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 ★
Digital designers moving slowly into computational design.
“Machine learning is going to radically change product design. But what is the future of machine learning? Is it the singularity, flying cars, voiceless commands, or an Alexa that can actually understand you? Before we can even get to that part–the grand futurism part – I want to offer a provocation: Machine learning won’t reach its potential–and may actually cause harm – if it doesn’t develop in tandem with user experience design.”
Caroline Sinders a.k.a. /caroline-sinders | @carolinesinders ~ FastCoDesign ★
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 ★
Innovation always happens at the edges.
“Research papers from the AAAI User Experience of Machine Learning Symposium ~ Consumer-facing predictive systems paint a seductive picture: espresso machines that start brewing just as you think it’s a good time for coffee; office lights that dim when it’s sunny and office workers don’t need them; just in time diaper delivery. The value proposition is of a better user experience, but how will that experience actually be delivered when the systems involved regularly behave in unpredictable, often inscrutable, ways? Past machine learning systems in predictive maintenance and finance were designed by and for specialists, while recommender systems suggested, but rarely acted autonomously. Semi-autonomous machine learning-driven predictive systems are now in consumer-facing domains from smart homes to self-driving vehicles. Such systems aim to do everything from keeping plants healthy and homes safe to “nudging” people to change their behavior. However, despite all the promise of a better user experience there’s been little formal discussion about how design of such learning, adaptive, predictive systems will actually deliver. This symposium aims to bridge the worlds of user experience design, service design, HCI, HRI and AI to discuss common challenges, identify key constituencies, and compare approaches to designing such systems.”
Mike Kuniavsky a.k.a. @mikekuniavsky, Elizabeth Churchill a.k.a. @xeeliz, and Molly Wright Steenson a.k.a. @maximolly