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Programming Languages for AI

[Nebraska State - Forbes]


- Overview

Artificial intelligence (AI) is already having a positive impact on a wide range of industries. It can automate processes, free employees from unnecessary labor, provide students with personalized learning options, enable cybersecurity companies to deploy faster solutions, and help fashion companies design better-fitting garments for their customers. 

Even ChatGPT is applying deep learning to detect coding errors and produce written answers to questions. And this is just the beginning. 

The AI, as we see it today, is called “narrow AI” or “weak AI.” That is because it can only perform narrow tasks such as facial recognition, an internet search or driving a car. The long term goal is to create “general AI” or “strong AI,” which would be able to outperform a human in any cognitive task.

Some of existing AI examples are:

  • Spam filters 
  • Voice to text features 
  • Automated customer support 
  • Manufacturing robots 
  • Self-driving cars
  • Smart assistants
  • Healthcare management
  • Automated financial investing
  • Virtual travel booking agent
  • Social media monitoring
  • Marketing chatbots


There are many more examples. AI has brought technology to another level.

If you are interested in AI, working on your own AI projects, then you will need to know what the most popular AI programming languages are. 

There are quite a few AI programming languages, and there is none of them that can be called “the best ai programming language.” They all have their pros and cons, and following are some of them: Python, Scala, Java, R, Javascript, Lisp, C++, and Prolog.

Python is a general-purpose language that can be used for a variety of AI needs, including machine learning, deep learning, and computer vision. Python is easy to learn and has a large developer community. 


- Python and R Languages for AI and Machine Learning

There are quite a few AI programming languages, and there is none of them that can be called “the best ai programming language.” They all have their pros and cons, and following are some of them: Python, Scala, Java, R, Javascript, Lisp, C++, and Prolog

R and Python both share similar features and are the most popular tools used by data scientists. Both are open-source and henceforth free yet Python is structured as a broadly useful programming language while R is created for statistical analysis.

Python is a programming language that is preferred for programming due to its vast features, applicability, and simplicity. The Python programming language best fits machine learning due to its independent platform and its popularity in the programming community. 

  • General-purpose language: Python is regarded as a better choice if your project demands more than just statistics. For instance — designing a functional website. Smooth Learning Curve — Python is easy to learn and easily accessible which enables you to find the skilled developers on a faster basis.
  • The bulk of Important libraries: Python basts countless libraries for munging, gathering, and controlling the information. Take an occasion of Scikit-realize which comprises tools for information mining and investigation to support the incredible AI convenience utilizing Python. Another bundle called Pandas gives engineers superior structures and data examination devices that help to diminish the improvement time. If your development team demands one of the major functionalities of R then RPy2 is the one to go for. 
  • Better Integration: Generally, in any engineering environment, the Python integrates better than R. Thus, regardless of whether the designers attempt to exploit a lower-level language like C, C++ or Java, it generally gives better joining different components with Python wrapper. Additionally, a python-based stack is anything but difficult to incorporate the remaining task at hand of data researchers by bringing it easily into creation. 
  • Boosts Productivity: The syntax of Python is exceptionally decipherable and like other programming languages, however unique in relation to R. In this way, it guarantees high profitability of the development groups.  


- Python

Although Python was created before AI became crucial to businesses, it’s one of the most popular languages for AI. Python is the most used language for Machine Learning (ML). One of the main reasons Python is so popular within AI development is that it was created as a powerful data analysis tool and has always been popular within the field of big data. 

As for modern technology, the most important reason why Python is always ranked near the top is that there are AI-specific frameworks that were created for the language. 

One of the most popular is TensorFlow, which is an open-source library created specifically for ML and can be used for training and inference of deep neural networks.

Other AI-centric frameworks include: 

  • scikit-learn – for training ML models.
  • PyTorch – visual and natural language processing.
  • Keras – serves as a code interface for complex mathematical calculations.
  • Theano – library for defining, optimizing, and evaluating mathematical expressions.


- Advantages of Python

The following are the advantages of Python over other languages.
  • Simple: Python has a simple syntax that is easy to learn and read. This makes it a good choice for beginners and experienced programmers alike.
  • Free: Python is free and open-source software. This means that it is free to use and distribute.
  • Easy to use: Python has a large and active community that supports it. This means that there are many resources available to help you learn and use Python.
  • Highly compatible: Python is compatible with a wide range of operating systems and platforms. This makes it a good choice for developing cross-platform applications.
  • Object-oriented: Python is an object-oriented language. This means that it uses objects to represent data and behavior. This makes it a good choice for developing large and complex applications.
  • Has lots of libraries: Python has a large number of libraries available. These libraries provide pre-written code that can be used to perform a variety of tasks. This can save you time and effort when developing your own applications.
  • Has built-in data structures: Python has a number of built-in data structures. These data structures can be used to store and organize data. This can make your code more efficient and easier to read.
  • Widely applicable: Python can be used for a wide variety of tasks. This includes web development, data science, machine learning, and more.

- Disadvantages of Python

  • Includes a very few statistical model packages.
  • Due to the presence of the Global Interpreter Lock (GIL), threading in Python becomes tricky and quite problematic. Subsequently, multi-threaded CPU-bound applications act slower than single-thread ones. An AI undertaking is more valuable for executing multiprocessing instead of utilizing multithreaded programming.


- R

R was developed by statisticians and basically for the statisticians which any developer can predict the same by looking at its syntax. As the language contains mathematical computations involved in machine learning which is derived from statistics, R becomes the right choice who wants to gain a better understanding of the underlying details and build innovative. 

If your project is heavily based on statistics then R can be considered as an excellent choice for narrowing down your projects which requires one-time dive into the dataset. For instance - if you like to analyze a corpus of text by deconstructing paragraphs into words or phrases to identify their patterns then R is the best choice.


[More to come ...]

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