The Editorial Team at Inside Big Data recently wrote a very insightful article about the future of artificial intelligence. They pointed out that advances in artificial intelligence are creating a new era of technological automation.
The editors pointed out that Python is the programming language that is engineering a new world governed by AI. Python is currently used to create most AI algorithms. Programmers that want to pursue a future in machine learning or AI programming generally need to develop a proficiency with Python first.
However, there is a strong possibility that other programming languages could be used to create AI code instead. Ruby could be a better programming language for certain AI projects at some point, although new machine learning and AI libraries need to be tweaked. We decided to take a nuanced look at the subject. We are personally skeptical that Ruby will replace Python, although it is possible. At the same time, we do believe that Ruby will be more popular for creating machine learning projects in the future.
Machine learning programmers should understand the basics of both Python and Ruby. They may want to consider developing a stronger understanding with Ruby to ensure greater job security if it becomes a popular machine learning language in the future.
To develop this understanding, they should familiarize themselves with the primary benefits of both programming languages. More companies may need to hire ruby on rails programmer if it becomes more widely supported for machine learning projects. The nuances of Python and Ruby in the context of machine learning are summarized below.
Why Python is currently the most popular language for machine learning
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Python wasn’t originally chosen to be the default language for machine learning developers because of its superior capabilities. It was primarily chosen for its perceived popularity and greater support from mainstream academics. As Python became more popular, it developed larger datasets that became more useful for machine learning projects.
Python is also widely used for machine learning because it can use platforms like TensorFlow to rapidly develop machine learning algorithms. You can gain a greater appreciation for the role of TensorFlow in machine learning development below.
Why TensorFlow Helps Make Python an Ideal Machine Learning Language
The Google Brain team developed TensorFlow back in 2015. The project was originally intended for internal use. It has since been used by other programmers for machine learning applications.
One of the biggest reasons that Python is widely used for machine learning projects is due to notebooks, which make writing code with large teams much easier. A notebook is a file generated by Jupyter Notebook that can be edited from a web browser, which means the programmer can mix Python code execution with annotations and provide a high level of versatility to share part of the code with annotations through the comfort and platform independence that a web browser offers.
The Collaboratory environment in TensorFlow is one of the reasons this language is so widely used for machine learning. It is highly convenient for developing new code. Most Python programmers can use the Collaboratory environment especially if they don’t have GPUs in their computer. This is a Google research project created to help disseminate Machine Learning education and research. It is a Jupyter notebook environment that requires no configuration and runs entirely in the Cloud allowing the use of Keras, TensorFlow or PyTorch. Notebooks are stored on Google Drive and can be shared just as you would with Google Docs. This environment is free to use and only requires a Google account and also allows the use of a GPU for free.
Once the developer has accessed the Google Collaboratory, they must connect the environment by selecting “CONNECT” on the top right of the menu (which after a few seconds you should see indicates with a green check that everything is correctly initialized). Then open a notebook through the “File” tab in the top left of the menu, and select the “New Python 3.0 notebook” option.
In summation, the Python interface is highly intuitive and ideal for creating machine learning code. However, this doesn’t meant that Ruby couldn’t be used to develop machine learning projects as well.
Could Ruby become more viable as a machine learning programming language?
A couple of years ago, a post on Reddit asked why Ruby was not frequently used in machine learning projects. Most of the users agreed that the reason is primarily that Ruby is not used commonly in academic environments. However, new machine learning libraries have recently emerged. This is helping Ruby become a decent language for AI projects.
The lack of mainstream adoption of Ruby has still curtailed its ability to emerge as a mainstream programming language for most projects, including machine learning. The fact that Ruby is not heavily backed by computer science departments in major universities means that there aren’t as many tool kits for the language.
However, this is starting to change as more independent developers create open-source libraries that can be used for machine learning. One of these is known as Rumale. This new library was announced on Dev.to last year. It is still a fairly new application but is already showing a lot of promise.
Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python. Rumale supports Linear / Kernel Support Vector Machine, Logistic Regression, Linear Regression, Ridge, Lasso, Factorization Machine, Naive Bayes, Decision Tree, AdaBoost, Gradient Tree Boosting, Random Forest, Extra-Trees, K-nearest neighbor classifier, K-Means, Gaussian Mixture Model, DBSCAN, Power Iteration Clustering, Mutidimensional Scaling, t-SNE, Principal Component Analysis, and Non-negative Matrix Factorization, the Dev.to author writes.
Ruby could become a mainstream language for machine learning if these new libraries become more widely adopted. However, they will need to undergo further testing and work out many of the bugs.
