These days, AI programming has gone far beyond pure software or hardware development firms. Companies within most demanded verticals, like e-commerce, real estate, healthcare, and more, start adopting AI.
Python and Golang are the most popular programming languages for AI. As a company that has worked with both—we know it can be difficult to choose the right one.
Recently we have examined Golang web programming capabilities comparing it to other languages. You may check our discoveries as for Golang vs Node JS, Go vs Ruby, and of course, Golang vs Python comparison.
Now, it’s time to see which language, Go or Python, is better specifically for AI programming.
What Python brings to AI programming
You can hardly consider any programming language perfect, but certainly, Python has its strengths in the context of AI. Here are the most significant ones:
Extensive set of libraries
Python libraries help engineers build new algorithms (LightGBM), do model prediction (Eli5) and datasets processing (Keras), work with complex data (Scikit-Learn), and more. Not to mention Tensorflow, the most popular open source library used for many of Google’s machine learning applications.
Well established community
The community and the ecosystem around Python are vibrant and active. According to GitHub annual statistics, last year the global Python community sent over 1 million of pull requests. Community contributes much into creating new libraries, updating documentation, and extending toolset.
Python is an accessible programming language, and it keeps gaining more ground. For businesses, accessibility means a vast market of Python experts. Besides, this language is widespread. Recently it’s been ranked by the Institute of Electronic and Electrical Engineers as the top programming language of 2018.
However, while Python is sometimes referred to as the best programming language for AI, it has its disadvantages.
Bad for large-scale engineering
When it comes to work involving a few hundred programmers, Python clearly losing to Golang scalability. It’s also challenging to use Python if you require a very ordered and disciplined way to do programming. The same is true when you are going to deploy very complex AI systems.
Codebase may be difficult to maintain
Python offers many libraries, supports multiple systems, and third-party integrations. But, such variety often plays against Python.
From many projections, developers state that Python is not easy to maintain. How so? Python lacks several language features like static type system. Also, its syntax is confusing and goes against assumptions that other programming languages make. Plus, libraries related to different versions of Python often conflict with each other. Such conflicts cause problems with configuring specific cluster or even leads to a general stop of working code.
Lack of performance and multicore processing
Another challenge with Python is its performance, specifically CPU and GPU processing. There are ways to get around this challenge, but they are mostly tweaks. What works for specific use cases often just can’t be applied for most common uses.
Too many versions of Python available
Even the most dedicated Python programmers find this point painful. The transition between versions and disconnection between Python 2 and Python 3 are just several issues. Also, simultaneously having several versions can require installing different environments. When you need them ready to work immediately, it can cause a mess and technical problems.
One more challenge that adds developers confusion is packaging systems. Packaging systems in different versions are broken down in different ways which is hard to manage and document. In turn, different packaging systems may require installing multiple environments.
Golang advantages for AI programming
Does Golang have what it takes to beat Python? Let’s take a look at Golang advantages.
Libraries written in Go comfortable for Go developers
Golang developers don’t need to call out to libraries written in other languages. Programming a purely Go solution means having fewer pieces from different languages. But the main advantage of having these libraries in Go isn’t deployment, but developer comfort.
Covering vast AI purposes
The number of libraries Golang offers is small (but consistently growing) and addresses a wide range of purposes. Go libraries cover the need for data handling (GoLearn), binary classification problems (Hector), and passing data (Goml). Also, Golang has Theano, a library similar to Python’s TensorFlow. Theano provides Go developers with pieces of algorithms that can be reused.
Good at scale and computations
Unlike Python, Golang scales and performs well within large-scale projects. Another reason to use Golang for AI programming is its speed, especially when it comes to the speed of math computations. To compare, Go copes with complex math problems up to 20-50 times faster than Python.
Minimalism and good code readability
Most of Go’s algorithms stick to a minimalist approach. This allows developers to create very readable code after algorithm implementation. Yet, this minimalist approach can also be a weak point. For instance, when there’s a need for recursive algorithms, which can run slower due to the absence of tail-call optimization.
Golang downsides for AI programming
Talking about weak points, we suggest looking at these two.
Need for deep AI expertise
Some Golang advantages for web development can play against it regarding AI programming. For instance, default multithreading is helpful for Golang web development. However, using multithreading for AI purpose requires seasoned Go developers with deep expertise in data science.
Golang toolkit extension in progress
Of course, Go has its libraries for AI and is capable of covering the most essential purposes, yet the toolkit is not as extensive. Basically, it’s in the process of extension along with Golang community itself. So far, Golang developers have performed about 285k pull requests globally, according to GitHub.
Python keeps striking the list of the most demanded languages for AI programming. However, Golang is expanding its territory gradually. So far, Go has served great for web apps. Now it also has good potential for AI programming. Clean codebase, reused algorithms, and good scalability makes Golang a great technology for AI. As Golang company, we truly believe its growing community will contribute much to overall AI programming.