5 Recommended Programming Languages for AI Development

What is the best programming language for AI development?

The development of artificial intelligence (AI) is one of the hottest topics in modern society. From self-driving cars, medical diagnosis, financial trading, and generative AI like ChatGPT that is integrated into our daily lives, AI is enriching our lives in many ways.

However, AI development is by no means easy. A wide range of knowledge and skills are required, including designing advanced algorithms and models, processing large amounts of data, and building complex systems. Among these, one of the most important choices when starting AI development is the “programming language.”

Various programming languages are used in AI development, but each has its own characteristics and areas of expertise, and the best language will vary depending on the type and purpose of the AI being developed. In this article, we will introduce the programming languages that are commonly used in AI development and provide a detailed explanation of the advantages and disadvantages of each, as well as examples of their use. We will also introduce preparations for starting AI development and future trends.

The importance of programming languages in AI development

In AI development, a programming language is not just a tool for writing code. It is the foundation that supports the entire development process, from designing, learning, evaluating, and deploying AI models. Choosing the right programming language affects various aspects, such as development efficiency, execution speed, and code maintainability.

Types of AI development and the languages required

Different types of AI development require different programming languages.

• Machine Learning: Machine learning is an AI technique that learns patterns and regularities from data to perform tasks such as prediction and classification. Python, R, Julia, etc. are commonly used for machine learning.
• Deep Learning: Deep learning is a type of machine learning that uses neural networks that mimic the neural circuits of the human brain to learn more complex patterns. Python, C++, Java, etc. are often used for deep learning.
• Natural Language Processing: Natural language processing is a technology that allows computers to understand and process human language. Python, Java, R, etc. are often used for natural language processing.
• Image Recognition: Image recognition is a technology that allows a computer to recognize what is in an image or video. Python, C++, Java, etc. are often used for image recognition.

Key points for choosing a language

When choosing a programming language to use for AI development, consider the following points:

• Development efficiency: It is important to choose a language that has high development efficiency, such as requiring less code to write and having a wealth of libraries and frameworks.
• Execution speed: AI model training and inference processing can require a lot of computing power. Choosing a language with fast execution speed can reduce processing time.
• Richness of libraries: AI development utilizes a variety of libraries and frameworks. By choosing a language that has a rich selection of these tools, you can improve development efficiency.
• Active community: A language with an active community makes it easier to discuss and ask questions, solve problems, and take advantage of community-developed libraries and tools.

Taking all these points into consideration, choose the programming language that best suits the type of AI you want to develop and your purpose.

Programming languages commonly used in AI development

Programming languages commonly used in AI development include Python, C++, Java, R, and JavaScript. Here, we will explain in detail the characteristics, advantages and disadvantages, and use cases of these languages.

Python: The standard language for AI development

Python is the most popular programming language for AI development due to its simple and readable syntax, extensive library and frameworks, and active community.

Features and benefits of Python

• Simple grammar, easy-to-read code: Python has simple grammar and highly readable code compared to other languages. Therefore, even beginners can learn it relatively easily. Another advantage is that the amount of code written is small, which increases development efficiency.
• Rich libraries and frameworks: Python has a wealth of libraries and frameworks that are useful for AI development.
• TensorFlow: A deep learning framework developed by Google. It is widely used as it is suitable for training large-scale datasets and complex models.
• PyTorch: A deep learning framework developed by Meta (formerly Facebook). It is suitable for research and development and prototyping, and allows flexible model construction.
• scikit-learn: A machine learning library that allows you to easily implement various machine learning algorithms such as classification, regression, and clustering.
• NumPy: A numerical calculation library that can efficiently handle multidimensional arrays and is essential for machine learning and deep learning calculations.
• Pandas: A data analysis library that makes it easy to work with tabular data and is useful for data preprocessing and analysis.
• Active Community: Python has an active community around the world, with community forums and Q&A sites to help you solve your questions and problems, and a wealth of community-developed libraries and tools.

Disadvantages of Python

• Execution speed may be slow: Because Python is an interpreted language, execution speed may be slower than compiled languages such as C++ and Java. However, libraries such as NumPy and Pandas are implemented internally in C, allowing for fast numerical calculations. In addition, execution speed can be improved by using a JIT compiler such as PyPy.

