Best AI Programming Languages for Creating AI Tools in 2024 [Updated]

Updated on May 7 2024
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The global AI software market is projected to reach a staggering $1300 billion by 2026, with the average salary for AI engineers in the United States hitting $115k. Moreover, the demand for AI professionals is expected to grow 33% faster than the average for all occupations from 2020 to 2030.

Artificial intelligence is the most rapidly growing and transformative fields in the world today, reshaping the way we work, interact, and innovate. As AI continues to surge, there’s a soaring demand for skilled AI professionals who play a pivotal role in developing and deploying AI systems, employing an array of AI programming languages to create algorithms, train models, and build applications AI programming languages are the keys to unlock the full potential of AI.

These languages allow us to develop and deploy AI algorithms and applications that can solve complex problems and automate tasks in ways that were never before possible.

In this article, we jump into the most popular AI programming languages and explore the diverse career paths, from machine learning engineers crafting sophisticated algorithms to data scientists unearthing insights from data, and AI researchers pioneering cutting-edge solutions in machine learning, natural language processing, and computer vision.

Before you read further, you can explore the Journey of ChatGPT and How it Evolved Overtime [Infographic]

Top AI Programming Languages to Learn for AI Development

1. Python

Python
Python

Python is a dynamic and high-level programming language which is renowned for its simplicity and readability. Its clean and easy-to-understand syntax makes it an excellent choice for both beginners and experienced programmers.

Python is particularly well-suited for AI programming due to its rich ecosystem of libraries and frameworks. These tools expedite the development of AI applications and make complex tasks more accessible. For instance, TensorFlow and PyTorch are leading libraries for building deep learning models, while scikit-learn simplifies machine learning.

Key features of Python for AI programming

Simplicity: Python’s clear and readable syntax makes it easier to write and understand code.

Abundant Libraries: Python has a vast ecosystem of AI-specific libraries and frameworks, such as TensorFlow, PyTorch, scikit-learn, Keras, and NLTK which provide pre-built tools and functions for various AI tasks.

Data Handling: Python is adept at data manipulation and analysis and makes it well-suited for tasks involving data preprocessing, cleaning, and transformation which is crucial in AI development.

Versatility: Python can be employed across various AI domains, from natural language processing and computer vision to machine learning and deep learning.

Rapid Prototyping: Python’s quick development cycle supports rapid prototyping and experimentation.

Machine Learning and Deep Learning: Python serves as the primary language for developing machine learning and deep learning models, thanks to robust frameworks like TensorFlow and PyTorch.

Data Science Ecosystem: Python is the go-to language for data scientists, with libraries like NumPy, Pandas, and Matplotlib that simplify data analysis and visualization.

Cross-Platform Compatibility: Python is cross-platform which means code can run on various operating systems without modification and enhance portability in AI projects.

2. Java

Java
Java

Java is an object-oriented programming language known for its portability and wide range of applications. In terms of AI programming, Java offers several advantages. It boasts strong libraries and frameworks like Deeplearning4j and Weka for machine learning and data analysis tasks.

Additionally, Java’s platform independence makes it suitable for deploying AI solutions across various operating systems. Java’s mature ecosystem, stability, and support also make it a reliable choice for developing AI applications.

Key features of Java for AI programming

Platform Independence: Java’s “Write Once, Run Anywhere” capability allows AI developers to create code that can be executed on diverse platforms.

Rich Ecosystem: Java offers a vast selection of libraries and frameworks like Deeplearning4j, Weka, and JavaML for machine learning and data analysis.

Scalability: Java’s scalability and multithreading support make it suitable for handling complex AI workloads.

Security: Java’s built-in security features help protect AI applications which is crucial for sensitive data handling.

Community Support: A large community of Java developers provides extensive support, resources, and documentation for AI programming

3. C++

C++ Language
C++ Language

C++ is a high-performance programming language known for its efficiency and flexibility. It is widely used in artificial intelligence development due to its ability to handle complex calculations and memory management efficiently.

C++ offers a variety of libraries and frameworks such as TensorFlow and OpenCV, which empower AI developers to create machine learning models, neural networks, and computer vision applications. Its low-level capabilities allow for fine-tuned control of hardware resources and make it suitable for optimizing AI algorithms on various platforms.

Additionally, C++ supports object-oriented programming enables developers to create structured and reusable AI code.

Key features of C++ for AI programming

High Performance: C++ offers efficient execution, crucial for AI algorithms that demand speedy computations.

Low-Level Control: It allows fine-grained management of memory and hardware resources and optimizes AI tasks.

Object-Oriented: C++ supports structured, modular code, and helps the development of complex AI systems.

Libraries and Frameworks: C++ has AI-specific libraries like TensorFlow and OpenCV for machine learning and computer vision.

Cross-Platform: Code written in C++ can be ported across different operating systems and enhance AI application versatility.

