Since AI and machine learning incorporated into products,
I have got interested in thinking about how to improve UX design using the new technology. As I keep learning and trying to solve more problems to come, I found that it’s a challenge for UXers to understand deeply like engineers. Although there are various materials about AI and machine learning, here I want to share some designer-friendly and useful resources to broaden our knowledge.
Understanding AI and Machine Learning
AI and machine learning are more challenging to work with than current programming logic because it applies the statistical framework to produce results. While it was not easy to grasp the concept, the following materials are more understandable comparing to engineering-focused explanations.
Machine Learning for Designers
Author Patrick Hebron not only introduces you to contemporary machine learning systems but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs.
A Visual Introduction to Machine Learning
Machine learning explained in interactive visualizations. If you keep scrolling, you can understand the way of how a data set becomes a machine learning model to distinguish homes in New York from homes in San Francisco with interactive visualizations. Also, if you go to http://www.r2d3.us/, you can explore more concepts.
An Executive’s Guide to AI | McKinsey & Company
This guide covers the overview of history and business use cases of machine learning and deep learning. With rich visualizations, you can easily understand what it is, when to use it and how it works.
Google AI Adventures — YouTube
If you pursue the more technical understanding of AI and Machine learning, there’s no other way to see materials that cover inside of its technology. I recommend Google AI adventures videos which are easily accessible to programming methods.
Exploring Creative AI Experiments
Google AI experiments provide inspiring experimental works using machine learning API. You can imagine creative possibilities by exploring deep learning based pictures, drawings, language, music. Also, it’s possible to launch them and get the code.
Embracing Predictive Recommendations and Personalization
UXers have already got used to machine learning features such as predictive recommendations and personalization. The cognitive scientist, Kristie Fisher suggests four principles to consider when introducing ML features into a UI for a right balance between habituation-friendly UI and algorithmically-generated recommendations.
Machine Learning to Evolve the Human-Computer Relationship
Microsoft has addressed the relationship problem between the user and machine learning system. The process based on the company’s governing principles for AI design had included the relationship should evolve and adapt to users.