According to PwC reporting, there shall be a rise to USD 15.7 trillion to make 2030 by just applying AI in the global businesses and economy. This figure shows the greatness of the AI field in the present world. To be a part of this success story, you can begin by learning the AI programming languages.
The 4 best programming languages for artificial intelligence are described below:
Python is an interpreted and object-oriented language. It is a high-level AI programming language that is supported by dynamic semantics. The data structure is high-level built-in and stands in combination with dynamic typing and dynamic binding. Thus, it is impressive for Rapid Application Development. Python is an easy-to-learn language with a simple syntax, enabling results and readability in cost-effective program maintenance. In addition to this, Python provides modules and packages which permit program modularity and code reuse. Python’s extensive library and interpreter are accessible in binary form or sources without any extra payments needed. They can be applied to all critical platforms and can also be distributed at no cost.
Strengths of Python
- If AI engineers opt to use Python, they benefit from good community support, flexibility and large libraries.
- Python’s specific characteristics provide the advantage of extensive frameworks for deep learning machine learning and platform independence.
- Python has its positive aspects also viewable at its short, concise, readable classes that present much activity in clear and shortcode.
Java programming is a general-purpose and object-oriented AI programming language, and it is so designed for software development for electronic devices. For instance, electronic devices like TVs, VCRs, and others. Java is not connected to any specific hardware or operating system, and therefore it is called a platform-neutral computer programming language. It is important to note here that it goes as “Write once, run anywhere” in Java. It is considered one of the best programming languages worldwide, and its applicability in the last 20 years has proved it.
Strengths of Java
- Its impressive flexibility, user-friendliness, and independent platform find application for programming related to AI projects in many methods.
It is an environment for computing and statistical graphing and is very much extensible. In addition to this, it is also a programming language for AI. R enables many statistical calculations (linear and non-linear modeling, time-series analysis, classical statistical tests, classification, clustering, and many others).
Strengths of R
- The well-designed plots of R language can be generated with much ease. This comprises mathematical symbols and formulae at the spots where the need for it arises.
- The qualities that turn R into a good reason for AI Engineer’s application are:
The main characteristic of R is that it is excellent for dealing with huge numbers. This benefit places R in a better position than even Python. Also, it is realized that Python, as compared to R, has an unrefined package known as Numpy.
By applying R, the applicant can deal with many programming paradigms, for instance, functional programming and vectorial computation.
It is a logic programming language for AI that exercises an essential role in AI. The main feature of Prolog language is that it is specifically targeted as a declarative language. In the Prolog language, logic Is presented as relations (Known as Rules and Facts).
Strengths of Prolog
- Prolog helps create an essential tool for AI development, that is, Natural Language Processing.
- Prolog presents with the best outputs when applied for the making of chatbots. ELIZA was the first chatbot created by applying Prolog.
Python, R, Java, and Prolog are the most important languages in the AI world. If you have knowledge of the same, you will always be welcomed in the vast AI globe of opportunities and career growth.
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