CategoryMachine Learning

August 7, 2023 — Friction often has a negative connotation in user experience design, but it actually has many benefits. Its best-known use case is mitigating unintended consequences in high-risk scenarios, yet it has a new place in the age of artificial intelligence. Adding strategic friction to interfaces can lead to profound efficiency gains in machine learning algorithms. Read more…

October 2, 2020 — In this article, Edoardo Cavazza will show you how to use face recognition with Tensorflow in order to extract some information from the camera, such as the distance between the screen and user’s face or the amount of people reading the page. Then, we will pass those data to CSS in order to adapt typography and to adjust the page layout. Read more…

September 9, 2019 — Using JavaScript and frameworks like Tensorflow.js is a great way to get started and learn more about machine learning. In this article, Charlie Gerard covers the three main features currently available using Tensorflow.js and sheds light onto the limits of using machine learning in the frontend. Read more…

January 9, 2019 — The scope of possibilities to apply Google Cloud Vision service is practically endless. With Python Library available, it can certainly help you bring out deeper interest in Machine Learning technologies. Google Vision API turned out to be a great tool to get a text from a photo. In this article, Bartosz Biskupski will guide you through the development process with Python in a sample project. If you’re a novice, don’t worry. You will only need a very basic knowledge of this programming language — with no other skills required. Let’s get started! Read more…

September 7, 2018 — What are the fundamentals of machine learning, and what are the necessary tools to evaluate risk and other concerns in a machine learning application? The goal of machine learning is to find patterns in data and use those patterns to make predictions. In this article, Alvin Wan will cover everything you need to get started. By the end, you should have an understanding of how to advance your practice and study of machine learning. Let’s begin! Read more…

August 29, 2018 — how can we detect the moment you and your phone are in the kitchen, or bedroom, or living room? With today’s commodity hardware, there are a myriad of possibilities: In this tutorial, Alvin Wan will show you how to build a desktop app that predicts which room you’re in using a simple machine learning algorithm: least squares. The code applies to any platform, but we only provide dependency installation instructions for Mac OSX. Read more…

April 4, 2018 — How can you gain a better understanding of your audience? Well, one way would be to use user-generated content that can be integrated into Machine Learning (ML) technology. In this article, Jamie Munro demonstrates how you can turn social media content into a powerful understanding of how the people creating the content think. Using the results from Watson, your application can use the categories of interest where the percentile exceeds or is less than a predetermined amount to change how you target your audience. Read more…

February 6, 2018 — Today’s article is all about facial recognition technology. Despite all of the limitations of facial recognition, such as variations in posing, lighting and image quality, the technology is gaining in popularity and eventually will become a part of users’ everyday lives. In this article, Nataliia Kharchenko and Illia Chemolosov decided to share their experience of dealing with facial recognition algorithms and engines and things they’ve learned. Read more…

April 24, 2017 — As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. To have your say about how best to use it, you need a good understanding about its applications and related design patterns. In this article, Lassi Liikkanen illustrates the power of machine learning through the applications of detection, prediction and generation. To help you get started, he has included two non-technical questions that will help with assessing whether your task is ready to be learned by a machine. Read more…