Top 5 Programming Languages Every AI Techie Should Learn

Top 5 Programming Languages In Artificial Intelligence 2024

We see Artificial Intelligence everywhere today, be it robots, chatbots or the automobile sector!!!

If AI is everywhere can Coding be left behind?? No right. 

Did you know?? There are programming languages made for AI and trust me if you’re an AI geek this blog is definitely for you!!

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Why we need AI in Programming languages 

You might be wondering, coding is all about getting the right code out of the human brain right?? But what if I tell you, AI can become your brain and help you excel in coding

  1. Code Completion and Suggestion– AI tools can suggest relevant code snippets and functions based on the context, reducing development time and improving code accuracy.
  1. Idea Generation and Code Exploration- AI can suggest new code structures, algorithms, and functionalities based on existing code and programmer intent. This can spark creative solutions and help programmers explore new possibilities.
  1. Automatic Code Translation- AI can translate code from one language to another, making codebases more accessible to a wider range of developers and fostering collaboration across language barriers.
  1. Automatic Code Optimization- AI can analyze code and suggest optimizations for performance, memory usage, and efficiency, ensuring your programs run smoothly.

Top Programming Languages you should know

  1. Python

Python’s clean syntax makes it easy to learn and use, even for beginners. This allows you to focus on the core AI concepts rather than wrestling with complex code structures.

Python boasts a vast ecosystem of AI-specific libraries like TensorFlow, PyTorch, and scikit-learn. These libraries provide pre-built modules and functions, saving you time and effort in developing core AI functionalities.

Examples of AI applications built with python are- 

  1. NLP- Python powers chatbots, language translation, and sentiment analysis applications.
  2. Image Recognition- Widely used for facial recognition and object detection with libraries like OpenCV.
  3. Machine Learning Models- Python is the go-to language for building predictive, classification, and recommendation models.
  4. Data Analysis- Python, along with Pandas and NumPy, is extensively used for AI-driven data analysis and manipulation.

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  1. Java

Java’s object-oriented programming paradigm promotes code reusability and maintainability, crucial for managing complex AI projects.

Java has a well-established developer community and a wealth of libraries like Weka and H2O, providing a robust foundation for AI development.

Examples of AI applications built with Java are-

  1. Speech Recognition- Java is used in developing speech recognition systems, enabling applications to understand and process spoken language.
  2. Data Mining- Java’s scalability and performance make it suitable for data mining applications. It helps in extracting valuable insights from vast datasets.
  3. Intelligent Agents- Java is employed in creating intelligent agents that can perform tasks autonomously, such as in autonomous vehicles or smart home systems.
  4. Expert Systems- Java is utilized in building expert systems that mimic human decision-making processes. 
  1. C++

C++ gives a fine-grained control over the code, allowing for performance optimization essential for pushing the boundaries of AI capabilities.

C++ offers exceptional control over memory management and hardware resources, making it ideal for computationally intensive tasks like deep learning algorithms.

Examples of AI applications built with C++ are-

  1. Game Development- C++ is extensively used in AI components of video games. It enables realistic and intelligent behaviour for non-player characters.
  2. Robotics- C++ is employed in programming the intelligence of robots and handling tasks like navigation and decision-making.
  3. Computer Vision- C++ is utilised in computer vision applications, where high-performance processing is essential for tasks like image recognition and object tracking.
  4. Embedded Systems- C++ is favoured for Artificial Intelligence 2024 applications in embedded systems, where efficiency and resource management are critical. For example, smart devices and IoT.

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  1. Julia

Julia is designed for scientific computing, making it a natural fit for AI development that heavily relies on numerical computations.

The Julia community is actively developing libraries specifically for AI applications, making it a language to watch for the future.

Examples of AI Applications Built with Julia are-

  1. Robotics and Control Systems- Julia’s strength in complex math makes it suitable for AI models that interact with the physical world, like robots. 
  2. Natural Language Processing- NLP libraries like NaturalLanguage.jl enable building applications for sentiment analysis and text classification. 
  3. Financial Modeling and Risk Analysis- AI models built with Julia can be used for portfolio optimization, fraud detection, and risk assessment in finance. 
  4. Drug Discovery and Materials Science- Julia helps develop AI models to simulate molecules and accelerate scientific discovery processes. 
  1. JavaScript

JavaScript allows you to create AI-powered applications that run directly in web browsers, eliminating the need for complex installations.

JavaScript might not be suitable for computationally heavy AI tasks due to performance limitations compared to languages like C++.

Examples of AI Applications Built with JavaScript are-

  1. Chatbots- JavaScript is commonly used in building AI-driven chatbots for websites, enhancing user engagement and support.
  2. Data Visualization- JavaScript, with libraries like D3.js, is employed in creating interactive visualizations for AI-generated insights.
  3. Speech Recognition- JavaScript can be utilized for implementing speech recognition functionalities in web applications.
  4. Reinforcement Learning Games- JavaScript is applied in creating browser-based games with AI-driven components.


We have discussed some of the coding languages that are suitable for coders who have keen interest in AI along with the applications of them in AI. 

But, it is necessary to understand that AI too has its limitations and disadvantages and it can never replace human effort!!

As it is said, “AI makes coding smarter, faster, and more intuitive.”

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