Python usage cases

Python is used in various areas of AI development, such as:

• Machine learning: It is used for various machine learning tasks such as classification, regression, and clustering. For example, it is used in spam filtering, customer behavior prediction, product recommendations, etc.
• Deep Learning: It is used for various deep learning tasks such as image recognition, natural language processing, and speech recognition, for example in self-driving cars, facial recognition systems, and voice assistants.
• Natural Language Processing: Natural language processing is used for various tasks such as machine translation, text generation, sentiment analysis, chatbots, etc. For example, it is applied to Google Translate, ChatGPT, spam filters, etc.
• Data Analysis: It is used for various tasks of data analysis, such as data pre-processing, visualization, analysis, and reporting. For example, it is applied to business data analysis, marketing analysis, scientific and technical calculations, etc.

Python is popular among a wide range of AI developers, from beginners to advanced users, due to its versatility and ease of use. If you are starting AI development, Python is one of the best options.

C++: For AI development that requires high-speed processing

C++ is a programming language with fast execution speed and hardware control capabilities, and it also plays an important role in AI development, especially in situations where high performance is required, such as AI applications that require real-time performance and training of AI models that handle large data sets.

Features and advantages of C++

• Fast execution speed: Because C++ is a compiled language, its execution speed is faster than that of interpreted languages such as Python. It can quickly execute computationally intensive tasks such as training and inference processing of AI models.
• Hardware control: C++ allows direct control of hardware, making it possible to maximize the performance of specialized hardware such as GPUs and TPUs, thereby shortening the training time of AI models and enabling the handling of more complex models.
• Available in frameworks such as TensorFlow and Caffe: C++ can be used in major deep learning frameworks such as TensorFlow and Caffe. These frameworks leverage the fast execution speed and hardware control capabilities of C++ to efficiently train and infer large-scale AI models.

Disadvantages of C++

• High learning cost: C++ has a complex grammar compared to other languages, and it may take time to learn. It also contains concepts that are difficult for beginners, such as pointers and memory management, so some programming experience is required.
• Code can easily become complicated: C++ is a highly flexible language, but it can also easily become complicated. This can lead to poor code readability and maintainability, especially in large projects.

C++ usage examples

C++ is used in the following AI development scenarios:

• Game AI: Game AI requires real-time decision-making and complex action generation, so the fast execution speed of C++ is put to good use.
• Robot Control: Robot control requires precise movement and real-time performance, so the hardware control capabilities of C++ are utilized.
• Embedded Systems: Embedded systems must operate with limited resources and therefore benefit from C++’s efficient memory management.

C++ is a language that is effective in AI development when high-speed processing and hardware control are required. However, it has a high learning cost, so it is recommended for those who have some programming experience.

Java: For large-scale development and web applications

Java is widely used in enterprise-level AI and web application development due to its stability and robustness. It is an object-oriented programming language, and its strength lies in the ease of managing and maintaining code, even in large-scale projects.

Features and benefits of Java

• Object Oriented: Java is an object-oriented programming language, which makes code more reusable and maintainable. In large AI projects, object-oriented programming is extremely useful as code complexity increases.
• Platform independence: Java runs on the JVM (Java Virtual Machine), so it can be run on a variety of platforms, including Windows, macOS, and Linux. This enables flexible AI development that is not dependent on the development or execution environment.
• Stability: Java is a language that has been developed and improved over many years, and is highly stable and reliable. Large-scale AI systems require stable operation, so Java is one of the best choices.
• Available with frameworks such as Deeplearning4j: Java has frameworks specialized for AI development, such as Deeplearning4j and Apache Spark MLlib. By utilizing these frameworks, you can efficiently build and train AI models in Java as well.

Disadvantages of Java

• Verbose code: Java tends to require more code to be written than other languages because it is a statically typed language, meaning that variable types must be explicitly declared.
• Runs slower than Python: Java can run slower than Python. This is because Java runs on the JVM, which incurs some overhead during execution. However, the Just-In-Time Compiler (JIT) can optimize the execution speed.

Java usage examples

Java is used in the following AI development scenarios:

• Large-scale AI systems: Java is often used to develop AI systems for large organizations such as financial institutions and medical institutions. Java’s stability and robustness are essential for such mission-critical systems.
• Web applications: Java is also widely used in the field of web application development, and it is also possible to develop web applications that incorporate AI functions.
• Enterprise systems: Java has a proven track record in the field of enterprise system development, and is used in a variety of enterprise solutions, including AI-based business efficiency improvements and decision-making support systems.

