Finally, two years after the workshop. Hopefully there will be another one.
“Cybernetics and artificial intelligence (AI) are often considered the same thing, with cybernetics having something to do with creating intelligent cyborgs and robots. In actuality, cybernetics and AI are different ways of thinking about intelligent systems or systems that can act toward reaching a goal. AI is primarily concerned with making computers mimic intelligent behavior based on stored representations of the world. Cybernetics more broadly encompasses the study of how systems regulate themselves and take action toward goals based on feedback from the environment. These systems are not just computational; they include biological (maintaining body temperature), mechanical (governing the speed of an engine), social (managing a large workforce), and economic (regulating a national economy) systems. In addition to reaching goals, AI and cybernetics both consider how systems can learn; however, while AI considers using stored representations as a means of acting intelligently, cybernetics focuses on grounded and situated behaviors that express intelligence and learning based on feedback and interaction.”
Nikolas Martelaro and Wendy Ju ~ ACM Interactions XXV.6 ★
Deep understanding through some deep human learning.
“(…) UX designers and researchers need to be the co-creators of intelligent solutions to make sure AI technology works for people and society. More than ever, we must consider the capabilities and roles of human versus machine. When should machines make decisions and take action, and when should they augment or support people making decisions? How will these AI solutions make people feel? Do people feel like the solution is trustable, easy, and fun, or do they feel frustrated or even potentially endangered? UX professionals must act to learn, share, collaborate, and participate in cognitive technology research and development both at a strategic level and as a part of the product development process. We should also get involved in governance. We encourage UX professionals to join us and continue this dialog so that we can help create a better world.”
Cindy Lu a.k.a. and Alice Preston ~ UXPA magazine ★
Known design principles applied in new territories.
“Artificial intelligence often looks like magic: sometimes even engineers have difficulty explaining how the machine-learning algorithm comes up with something. We see our job as a UX team as helping people understand how machines work so they can use them better. This doesn’t mean we should explain how a convolutional neural network functions in a simple photo search up. But we should give users hints about what the algorithm does or what data it uses. A good old example comes from e-commerce, where we explain why we recommend certain products. These recommendation engines were the first AI UX many people encountered, many years ago.”
Dávid Pásztor a.k.a. /davidpasztor ~ UX Studio Team ★
Always keep the human in mind, even when the mind is artificial.
“Machine learning (ML) is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It’s a powerful tool for creating personalised and dynamic experiences, and it’s already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it’s clear that UX’ers still have a lot to learn about how to make users feel in control of the technology, and not the other way round.”
Josh Lovejoy and Jess Holbrook ~ IoT for all ★
Thinking, designing and doing with, by and for computers.
“Computational thinking refers to a deliberative process that finds a computational solution for a concern. Computational doing refers to use of computation and computational tools to address concerns. Computational design refers to creating new computational tools and methods that are adopted by the members of a community to address their concerns. Unfortunately, the definitions of both “thinking” and “doing” are fuzzy and have allowed misconceptions about the nature of algorithms. Fortunately, it is possible to eliminate the fuzziness in the definitions by focusing on computational design, which is at the intersection between thinking and doing. Computational design is what we are really after and would be a good substitute for computational thinking and doing. (…) Computational design is where the power of the computing revolution is showing up. Computational design is what we are really after and would be a good substitute for computational thinking and doing.”
Peter J. Denning a.k.a. /peter-denning ~ Ubiquity (August 2017) ★
New technology waves are ahead of us.
“Machine learning is the science of helping computers discover patterns and relationships in data instead of being manually programmed. It’s a powerful tool for creating personalized and dynamic experiences, and it’s already driving everything from Netflix recommendations to autonomous cars. But as more and more experiences are built with ML, it’s clear that UXers still have a lot to learn about how to make users feel in control of the technology, and not the other way round.”
Jess Holbrook a.k.a. /jessholbrook courtesy of O’Reilly Design ★
Re-inventing UX design for new technology waves.
“Through machine learning and artificial intelligence, organizations can use big data to predict our next actions – sometimes even better than we can predict them ourselves. The implications of big data are enormous—enabling us to view suggested products while on a retailer’s Web site, receive recommendations to connect with people who we might know on social-media sites, and benefit from smart IoT devices that gather data from us and those who are similar to us, then act accordingly. Organizations in the healthcare and financial arenas use big-data systems to spot potential adverse events, while also pinpointing scenarios that can bring increased profits and positive outcomes.”
Janet M. Six a.k.a. /janetmsix ~ UXmatters ★
Perfect text for those involved in circle three of Maeda: Computational Design.
“The design and presentation of data is just as important as the underlying algorithm. Algorithmic interfaces are a huge part of our future, and getting their design right is critical—and very, very hard to do. My work has begun to turn to the responsible and humane presentation of data-driven interfaces. And I suspect that yours will, too, in very short order. While constructing these machine learning models is indeed heavy-duty data science, using them is not. Tons of these machine learning models are available to all of us here to build upon right now.”
