applications.

Analyzing Customer Experience

NLP technology has been applied extensively to the area of customer experience in order to bring out meaningful insights from textual data sources like customer feedback, reviews, and social media interactions. It helps businesses understand customer sentiments, preferences, and behaviors through sentiment analysis, topic modeling, and named entity recognition. That helps make the right business decisions, making the offer personal for the needs of clients, improving the quality of products and services, and increasing the general level of customer satisfaction and loyalty.

Automating recruitment process

NLP is used for the automation of the screening of résumés, matching jobs, and making engagements with candidates. NLP will help the algorithms evaluate résumés, job descriptions, and communication from candidates to find the relevant skills, experiences, and qualifications. More basically, NLP in this lean process of engaging and screening candidates helps businesses find top talent more efficiently, employ more people in an efficient way, and save time and money.

Wrapping Up

There is no doubt about the power of transformation that NLP techniques hold over businesses: whether it is the breaking down of language barriers, understanding unstructured data, improving customer experience, or increasing efficiencies in business processes, NLP is one area with wide reach and many applications that drive growth, innovation, and competitive advantage. 

Therefore, newer ways of better success and being at the forefront of the pace of digital changes may be more and more found by a lot of organizations. It is now the perfect moment for businesses to adopt NLP and use its ability to increase productivity, efficiency, and overall success.