Extensibility: C++ facilitates integration with other languages like Python and broadens AI development possibilities.

4. R

R Language
R Language

R is a programming language and environment used in AI and data science. Its strengths lie in statistical analysis, data manipulation, and visualization which make it a popular choice for AI tasks. R’s extensive package ecosystem includes libraries like ‘caret’ and ‘tensorflow’ that empowers AI developers to build and deploy machine learning models efficiently.

With R, you can create predictive models, perform natural language processing, and tackle computer vision tasks. Its rich statistical functions, combined with cutting-edge AI techniques, enable data scientists to handle complex AI projects and make R a powerful tool in the field of artificial intelligence.

Key features of R for AI programming

Extensive Libraries: R boasts a rich collection of libraries for machine learning, data manipulation, and statistical analysis.

Data Visualization: It excels in data visualization which helps in data exploration and model interpretation.

Statistical Functions: R provides robust statistical functions for hypothesis testing, regression, and more.

Integration with AI Tools: Integration with AI frameworks like TensorFlow and Keras for deep learning projects.

Data Handling: R is proficient in data preprocessing, transformation, and data cleaning, crucial for AI projects.

Cross-Validation: It offers tools for cross-validation, model selection, and hyperparameter tuning.

Open-Source: R is open-source which makes it cost-effective and accessible for AI development.

Interoperability: R can be integrated with other languages like Python and allow for versatile AI development.

Rich Ecosystem: The CRAN repository hosts thousands of packages for diverse AI applications, from natural language processing to computer vision.

5. Julia

Julia
Julia

Julia is a high-level and dynamic programming language known for its exceptional performance in scientific and numerical computing and makes it a powerful choice for AI programming. Julia’s key strength lies in its just-in-time (JIT) compilation, which allows for rapid execution of code and seamless integration with existing C and Fortran libraries. This makes it ideal for data manipulation, machine learning, and scientific simulations.

Julia offers extensive mathematical and statistical libraries such as Flux.jl for deep learning and MLJ for machine learning which makes it a compelling language for AI development. Its simple, expressive syntax and built-in support for parallel and distributed computing further enhance its suitability for AI tasks and facilitate the development of high-performance and scalable AI applications.

Key features of Julia for AI programming

Just-in-Time Compilation (JIT): Julia’s JIT compiler ensures high performance which makes it competitive with languages like C or Fortran for numerical tasks.

Interoperability: Julia can seamlessly call C, Fortran, and Python functions and enable users to leverage existing libraries and tools.

Parallel and Distributed Computing: Built-in primitives for multi-threading, multi-processing, and distributed computing to scale AI tasks.

Flux.jl: A machine learning library that offers easy model definition and GPU acceleration for deep learning.

MLJ: A toolbox for machine learning in Julia, offering a wide array of algorithms and data manipulation tools.

DifferentialEquations.jl: Enables sophisticated numerical solutions for differential equations, useful for simulations in AI.

Rich Ecosystem: A rapidly growing ecosystem of packages optimized for various AI-related tasks.

GPU Support: Native support for GPU computation that enhances performance for deep learning and other AI workloads.

Dynamic Typing: Provides flexibility in coding while the JIT compiler maintains high performance.

Mathematical Syntax: Natural mathematical notation makes it easy to translate algorithms and models into code.

6. Prolog

Prolog
Prolog

Prolog stands for “Programming in Logic” which is a high-level programming language primarily used in the field of AI. What sets Prolog apart is its unique declarative approach, where programs are expressed in terms of logical relationships and rules. This makes it particularly well-suited for tasks involving symbolic reasoning and knowledge representation, common in AI applications.

Prolog excels in areas like expert systems, natural language processing, and rule-based systems. It uses a built-in inference engine to perform automated reasoning and search through its knowledge base to find solutions to logical queries. Prolog’s ability to represent and manipulate complex relationships and its efficient pattern matching capabilities make it a powerful tool in AI programming.

Key features of Prolog for AI programming

Declarative Language: Prolog is a declarative language that makes it well-suited for expressing logical relationships and rules in AI systems.

Pattern Matching: It excels at pattern matching and allows for efficient searching and inference over knowledge bases.

Rule-Based System: Prolog rule-based approach is ideal for developing expert systems and knowledge representation.

Backtracking: It offers built-in backtracking to explore multiple solutions to problems, which is essential in AI decision-making.

Natural Language Processing: Prolog is used for tasks like parsing and understanding natural language which makes it valuable in NLP applications.

Knowledge Representation: It is highly capable of representing and manipulating complex knowledge structures in AI domains.

7. Lisp

Lisp
Lisp

Lisp stands for “List Processing” which is a programming language known for its significant contributions to AI field. It was developed in the late 1950s and has remained a popular choice in AI research and development due to its unique features.

Lisp’s key strength lies in its support for symbolic processing that makes it well-suited for representing and manipulating complex data structures and knowledge. This has made it a cornerstone in the development of AI applications, including expert systems, natural language processing, and intelligent robotics.

