Future UI Design Without Buttons
Since the dawn of graphical user interfaces, we’ve been using buttons. Consider that Xerox PARC’s original GUI is 44 years old, yet our user interfaces still look remarkably like it. I recently traced the history of button styles by creating the Dribbble Timeline. Though buttons evolved in sync with current trends and alongside technology, their origin is undoubtedly inspired by real objects of the past. For more than a decade, we have been creating devices without a physical interface—that don’t depend on human touch but can be activated by voice or gesture. Why do we persist in creating shapes with which to interact that are based on the familiar objects that surround us? The shape of a digital button is still modeled on tools and mechanisms we developed in the 19th century!
Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study
Deep neural networks (DNNs) have advanced performance on a wide range of complex tasks, rapidly outpacing our understanding of the nature of their solutions. While past work sought to advance our understanding of these models, none has made use of the rich history of problem descriptions, theories, and experimental methods developed by cognitive psychologists to study the human mind. To explore the potential value of these tools, we chose a well-established analysis from developmental psychology that explains how children learn word labels for objects and applied that analysis to DNNs. Using datasets of stimuli inspired by the original cognitive psychology experiments, we find that state-of-the-art one-shot learning models trained on ImageNet exhibit a similar bias to that observed in humans: they prefer to categorize objects according to shape rather than color...
Human, AI and UX
Artificial Intelligence And The Age Of Empathy - CONSCIOUS
Our products and services as we know them today are bound to change fundamentally: driverless cars, software that could predict what people want to purchase, virtual assistants, administrative and care staff, augmented reality, speech recognition services, education, banking, travel, and entertainment. Everything is about to become smarter but can we also expect things to become more empathic? Can we aim for the age of emotional intelligence?
The Human Brain: Ultimate Supercomputer – Health Transformer
At first glance, brain-computer interface (BCI) technology seems like it is straight out of a sci-fi thriller. A scientist hooks dozens of nodes to a patient’s head — and suddenly doctors can read the patient’s brain activity and the patient can communicate electronically and, by some miracle, control external machines. In fact, as I write this, I can’t believe it myself. Can we really do that? In a short answer: yes, we can. Or at least, we are constantly getting better at it.
Making Chatbots Talk — Writing Conversational UI Scripts Step by Step
As a content writer, working in UX design agency, I’ve learned to accept the fact that visuals usually have much bigger impact than the text. From my perspective, this is a bit frustrating. So when my team was faced with the task of designing a website chatbot, I was really excited: finally the time has come for writing to take over!
Books and Resources 📖
Data Visualization for Human Perception | The Encyclopedia of Human-Computer Interaction, 2nd Ed.
Data visualization is the graphical display of abstract information for two purposes: sense-making (also called data analysis) and communication. Important stories live in our data and data visualization is a powerful means to discover and understand these stories, and then to present them to others. The information is abstract in that it describes things that are not physical. Statistical information is abstract. Whether it concerns sales, incidences of disease, athletic performance, or anything else, even though it doesn't pertain to the physical world, we can still display it visually, but to do this we must find a way to give form to that which has none. This translation of the abstract into physical attributes of vision (length, position, size, shape, and color, to name a few) can only succeed if we understand a bit about visual perception and cognition. In other words, to visualize data effectively, we must follow design principles that are derived from an understanding of human perception.