J is for Jupyter Notebooks

From A to I to Z: Jaid’s Guide to Artificial Intelligence

Remember the lab reports from your high school biology class, where you recorded your experiments and observations? 

Jupyter notebooks are the AI equivalent: a collection of documents containing live code, written explanations, equations, and visualizations. 

The information is stored in an interactive, open-source, browser-based environment, and made available to scientists and engineers working on various AI projects. 

Sharing data in this manner makes it easier to communicate findings and, ultimately, move the field of AI forward. The data can also be edited in real time, which means teams working on the same projects can collaborate even if they’re located in opposite corners of the world. 

Jupyter notebooks are designed to be modular. In other words, every bit of data can be stored individually in components called cells and, in the case of code, executed independently. 

This makes it easy to organize and manage complex projects, as well as test or experiment without breaking everything. Different teams can even work on different parts of a project simultaneously without stepping on each other’s toes. 

Jupyter notebooks are also highly customizable. They support a huge number of programming languages and integrate with cloud-based infrastructure. 

You can also build custom extensions and export data as PDF, HTML, Markdown, and other formats for easy sharing with a wider audience.

Some facts:

“Jupyter” is a mishmash of Julia, Python, and R —  the three core programming languages Jupyter notebooks support. Originally, the project supported only Python and was called iPython. Over time, it grew to include more languages, and the name was changed to reflect this. 

Today, Jupyter notebooks support over 100 programming languages, including JavaScript, PHP, C++, Ruby, CSS, and SQL. 

Fernando Pérez built iPython — the first iteration of Jupyter notebooks — while working on his PhD in particle physics in 2001. 

The data in his thesis was too complex for the standard Python shell to handle, so he built an enhanced shell that better-suited his requirements. The end-result worked so well that he developed it into a general-purpose platform and extended support to other programming languages. 

Jupyter notebooks are now open source, which means they’re maintained and improved collaboratively by thousands of developers. And anyone can leave feedback, make suggestions, or access the platform. 

The project is funded from a variety of sources, from academic institutions to foundations and charitable trusts. Aside from advancing AI technology, their interactivity and accessibility makes them ideal for teaching data science, programming, and other technical subjects.

Want to know more?

This article is a comprehensive guide on Jupyter notebooks for complete newbies. It covers everything you need to know to get started in plain language — from creating a new notebook to customizing it with add-ons and extensions. 

If you’re interested in taking things further, Jupyter’s documentation, aptly called The Jupyter Notebook, is surprisingly readable and easy to follow, even if you’re not especially technical.

Jaid’s perspective

Jupyter notebooks have been instrumental in advancing AI, because they make it easy to break down large projects into manageable parts, share knowledge, and work together. From a customer services perspective, they’re extremely useful for processing and analyzing chat logs, customer feedback, and other data which can then be used to make chatbots and other AI-powered apps more reliably helpful, and for tracking performance over time.

Take advantage of Jaid’s AI-powered platform to optimize your business functions and customer data – contact us today!