Java is a language suitable for large-scale AI and web application development. By taking advantage of Java’s strengths, such as object-oriented programming and platform independence, you can build high-quality, reliable AI systems.

R: Specialized in statistical analysis and data visualization

R is a programming language specialized for statistical analysis and data visualization. It is widely used in the fields of statistics and data analysis, and also plays an important role in AI development. It is particularly useful for tasks related to data analysis, such as data preprocessing, statistical modeling, and visualization of results.

Features and benefits of R

• Abundant statistical analysis libraries: R has a package repository called CRAN (Comprehensive R Archive Network), which has thousands of statistical analysis libraries available to the public. By utilizing these libraries, you can easily implement various statistical analysis methods.
• Powerful data visualization capabilities: R provides powerful data visualization libraries such as ggplot2 and lattice. Using these libraries, you can easily create beautiful graphs and charts.
• Widely used in research fields: R is widely used in the fields of statistics and data analysis, and many researchers have made their R code public. This makes it easy to refer to the latest research results and collaborate with other researchers.

Disadvantages of R

• Slow execution speed: Because R is an interpreted language, execution speed can be slower than compiled languages such as Python and C++. However, by using the Rcpp package, you can call C++ code from R, which can improve execution speed.
• Not object-oriented: R is not an object-oriented programming language, which can make the code difficult to manage and maintain in large projects.

R usage cases

R is used in the following AI development scenarios:

• Statistical analysis: R is a language specialized for statistical analysis, so it is easy to implement various statistical analysis methods such as hypothesis testing, regression analysis, and factor analysis.
• Data Analysis: R is used for a variety of data analysis tasks including data pre-processing, exploratory data analysis, and statistical modeling.
• Data visualization: R can create beautiful graphs and charts using libraries such as ggplot2 and lattice. These visualization tools are very effective in communicating the results of data analysis in an easy-to-understand way.
• Research and Development: R is widely used in the research field, so you can access the latest research findings and collaborate with other researchers.

R is a language specialized for statistical analysis and data visualization, and also plays an important role in AI development. It is particularly useful in the fields of data analysis and research and development.

JavaScript: Developing AI that runs on web browsers

JavaScript is a programming language that runs on web browsers and is essential for realizing interactive functions on web pages. In recent years, JavaScript has also been attracting attention in the field of AI development, and AI applications that run on web browsers and interactive data visualization tools have been developed.

Features and benefits of JavaScript

• Running in a web browser: JavaScript can be run directly in a web browser, so no special environment needs to be built. This makes it possible to embed AI applications in web pages and make them easily accessible to users.
• Friendliness with front-end development: JavaScript is a language that is widely used in front-end development of web pages. Therefore, it is easy for web developers to learn and can be combined with existing web technologies to develop richer AI applications.
• Available in libraries such as TensorFlow.js: TensorFlow.js is a machine learning library for JavaScript developed by Google. By using TensorFlow.js, you can train and run machine learning models on a web browser.

Disadvantages of JavaScript

• Slow execution speed: Because JavaScript is an interpreted language, its execution speed can be slower than compiled languages such as Python and C++. However, by leveraging technologies such as WebAssembly, execution speed can be improved.
• Not suitable for large-scale AI development: JavaScript may not be suitable for large-scale AI development. Languages such as Python and C++ are better suited for training complex models and processing large datasets.

Examples of using JavaScript

JavaScript is used in the following AI development scenarios:

• Web browser-based AI app:
• You can develop applications that run AI functions such as image classification, object detection, facial recognition, and natural language processing on a web browser.
• For example, web apps are being developed that use AI to analyze images uploaded by users and recognize objects in the images or analyze people’s emotions.
• Interactive Data Visualization:
• By combining it with JavaScript libraries such as D3.js and Chart.js, you can create interactive data visualization tools.
• For example, interactive dashboards are being developed that allow users to zoom in and out of graphs and view detailed information about data points using the mouse in a web browser.

JavaScript is a language well suited to developing AI applications that run on web browsers. Its affinity with front-end development and the existence of libraries such as TensorFlow.js make it relatively easy for web developers to incorporate AI functions into web services.

Julia: The next generation scientific and technical computing language

Julia is a relatively new programming language that was released in 2012, but it has been attracting attention in the field of AI development due to its high performance and productivity. Julia was designed specifically for scientific and technical calculations, and is characterized by its combination of the ease of use of Python and the execution speed of C++.

