Artificial Intelligence(AI), is a new tool for a new era. It is transforming industries for self-improvement with intelligent systems.  The software enables the capability to learn new skills with reasoning. AI systems have programming languages that developers use in developing algorithms, neural networks, and data-driven applications. In 2024, AI development is progressing with specific emerging programming languages.

This blog aligns the top 10 popular AI programming languages, their usages, their benefits, and their value in the current market to drive innovation. 

Checkout Best Programming  AI Languages

Different programming languages have their value based on their usage. Checkout the  top 10 languages list  suitable for AI-relevant technologies and applications:

1. Python

It is one of the top languages that everyone will agree to keep in the first place among the top 10 popular AI programming languages. It has earned its popularity with simple, free, universal features. The large library ecosystem helps to become an ideal choice for AI development. 

Why Python?

• Large libraries  (TensorFlow, Keras, PyTorch).
• Seamless, readable syntax
• Strong community support.

Usage in AI:

Python is an excellent choice for applications for Machine Learning(ML), Deep Learning(DL), and Natural Language Processing(NLP). It is capable enough of providing fast prototyping and rapid AI model testing. 

Statistics:

• Python took the second largest share in AI languages with a 28.73% market share as of 2023.
• More than 70% of artificial intelligence initiatives around the world are developed on Python.

Kodehash and Python:

Data application and creating machine learning models are the primary areas, where Kodehash includes  Python. Python’s easy-to-use integration and extended library support come up with the best development solutions.  Python improvement areas at Kodehash include artificial intelligence solutions for various companies.

2. R

It is one of the programming languages that primarily works for mathematical analysis and statistical computing. Machine learning, better visualization, and detailed data analysis are the basic areas for R’s utilization. 

Why R?

• Specialized in statistical modeling and risk analysis.
• Extensive libraries for Data Science and AI (caret, e1071, randomForest).
• Effective data visualization tools (ggplot2).

Usage in AI:

Statistical computing and data analysis are its main function areas. It is compatible with Java and Python.  It is mostly used for complete data analysis on AI applications. 

Statistics:

• After Python and SQL, R is the third most used language for data science.
• 12% of the companies that focused on using AI relied on R for their projects in 2023.

Kodehash and R:

R is used for data visualization and statistical analysis at Kodehash. It mostly enables development processes for artificial intelligence solutions in the field of health and financial sectors. R is capable enough to manage large data sets in developing the predictive analytics model. 

3. Java

Java is widely used for AI. It is the most preferred choice for large-scale developments, complex systems, and intensive enterprise-level applications. 

Why Java?

• Independent platform computing(Write Once, Run Anywhere)
• Effective in complex systems development
• Object-oriented features adoption
  

Usage in AI:

Neural network building mostly uses Java for its process.  NLP tools and enterprise-level AI systems development enable high-performance level Java features for scalable solutions.  

Statistics:

• Leading software companies apply 60% Java in AI initiatives To target large-scale systems.  
• AI and machine learning applications have used 17% of Java applications in 2023. 

Kodehash and Java:

Kodehash uses Java for high-performance and scalable AI solutions in enterprises. Java is capable enough of improving high traffic for building large-scale AI systems.

4. C++

The demanding C++ features that make it stand out from the crowd are speed, efficiency, and management-heavy applications. All these features make it a number-one choice in the fields of robotics and  AI-driven game development. 

Why C++?

• High-speed control programming with memory control features 
• Ideal  for real-time and simulation complex systems solutions
• AI applications utilization to meet speed requirements.  

Usage in AI:

 In AI sectors like autonomous vehicles, gaming, and real-time computer vision, developers use C++ for low-potency processing. 

Statistics:

C++  is used 15% for AI applications in real-time processing in 2023. 

Kodehash and C++:

Kodehash knows the importance of C++. It uses C++ for high-performance and complex real-time processing services for AI development systems. The most favored domains for C++ are AI-driven game development and Robotics applications at Kodehash.  

5. Julia

This new language is designed for scientific computing and is prominently believed as a Next Generation AI language. 

Why Julia?

• Excellent execution speed.
• Established in numerical computation and data analysis
• Strong cloud computing capabilities with parallelism 

Usage in AI:

Julia provides excellent services for developing speed and efficiency in  artificial intelligence, big data, and high-performing computing applications 

Statistics:

Julia’s adoption of AI was doubled in 2023, especially in high-performance computing projects.

