Go vs. Python, which is better for AI?

Original article can be found here (source): Artificial Intelligence on Medium

Go vs. Python, which is better for AI?

Golang is now becoming the mainstream programming language for machine learning and AI with millions of users worldwide.

Python is awesome, but Golang is perfect for AI programming!

Golang : Advantages

AI is on the verge of taking the technological world by storm. Machine learning, self-correction, and reasoning are some of the applications that can mimic human intelligence. AI-powered applications are now empowering businesses to improve the usage of their resources resulting in positive effects.

1) High scalability and computation: Golang has a higher potential in scalability and performance as compared to Python. The idea of using Go is because of its high speed as compared to the speed of math computation. For instance, it can cope with complex math problems of up to 20–50 times higher and much faster as compared to Python.

2) Vast AI purposes covered by Golang: Although Go offers small libraries it is consistently growing thus, addressing a large range of AI purposes. Go libraries such as GoLearn (data handling), Goml (passing data), and Hector (binary classification problems) are some of the libraries that serve AI and its applications.

3) Offers a good amount of code readability: Algorithms used in Go offers a minimalist approach allowing developers to easily create readable codes.

4) Ease of usage of Go libraries by Go developers: Most of the Go developers do not need to opt for libraries written in other programming languages. The core advantage of having libraries in Go is that it gives the AI professionals working programming with Go a developer’s comfort.

Python : Advantages

What’s the new black in the IT industry? Most answers will include machine learning and AI, and the results wouldn’t be wrong. Both these technologies have been the mainstream of the IT industry and will remain so until the foreseeable future.

1) Multiple numbers of libraries: Multiple libraries can now help AI engineers build new algorithms, conduct dataset processing, do model processing, work with the most complex data, and many more other functions. Not to forget, TensorFlow is one of the most popular libraries (open source) that is utilized for many machine learning applications of Google.

2) Python as a language is accessible: In business terms, language accessibility simply means having a vast market of experts in Python programming. Moreover, as we’re aware these programming languages are widespread across the globe.

3) Strong community: Python has a well-established and strong community. Based on GitHub’s report in 2019, there was nearly 1 million pull request sent over worldwide. The community tends to contribute toward creating new libraries to extending toolset and updating documentation.