Features and benefits of Julia

• Fast execution speed: Julia uses a technology called JIT compilation (Just-In-Time Compilation), which compiles code into machine code at runtime, achieving fast execution speed. This allows training and inference processing of AI models to be performed faster than interpreted languages such as Python and R.
• Both dynamic and static typing: Although Julia is a dynamically typed language, it is possible to perform type checking like a statically typed language by declaring types. This allows for both development efficiency and execution speed.
• Specialized for scientific and technical computing: Julia is designed specifically for scientific and technical computing and comes with a variety of standard scientific and technical computing libraries, including linear algebra, differential equations, optimization, and statistical analysis.
• Multiple Dispatch: Julia supports a feature called multiple dispatch, which allows you to define functions with the same name that accept arguments of different types, which is a very useful feature in scientific computing.

Disadvantages of Julia

• Small community: Julia is a relatively new language, so the community is still small compared to languages such as Python and R. Therefore, it may be difficult to gather information and solve problems.
• Few libraries yet: Julia has a wealth of libraries specialized for scientific and technical computing, but there are still few libraries in other fields.

Julia use cases

Julia is used in the following AI development scenarios:

• Scientific and technical computing: Julia has fast execution speed and a rich library of scientific and technical computing, so it is widely used in the field of scientific and technical computing, for example, in physics simulation, weather forecasting, and fluid analysis.
• Machine Learning: Julia provides deep learning frameworks such as Flux.jl and Knet.jl, allowing you to build and train machine learning models just like in Python.
• Data Analysis: Julia provides data analysis libraries such as DataFrames.jl, which allow you to work with tabular data, similar to Python’s Pandas.

Julia is attracting attention as the next-generation scientific and technical computing language, and its importance is expected to increase in AI development as well.

How to choose a programming language for AI development

The best programming language for AI development depends on the type of AI being developed, the purpose, the developer’s skill level, etc. Here, we will explain the points to consider when choosing a programming language and the type of developer each language is suitable for.

Choose by purpose and use

• Machine Learning/Deep Learning:
• Python is the most popular language in the field of machine learning and deep learning. This is due to its abundant libraries and frameworks, active community, and simple grammar. It is recommended for a wide range of users, from beginners to advanced users.
• example:
• scikit-learn: A comprehensive library of machine learning algorithms
• TensorFlow, PyTorch: The world’s most popular deep learning frameworks developed by Google and Meta.
• If you need high speed processing:
• C++ has excellent execution speed and hardware control capabilities, making it suitable for AI applications that require real-time performance and for training AI models that handle large data sets.
• example:
• Game AI: C++ is often used because complex calculations need to be performed quickly.
• Autonomous driving systems: Real-time decisions must be made, so the speed of C++ is important.
• Large scale development and web applications:
• Java has characteristics suitable for large-scale development, such as object-oriented programming, platform independence, and stability. It is also widely used in web application development, so it is also suitable for developing web services that incorporate AI functions.
• example:
• AI systems in financial institutions: Java is often used because they need to process large amounts of transaction data quickly and securely.
• Recommendation system for a large-scale e-commerce site: Java’s stability and scalability are important for providing personalized product recommendations to a large number of users.
• Statistical analysis and data visualization:
• R is a language specialized for statistical analysis and data visualization, and has a rich statistical analysis library and powerful data visualization functions. It is widely used in the fields of statistics and data analysis, and is recommended for researchers and data scientists.
• example:
• Medical research: Statistical analysis is widely used in the medical field, where it plays an important role, for example in analyzing clinical trial data and predicting the risk of disease occurrence.
• Marketing analysis: It is also used in marketing fields that require analysis using statistical methods, such as customer data analysis and market research.
• AI development running on a web browser:
• JavaScript is a language suitable for developing AI that runs on a web browser. By using libraries such as TensorFlow.js, you can train and run machine learning models on a web browser. It is recommended for web developers and those who want to develop interactive AI applications.
• example:
• Chatbots on your web pages: You can embed chatbots on your web pages that answer user questions in real time.
• An image recognition app that runs in the browser: It can analyze images uploaded by users and describe their content or recognize certain objects.