Josh Clark a.k.a. /joshclark | @bigmediumjosh ~ big medium ★ courtesy of @gnat
Moving ‘Lick’ forward into the design world.
“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. This article illustrates the power of machine learning through the applications of detection, prediction and generation. It gives six reasons why machine learning makes products and services better and introduces four design patterns relevant to such applications. To help you get started, I have included two non-technical questions that will help with assessing whether your task is ready to be learned by a machine.”
Lassi Liikkanen a.k.a. /lassial | @lassial ~ Smashing Magazine ★
A little more on ethics would help.
“We’re on the cusp of a new era of design. Beyond the two-dimensional focus on graphics and the three-dimensional focus on products, we’re now in an era where designers are increasingly focusing on time and space, guided by technological advances in artificial intelligence, robotics, and smart environments.”
Katharine Schwab a.k.a. /katharineschwab | @kschwabable ~ FastCoDesign ★
We’re getting some clear messages on this topic lately.
“As I began to explore how AI would affect design, I started wondering what advice I would give my daughter and a generation of future designers to help them not only be relevant, but thrive in the future AI world. Here is what I think they should expect and be prepared for in 2025.”
Rob Girling ~ O’Reilly Radar ★
Algos as the augmentation tools for designers.
“I’ve been following the idea of algorithm-driven design for several years now and have collected some practical examples. The tools of the approach can help us to construct a UI, prepare assets and content, and personalize the user experience. The information, though, has always been scarce and hasn’t been systematic. However, in 2016, the technological foundations of these tools became easily accessible, and the design community got interested in algorithms, neural networks and artificial intelligence. Now is the time to rethink the modern role of the designer.”
Yury Vetrov a.k.a. @jvetrau ~ Smashing Magazine ★
ML eats XD for breakfast, lunch, and diner.
“Traditionally the experience of a digital service follows pre-defined user journeys with clear states and actions. Until recently, it has been the designer’s job to create these linear workflows and transform them into understandable and unobtrusive experiences. This is the story of how that practice is about to change. Over the last 6 months, I have been working in a rather unique position at BBVA Data and Analytics, a center of excellence in financial data analysis. My job is to make the design of user experiences reach a new frontier with the emergence of machine learning techniques. My responsibility — among other things — is to bring a holistic experience design to teams of data scientists and make it an essential part of the lifecycle of algorithmic solutions (e.g. predictive models, recommender systems). In parallel, I perform creative and strategic reviews of experiences that design teams produce (e.g. online banking, online shopping, smart decision making) to steer their evolution into a future of ‘artificial intelligence’. Practically, I boost the partnerships between teams of designers and data scientists to envision desirable and feasible experiences powered by data and algorithms.”
Fabien Girardin a.k.a. /fabiengirardin ~ D&A blog ★ courtesy of puttingpeoplefirst
Magic from the Machine.
“This paper describes techniques in computational creativity, blending mathematical modeling and psychological insight, to generate new magic tricks. The details of an explicit computational framework capable of creating new magic tricks are summarized, and evaluated against a range of contemporary theories about what constitutes a creative system. To allow further development of the proposed system we situate this approach to the generation of magic in the wider context of other areas of application in computational creativity in performance arts. We show how approaches in these domains could be incorporated to enhance future magic generation systems, and critically review possible future applications of such magic generating computers.”
Howard Williams and Peter W. McOwan ~ Frontiers in Psychology ★
From code to language: algorithms.
“As experience designers, we rely more on algorithms with every iteration of a Web site or application. As design becomes less about screens and more about augmenting humans with extended capabilities, new ideas, and even, potentially, more emotional awareness, we need algorithms. If we think of experience designers as the creators of the interface between people and technology, it makes sense that we should become more savvy about algorithms.”
Pamela Pavliscak a.k.a. /pamelapavliscak | @paminthelab ~ UXmatters ★
After digital disruption we’re now moving into computational disruption.
“Artificial Intelligence promises everything from self-driving cars to self-writing newspapers, but AI may be missing its greatest opportunity in healthcare, where AI-driven ‘conversational interfaces’ hold untapped potential to influence the health and wellbeing of billions of people.”
Thomas Sutton a.k.a. /thomasthinks | @thomas_thinks ~ frog Designmind ★
The blend of creativity, design, and deep understanding of digital technology.
“A primary goal of this research project was to find a set of guiding principles, metaphors and ideas, that inform the development of future theories, experiments, and applications. By combining different domains into one narrative, we formulate a new school, or praxis for creativity: CreativeAI. Its desire is to explore and celebrate creativity. Its goal is to develop systems that raise the human potential. Its belief is that addressing the “what” and “why” is as important as the “how”. Its conviction is that complex ethical questions are not an afterthought, but an opportunity to be creative collectively. Finally, CreativeAI is a question, rather than an answer. Its only demand is more collaboration and creativity. It is an invitation for play!”
Samim Winiger a.k.a. /samimwiniger | @samim & Roelof Pieters a.k.a /roelofpieters | @graphific ~ Medium ~ courtesy of karsalfrink