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Latest Programming Languages for AI
Languages for AI to entertain it in the future
In the future, AI will be very close to replicating human intelligent behavior. So, after seeing the miracle in the advancements in the technology of AI in every field from agriculture to the industry/business mostly people want to learn AI.
Therefore, Suggesting some programming language that will greatly help in creating an AI system.
Python is a very easy programming language undoubtedly especially for machine learning, Deep learning, Natural language processing, and neural network. since it has very easy syntax, flexibility, inbuilt powerful libraries like Numpy, Pandas, SciPy, and nltk which will help in creating AI applications to the beginner as well as perfectionist easily.
According to Forbe’s article, ML and DL become very easy after coming of python.
Python keywords provide object-oriented programming and can be integrated with other languages like Java. The development speed of Python is comparatively faster.
It is one of the oldest programming languages for AI. This language has a powerful framework that works with facts, rules, and goals.
A programmer defines these three elements and Prolog establishes relations between them to reach a certain conclusion by analyzing facts and rules. Logical inferences and searches help in the Implementation of algorithms for developing AI systems.
Prolog gives excellent performance in the case of natural language processing. For example, the first chatbot “ELIZA” was developed using Prolog, and later on it has been used in research and education areas for expert systems, theorem, machine learning special cases.
Prolog is also used in academic teaching for running artificial intelligence courses.
This language is also best suited for creating AI applications like graphical user interfaces (GUI), voice assistants, and chatbots.
LISP was one of the oldest programming languages created in 1958 by John McCarthy that is specially used for AI development. AI was introduced after Lisp.
LISP is a fundamental and functional tool for machine learning. LISP was flexible, used for rapid prototyping and dynamic creation of new objects. But coding in this language is not an easy task, it also lacks libraries and has weird syntax.
When someone wants to work with it, a special configuration of hardware and software is required. As it is the original language of artificial intelligence therefore some programming lovers love coding in it but in reality, it is outranked by other AI programming languages.
Java is one of the most popular programming languages with great libraries. Java provides an independent platform, flexible, easier debugging of codes, scalability features, fastest, graphical representation of data.
Its Virtual Machine Technology allows and helps in the development of AI language for different platforms.
C++ is an extension of the C programming language and can be used to build neural networks in DL which is a subfield of AI. The speed of C++ is the greatest benefit for AI complex computations and can make calculations faster.
It has complex syntax but is cost-efficient compared to other languages. The area of AI in which C++ can be used is search engine optimization and ranking.
R language is one of the emerging programming languages of AI that has gained popularity after solving tasks easily.
R is especially good in dealing with large numbers and it is also observed that the researcher’s first choice is R for statistical data and in the future statistical data will play a significant role in developing AI machines.
Like some other Open-source software, R’s packages enable the application of machine learning, data mining, and data analysis tools.
Therefore, currently one has to acquire at least basic knowledge of AI to entertain it in the future, and for the professionals, it is high time to become an AI expert for coming years.
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2. Python Data Structures Data-types and Objects
3. Exception Handling Concepts in Python
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5. Neural Networks: The Rise of Recurrent Neural Networks
6. Fully Explained Linear Regression with Python
7. Fully Explained Logistic Regression with Python
8. Differences Between concat(), merge() and join() with Python
9. Data Wrangling With Python — Part 1
10. Confusion Matrix in Machine Learning
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