Choose by skill level

• beginner:
  *Python is recommended for beginners in AI development because of its simple grammar and abundant learning resources.
• Experienced person:
• C++ and Java are recommended for those with some programming experience. These languages are suitable for more advanced AI development and larger projects.
  *R is recommended for those with knowledge of statistics and data analysis.

Preparation for starting AI development

There are two main preparations you need to make before starting AI development.

Building a development environment

AI development requires an appropriate development environment.

• OS Selection: Choose the OS you are most comfortable with, such as Windows, macOS, or Linux.
• Building a Python environment: If you are using Python, it is convenient to install a distribution such as Anaconda, as it allows you to install all the necessary libraries and tools at once.
• Install the necessary libraries and frameworks: Install the necessary libraries and frameworks depending on the type and purpose of the AI you want to develop. For example, if you are doing deep learning, install TensorFlow or PyTorch.

Learning Resources

Take advantage of various learning resources to acquire the knowledge and skills necessary for AI development.

• Online Courses: Online learning platforms such as Coursera, Udemy, and edX offer a variety of courses on AI development.
• Books: There are many books on AI development. Choose a book that suits your level, from beginner to advanced.
• Tutorials: The official websites of each library and framework provide tutorials. Through these tutorials, you can learn the basics of how to use them.
• Community, Forums: AI development communities and forums allow you to interact with other developers and ask questions. By actively participating in the community, you can deepen your knowledge and skills.

The latest trends and future of AI development

AI development is evolving day by day, with new trends and technologies constantly emerging. Here we explain the latest trends and future of AI development.

AI development trends

• AutoML (Automatic Machine Learning): AutoML is a technology that automates the construction, training, and evaluation of machine learning models. It is attracting attention because it makes it easy to develop AI models even without specialized knowledge.
• MLOps (Machine Learning Operations): MLOps is a methodology for streamlining the process from machine learning model development to operation. By automating model version management, deployment, monitoring, retraining, etc., it can improve the productivity of AI development.
• Edge AI: Edge AI is a technology that performs AI processing on the device side, rather than on the cloud. It is expected to be used in IoT devices that require real-time performance and security.

The future of AI development

AI development is expected to continue to evolve in the future, leading to developments in the following fields:

• Development of more advanced AI models: We will see the development of more versatile AI models that can handle more complex tasks.
• Addressing ethical challenges of AI: It will become increasingly important to address challenges related to AI ethics, including AI fairness, transparency and accountability.
• Developing AI talent: Developing talent with the knowledge and skills necessary for AI development is essential for the further development of AI technology.

Preparation for starting AI development

Are you interested in AI development? What preparations do you need to make to actually get started? Here we’ll explain in detail the steps to start AI development, from building a development environment to learning resources.

Building a development environment

To begin AI development, you must first prepare a development environment, which is a set of software and hardware for building, training, and evaluating AI models.

• Select your OS:
• Various operating systems can be used for AI development, including Windows, macOS, and Linux. The OS you choose will depend on your personal preferences and the environment you are familiar with, but in general, Linux is recommended as it supports the most AI development tools and libraries.
• Windows: This is the most beginner-friendly OS, but some libraries and tools may not be compatible.
• macOS: A Unix-based OS that supports many AI development tools, just like Linux.
• Linux: This is the standard OS for AI development and supports the most tools and libraries. Ubuntu and CentOS are popular.
• Building Python environment:
• Python is the most commonly used programming language for AI development. By installing a distribution such as Anaconda, you can install not only Python itself, but also major libraries such as NumPy, Pandas, and Matplotlib all at once.
• Anaconda: A Python distribution for data science. It contains many libraries and tools necessary for AI development, making it easy for even beginners to set up the environment.
• Miniconda: A lightweight version of Anaconda. It is useful if you want to install only the libraries you need.
• venv: A virtual environment tool included in the Python standard library that allows you to create an independent Python environment for each project.
• Install required libraries and frameworks:
• AI development utilizes various libraries and frameworks. Install the necessary libraries and frameworks depending on the type and purpose of the AI you are developing.
• TensorFlow: A deep learning framework developed by Google that is suitable for training large datasets and complex models.
• PyTorch: A deep learning framework developed by Meta, suitable for research, development and prototyping.
• scikit-learn: A machine learning library that allows you to easily implement various machine learning algorithms such as classification, regression, and clustering.
• Keras: A library that provides a high-level API on top of TensorFlow. It is easy to use even for beginners because it allows you to define models with simple code.

