The advantages and disadvantages of these AI programming languages

01 Python

Python has simple syntax and diverse functions. It is one of the favorite AI development programming languages for developers. For developers, the use of Python for machine learning is very exciting because it is simpler than languages like C++ and Java. Python is also a very portable language as it can be used on Linux, Windows, Mac OS and UNIX platforms. Python is also popular among developers because it allows developers to create interactive, interpretable, modular, dynamic, portable and advanced code, which makes it more unique than the Java language.

In addition, Python is a multi-paradigm programming language that supports object-oriented, procedural and functional programming styles. Due to its simple library and ideal structure, Python is well suited for the development of neural network and natural language processing (NLP) solutions.

Advantages

Python has a rich variety of libraries and tools.

Test algorithms without having to implement them.

Python’s object-oriented design improves developer productivity.

Python runs faster in development compared to Java and C++.

Disadvantages

Developers who are accustomed to using Python will have difficulty adjusting to using a completely different syntax when trying to use other languages for AI programming.

Unlike C++ and Java, Python runs with the help of an interpreter, which makes compilation and execution slower in AI development.

Not suitable for mobile computing.

02 C++

Advantages

C++ is the fastest computer language and it is especially suitable for time-sensitive AI programming projects. C++ is able to provide faster execution time and response time (which is why it is often used in search engines and games). Additionally, C++ allows the use of algorithms at scale and is very efficient in using statistical AI techniques. Another important factor is that C++ supports code reuse during development due to inheritance and data hiding, thus saving both time and money.

C++ is suitable for machine learning and neural networks.

Disadvantages

Multitasking is not good; C++ is only suitable for implementing the core or foundation of a specific system or algorithm.

C++ follows a bottom-up approach and is therefore very complex.

03 Java

Java is also a multi-paradigm language that follows the object-oriented principles and the write once, run anywhere (WORA) principle. Java is an AI programming language that runs on any platform that supports it without recompiling.

In addition to AI development, Java is also one of the most commonly used languages and is compatible with most of the syntax in C and C++. Java is not only suitable for natural language processing and search algorithms, but also for neural networks.

04 LISP

Advantages:

Lisp is the second oldest programming language in the computer programming language family after Fortran. Over time, LISP has developed into a powerful and dynamic programming language.

Some people believe that Lisp is the best AI programming language because of the freedom it provides developers. The Lisp language is used in AI development because its flexibility makes rapid modeling and experimentation possible, which in turn promotes the development of Lisp in AI development. For example, Lisp has a unique macro system that helps explore and realize different levels of intelligence.

Unlike most AI programming languages, Lisp is more efficient at solving specific problems because it adapts to the needs of developers writing solutions. Lisp is great for inductive logic projects and machine learning.

Disadvantages:

Few developers are familiar with Lisp programming.

As an old programming language, Lisp requires new software and hardware to adapt to the current environment.

05 Prolog

Prolog is also one of the oldest programming languages, so it is also suitable for the development of AI. Like Lisp, it is also a major AI programming language. Prolog's mechanisms enable the development of a more flexible framework that is popular with developers. Prolog is a rule- and declaration-based language because it has facts and rules that dictate AI programming languages.
Prolog supports basic mechanisms such as pattern matching, tree-based data structures, and automatic backtracking necessary for AI programming. In addition to being widely used in AI projects, Prolog is also used to create medical systems.