Kodehash and Julia:

Kodehash employs Julia for its tasks connected with large amounts of data and numerical computations at their core. In particular, time and computation-efficient models make it particularly effective to apply AI-driven platforms that require fast and instant results.

6. Lisp

Lisp perhaps is one of the oldest programming languages. It is very appropriate for AI development, specifically in symbolic processing and timesaving prototypes.

Why Lisp?

• Flexible enough to manage symbolic data 
• Rule-based system and AI prototype development expert

Usage in AI:

Expert systems such as NLP and machine learning often use Lisp. It gives its best performance in a rapid application development environment to meet experimentation purposes. 

Statistics:

5% of Lisp’s share is included in Academic research and symbolic AI projects during 2023’s AI applications. 

Kodehash and Lisp:

The research projects for Natural Language Processing and Symbolic Processing Kodehash team use Lisp programming language. This makes it possible to design intelligent systems that are characterized by distinct rule-based reasoning.

7. Prolog

Prolog is mostly used in logic programming. It is a method fundamental for AI in different fields like theorem proving and expert systems.

Why Prolog?

• Its strong reasoning features provide solutions for rule-based AI systems 
• Suitable for reasoning, decision-making, and NLP application-solving requirements. 

Usage in AI:

Prolog is widely used in areas of AI-driven databases, proving theorems, and chatbots. Because it is capable enough to represent knowledge and provide logical problem solutions. 

Statistics:

In 2023, 3% of Prolog is used in expert systems and NLP. 

Kodehash and Prolog:

Kodehash uses Prolog for AI system applications that need to incorporate logical decisions such as expert systems and AI databases.

8. Scala

Scala is designed with the concept of keeping things concise and expressive. This language fits well with Java. The functional programming characteristics of these tools make them quite useful in data-driven artificial intelligence applications.

Why Scala?

• Work simultaneously for object-oriented and functional programming
• 100% compatible with Java libraries
• Efficient enough in managing big data processes

Usage in AI:

Scala is known for providing big data environment AI solutions. This language is perfectly integrated with Apache Spark for a machine-learning framework. 

Statistics:

In 2023, Scala usage is up by 8% for AI-driven big data and analytics. 

Kodehash and Scala:

Kodehash needs Scala for big data AI applications. It is also very useful in systems for seamless integration with Java and Spark to meet complex data processing needs.

9. Haskell

It is known as one of the best functional programming languages and works best in mathematical computations and abstract AI algorithms. 

Why Haskell?

• Capable of dealing with theories in abstract mathematics and symbolic computation.
• Best in use for pure AI application functionalities and immutability. 

Usage in AI:

Haskell is a preferred language in artificial intelligence research. It also involves mathematical computation and applications for higher-order symbolic manipulations.

Statistics:

In 2023, Haskell was used in 2 % of symbolic AI projects.

Kodehash and Haskell:

Kodehash uses Haskell in those projects on high calculative mathematics. Most artificial intelligence research and simulation modeling use this programming language. 

10. JavaScript (TensorFlow.js)

Overview:

JavaScript, especially through TensorFlow. js has become eminent in the creation of AI solutions. It enables machine learning models for direct running on browsers. 

Why JavaScript?

• Pushes AI models to the client’s browsers
• Perfect for AI-driven web applications 
• Supports TensorFlow.js. like frameworks

Usage in AI:

AI in the web context frequently uses Javascript for different purposes such as live object identification and engaging learning models.

Statistics:

In 2023, TensorFlow.js used 12% more to run on the browser.

Kodehash and JavaScript:

Kodehash incorporates JavaScript into the management of AI-embedded web applications. Enable machine learning models to start on client browsers without heavy service infrastructure support. 

Final Thought 

In this blog, find 2024’s top 10 programming languages, dominating the market. Each language is different according to its use. Whether it is Python’s versatile programming or Julia’s high-performance computing capabilities, we are listing each one’s best qualities. Kodehash is using these languages to bring cutting-edge AI solutions to the next generation. The AI applications we are focusing on at Kodehash focus on different industries by keeping these languages’ value at the center of AI’s future. 

There are many languages to choose from in AI development, and the pace is growing rapidly. These languages allow you to create practical systems for solving real-world problems.  Whether you prefer code simplicity, performance or code scalability these languages are your tools of trade.