The language’s flexibility, dynamic typing, and emphasis on recursion have allowed AI researchers to experiment with various AI techniques, and Lisp dialects like Common Lisp and Scheme continue to be influential in AI programming and research.

Key features of Lisp for AI programming

Symbolic Processing: Lisp excels at manipulating symbols and representing complex knowledge which makes it ideal for AI applications.

Dynamic Typing: Its flexible data types accommodate dynamic changes, which is essential for AI systems dealing with evolving data.

Garbage Collection: Automatic memory management simplifies AI development by avoiding manual memory allocation.

Recursion: Lisp’s support for recursive functions facilitates elegant and efficient AI algorithms.

Expressive Syntax: Its minimalist syntax encourages concise and readable AI code.

High-Level Language: Lisp abstracts low-level details which enable focus on AI logic and problem-solving.

Extensibility: AI developers can easily create custom functions and data structures to tailor Lisp to specific needs.

Interactive Development: Lisp environments offer interactive debugging and prototyping to speed up AI development.

Rich Libraries: Libraries like Common Lisp’s CLOS aid in developing complex AI systems with object-oriented programming.

Historical Significance: Lisp has a long history in AI, contributing to its enduring relevance in AI research and applications.

Why Learn AI Programming Languages in 2024

Learning AI programming languages is highly beneficial in today’s tech-driven world. These languages like Python, R, and TensorFlow, provide the tools to understand and create cutting-edge artificial intelligence applications.

They enable you to work on machine learning, deep learning, and data analysis projects, opening doors to lucrative career opportunities.

Moreover, AI languages are versatile, widely supported, and backed by vibrant communities which makes it easier to access resources and stay updated with the latest advancements in AI technology.

With AI programming skills, you can contribute to solving complex problems, automate tasks, and drive innovation in various industries, from healthcare to finance and ultimately enhancing your career prospects and personal development. Also Read: List of AI Tools for Amazon Sellers

Career Options in AI Field

Artificial Intelligence has rapidly emerged as a transformative force across industries and offers a wealth of exciting career opportunities. From creating intelligent software to delving into ethical considerations, the AI landscape is vast and diverse.

Here is the list of some most demanding career option in AI field:

Machine Learning Engineer: These professionals design and implement machine learning models to solve specific problems, often working with large datasets.

Data Scientist: Data scientists use AI and statistical techniques to analyze data and extract valuable insights for informed decision-making.

AI Research Scientist: They focus on pushing the boundaries of AI, developing new algorithms, and contributing to the academic and theoretical aspects of the field.

AI Ethicist: This role involves addressing the ethical and social implications of AI and ensuring responsible and unbiased AI development.

AI Software Developer: These professionals create AI-powered applications, from chatbots to recommendation systems.

Computer Vision Engineer: Specializing in computer vision, they work on applications like facial recognition, image analysis, and autonomous vehicles.

Natural Language Processing (NLP) Engineer: NLP engineers develop systems for language processing tasks, including chatbots and sentiment analysis.

Robotics Engineer: Focusing on AI in robotics, these engineers build intelligent robots for various industries, including healthcare and manufacturing.

AI Consultant: Consultants provide expertise to businesses seeking to implement AI solutions, helping them make informed decisions.

Alternatives of Learning Coding For AI App Building

Learning programming languages to build AI apps can be overwhelming for so many people. So to help you get out of this problem, we have found the solution for building an AI app without writing a single line of code.

Here is the Alternative of learning programming languages for building AI apps.

Amazon SageMaker Canvas

Amazon SageMaker Canvas is a no-code AI app builder that enables business analysts and data scientists to build and deploy machine learning (ML) models without writing any code. It provides a visual interface for users to explore and prepare data, choose ML algorithms, train models, and generate predictions.

SageMaker Canvas also includes a library of pre-built ML models that can be used for common tasks such as classification, forecasting, and anomaly detection.

SageMaker Canvas is a good choice for businesses and organizations that want to use ML to solve business problems but don’t have the resources or expertise to build ML models from scratch. It is also a good choice for businesses that want to democratize AI and make it accessible to a wider range of users.

Google AutoML

Google AutoML is a suite of machine learning (ML) products and services that make it easy to create custom ML models without any coding experience. It allows anyone to build ML models that can be used to solve a wide range of problems such as image classification, object detection, text classification, and translation.

AutoML is a no-code AI app builder, which means that users can create and deploy ML models without writing any code. AutoML provides a simple and intuitive user interface that allows users to train and deploy ML models with just a few clicks.

Also read: List of Best AI Content Detector

Conclusion

The world of AI programming languages is vast and continually evolving. We’ve delved into a selection of essential languages that provide a strong foundation for those venturing into the field of artificial intelligence.

From Python’s versatility to R’s statistical power and the efficiency of languages like Java and C++, each has its unique strengths.

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