Five programming languages to study for AI development

Programming languages hold significant importance as they serve as the instruments that developers utilize to build software, applications, and websites. Each programming language possesses its own syntax, structure, and functionality, rendering them appropriate for particular tasks and projects. Acquiring knowledge and comprehension of programming languages is crucial for developers to produce efficient and effective code, as well as to collaborate with fellow developers on various projects.

Below are five programming languages worth learning for AI development.

Python

Python is a favored option for artificial intelligence (AI) development due to its ease of use, clarity, and adaptability. It boasts an extensive array of libraries and frameworks for machine learning, natural language processing, and data analysis, including TensorFlow, Keras, PyTorch, Scikit-learn, and NLTK.

These tools enable users to create and train neural networks, manage large data sets, interpret natural language, and much more. Additionally, Python is a popular language for AI research and education, with numerous online tutorials and courses available for individuals looking to embark on AI development, thanks to its user-friendly nature and community support.

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Lisp

Lisp is a programming language that originated in the late 1950s, making it one of the oldest programming languages still in use today. Lisp is recognized for its distinctive syntax and robust support for functional programming.

Having been utilized to develop some of the earliest AI systems, Lisp has historically had a considerable influence on the field of AI. It is a suitable choice for AI research and development due to its support for symbolic computation and its ability to treat code as data.

Although Lisp is not as commonly used as some of the other languages mentioned earlier in AI development, it still retains a loyal following among AI professionals. Many AI researchers and developers appreciate Lisp for its expressiveness and capacity to handle complexity. Common Lisp Artificial Intelligence (CLAI) and Portable Standard Lisp (PSL) are two prominent AI frameworks and libraries implemented in Lisp.

Both CLAI and PSL are Lisp-based artificial intelligence frameworks, with CLAI concentrating on expert systems and PSL offering a portable implementation of the Common Lisp programming language.

Lisp is the Metallica of the programming world

— Stephen Bolton (@skbolton) April 4, 2023

Java

Java is a general-purpose programming language frequently employed in the development of large-scale enterprise AI applications. Due to Java’s reputation for security, reliability, and scalability, it is often used to create complex AI systems that must handle substantial amounts of data.

Deeplearning4j, Weka, and Java-ML are just a few of the libraries and frameworks available for AI development in Java. These tools allow users to create and train neural networks, process data, and implement machine learning algorithms.

Furthermore, Java is a popular choice for developing AI applications that function across multiple devices or in distributed environments, owing to its platform independence and support for distributed computing. Given Java’s prominence in enterprise development, a large community of Java developers and a wealth of resources are available for those looking to start AI development in Java.

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C++

C++ is a high-performance programming language that is often utilized in AI development, particularly when designing algorithms and models that require speed and efficiency. Known for its low-level hardware control, C++ is frequently employed to develop AI systems that necessitate precise management of memory and processor resources.

TensorFlow, Caffe, and MXNet are some of the libraries and frameworks for AI development available in C++. These tools enable users to create and train neural networks, process data, and implement machine learning algorithms.

C++ is also widely used in the gaming industry, where it is employed to develop real-time game engines and graphics libraries. This experience has translated into the creation of AI applications that require real-time processing, such as autonomous vehicles or robotics.

While C++ may present a steeper learning curve compared to some other languages, its power and speed make it a favored option for constructing high-performance AI systems.

Do you know?
The name of C++ signifies the evolutionary nature of the changes from C. “++” is the C increment operator#cplusplus #coding #CodeNewbie #learntocode #programming #100DaysOfCode #codinglife

— C++ Programming (@CProgramming1) August 12, 2022

R

R is a programming language and software environment designed for statistical computing and graphics. It is widely utilized in AI development, particularly for statistical modeling and data analysis. R is a preferred choice for developing and analyzing machine learning models due to its strong support for statistical analysis and visualization.

Caret, mlr, and h2o are just a few of the libraries and frameworks available in R for AI development. These technologies facilitate the building and training of neural networks, the application of machine learning techniques, and data processing.

In academia, where research and data analysis are prevalent, R is also highly regarded. Researchers aiming to conduct complex data analyses or develop predictive models often choose it for its user-friendly interface and robust statistical analytical capabilities.

Which programming language is used in DApp development?

Blockchain technology has emerged as a transformative force across various sectors, including finance, healthcare, and supply chain management. Consequently, there is an increasing demand for developers skilled in blockchain programming languages.

Solidity is among the most widely used programming languages for creating smart contracts on the Ethereum blockchain, while JavaScript is often employed to develop decentralized applications (DApps). Python serves as a versatile language utilized for a range of blockchain-related tasks, from building analytics platforms to developing smart contracts, whereas Go and C++ are favored alternatives for constructing high-performance blockchain systems.

As the blockchain landscape continues to evolve, it is possible that new programming languages may emerge to meet the demands of developers working in this dynamic and rapidly growing field.