Learning Resources

Take advantage of various learning resources to acquire the knowledge and skills necessary for AI development.

• Online Courses:
• Online learning platforms such as Coursera, Udemy, and edX offer a variety of courses on AI development.
• Coursera: A wealth of high-quality courses, including courses taught by Stanford University and deep learning authority Andrew Ng.
• Udemy: Offers a wide range of courses on AI development, from the basics to applied science.
• edX: You can take courses offered by world-class universities such as MIT and Harvard University.
• Books:
  *There are many books on AI development. Choose a book that suits your level, from beginner to advanced.
• Deep Learning from Scratch: A popular book that allows you to learn the basics of deep learning while implementing it in Python.
• Deep Learning (Machine Learning Professional Series): A book that provides a detailed explanation of the theoretical background of deep learning.
• Deep Learning with Python and Keras: A book that teaches you how to build deep learning models using Keras.
• Tutorial:
• Each library or framework has a tutorial available on their official website. Through the tutorial, you can learn the basics of how to use them.
• TensorFlow tutorials: https://www.tensorflow.org/tutorials
• PyTorch tutorials: https://pytorch.org/tutorials/
• scikit-learn tutorial: [removed invalid URL]
• Community, Forums:
• AI development communities and forums allow you to connect with other developers and ask questions. By actively participating in the community, you can deepen your knowledge and skills.
• Stack Overflow: A Q&A site where you can ask questions about programming. Many questions about AI development are also posted here.
• Reddit: A community site where various information about AI is exchanged.

The latest trends and future of AI development

AI development is evolving day by day, with new trends and technologies constantly emerging. Here we explain the latest trends and future of AI development.

AI development trends

• AutoML (Automatic Machine Learning):
• AutoML is a technology that automates the construction, training, and evaluation of machine learning models. It is attracting attention because it makes it easy to develop AI models even without specialized knowledge.
• Google Cloud AutoML: An AutoML service provided by Google Cloud that supports a variety of tasks, including image classification, natural language processing, and tabular data analysis.
• Amazon SageMaker Autopilot: An AutoML service provided by AWS that automatically performs data preprocessing, feature engineering, model selection, hyperparameter tuning, etc.
• Microsoft Azure Automated ML: An AutoML service provided by Azure that supports a variety of tasks, including classification, regression, and time series forecasting.
• MLOps (Machine Learning Operations):
• MLOps is a methodology for streamlining the process from development to operation of machine learning models. By automating model version management, deployment, monitoring, re-training, etc., it can improve the productivity of AI development.
• MLflow: An open source platform for managing the entire machine learning lifecycle.
• Kubeflow: A platform for building and running machine learning workflows on Kubernetes.
• TensorFlow Extended (TFX): A platform that helps develop, deploy, and monitor TensorFlow models.
  Edge AI:
• Edge AI is a technology that performs AI processing on the device side, rather than on the cloud. It is expected to be used in IoT devices that require real-time performance and security.
• NVIDIA Jetson: A platform for edge AI development. Equipped with a high-performance GPU, it can process tasks such as image recognition and object detection in real time.
• Google Coral: An edge TPU (Tensor Processing Unit) developed by Google. It achieves high-performance AI processing with low power consumption.
• Intel Movidius: A VPU (Vision Processing Unit) developed by Intel, with processing power specialized for image processing and computer vision.

The future of AI development

AI development is expected to continue to evolve, with developments in the following areas:

• Developing more advanced AI models:
• More versatile AI models that can handle more complex tasks will be developed. For example, we can expect to see the development of AI that can understand human emotions and intentions, and AI that can perform creative activities.
• Addressing the ethical challenges of AI:
• It is becoming increasingly important to address issues related to AI ethics, such as fairness, transparency, and accountability. Guidelines for AI ethics are being established, and active discussions are being held regarding the social impact of AI.
• AI talent development:
• Nurturing human resources with the knowledge and skills necessary for AI development is essential for the further development of AI technology. Universities and companies are expanding their AI education programs and are actively working to develop AI human resources.

Summary: Choose the best language and try your hand at AI development

AI development can be done using a variety of programming languages. The language you choose will depend on the type of AI you are developing, your purpose, and the skill level of the developer. Refer to the features, advantages and disadvantages of Python, C++, Java, R, JavaScript, and Julia introduced in this article to choose the language that’s best for you and try your hand at AI development.