Best coding languages ​​for AI

Artificial intelligence (AI) opens up completely new possibilities for software developers: With the help of a machine and deep learning, better user-profiles, and recommendations, a higher degree of personalization, smarter search options, or more intelligent interfaces can be implemented. This inevitably raises the question of which programming language should be used for this. The requirements that an AI coding language must meet are diverse: a large number of machine and deep learning libraries should be available as well as a high-performance runtime environment, extensive tool support, a large developer community, and a healthy ecosystem.
Even though this catalog of requirements is comprehensive, you have a few good options to choose from when it comes to programming artificial intelligence. We’ll show you a selection of the Best Artificial Intelligence company in Dubai and AI programming languages.

Python

That being said, Python’s available mathematical and statistical libraries are way ahead of those of other programming languages: NumPy has become so ubiquitous that it can almost be described as the standard API for Tensor Operations, while Pandas carries the flexible data frames from R into the Python world. When it comes to Natural Language Processing (NLP), you have the choice between the time-honored NLTK and the super-fast SpaCy, while the tried and tested sci-kit-learn is recommended for machine learning purposes. On the other hand, when it comes to deep learning, all current libraries ( TensorFlow, PyTorch, Chainer, Apache MXNet , Theano, etc.) basically “Python-first” projects.

C ++

modern C ++ code can actually be pleasant to write. There are several approaches to choose from: Either you use libraries such as Nvidia’s CUDA to write your own program code that flows directly into the GPU — or you can choose to use TensorFlow or PyTorch to gain access to flexible high-level APIs. Both PyTorch and TensorFlow allow you to integrate models written in Python into a C ++ runtime environment. This brings you much closer to productive use while remaining flexible in development.

JavaScript

Learning JavaScript solely to develop AI applications is a highly unlikely scenario. However, Google’s TensorFlow.js still offers a good way to deliver your Keras and TensorFlow models via browser or Node.js.

Nevertheless, the great rush of JavaScript developers in the field of artificial intelligence has so far not taken place. This could be due to the fact that the JavaScript ecosystem has so far lacked the necessary depth in terms of available libraries — at least in comparison to programming languages ​​such as Python. In addition, on the server-side, deployment models with Node.js (again compared to the Python options) do not offer any real advantages. AI applications based on JavaScript should therefore continue to be developed based on browsers.

Swift

Swift for TensorFlow combines the latest and greatest features of TensorFlow with the advantages of Python libraries, which can be integrated easily — just as if you were using Python yourself.

The dev links team is currently working on a Swift version of its popular library — and is holding out the prospect of numerous optimizations, especially in connection with the LLVM compiler. It’s out of the question as production-ready, but the next generation of deep learning development work could emerge on this basis, so be sure to keep an eye on Swift.