Seeing music. Predicting earthquake aftershocks. Finding emojis in real life. These are just a few examples of how researchers, engineers and user-experience (UX) professionals made imaginative ideas real. They made it happen using tools and techniques developed by Google\u2019s People + AI Research (PAIR) team in 2018.<\/p>\n
We founded PAIR in 2017 to\u00a0conduct research<\/a>,\u00a0create design frameworks<\/a>\u00a0and\u00a0build new technologies<\/a>\u00a0that help make partnerships between humans and artificial intelligence productive, enjoyable and fair. One of our main goals is to create easy-to-use tools to\u00a0visualize machine learning (ML) datasets<\/a>\u00a0and\u00a0train ML models<\/a>\u00a0(the mathematical equations that represent the steps a machine will complete to make a decision) in browsers. Put simply, this means anyone with an internet connection can now use ML.<\/p>\n Here\u2019s what PAIR has accomplished over the past year\u2014and here\u2019s how engineers and UX teams can put our resources to use in 2019 and beyond.<\/p>\n In January, we launched\u00a0a library<\/a>\u00a0of user-experience articles and case studies on\u00a0Google Design<\/a>. These show how Google makes decisions to balance our users\u2019 needs for familiarity and trust with new functionality and experiences enabled by AI. The case studies go behind the scenes to show how Google teams developed user experiences for applications, like the fun mobile game\u00a0Emoji Scavenger Hunt<\/a>.<\/p>\n In these articles, practicing user-experience designers offer clear how-tos. They address challenges in designing for AI, such as balancing how to design for habits like swiping or scrolling in certain directions, and building\u00a0personalized experiences<\/a>\u00a0for individual users. We know we don\u2019t have all the answers, so we also seek advice from outside experts, like Paola Antonelli, Senior Curator of Architecture and Design at New York\u2019s Museum of Modern Art (MoMA), who\u00a0answered our team\u2019s questions\u00a0<\/a>on how to use AI as a design material itself.<\/p>\n A key part of our process is partnering with domain experts in other fields. For example, this year we worked with Harvard\u2019s Brendan Meade and the University of Connecticut\u2019s Phoebe de Vries on a model for predicting and visualizing earthquake aftershocks<\/a>. This project led to a state-of-the-art model for aftershock prediction–and, intriguingly, our analysis of the \u00a0AI suggested new, unexpected directions for human researchers to investigate.<\/p>\n In March, we \u00a0hosted our first UX symposium<\/a> in Zurich, featuring external researchers and industry professionals. And in May, we held a panel at \u00a0I\/O, \u201cAI for Everyone<\/a>,\u201d featuring Google engineering leaders with a spectrum of expertise, from cloud computing to climate science, to discuss fair and inclusive AI in these fields.<\/p>\n We\u2019re also dedicated to translating the complicated language behind AI for everyone who uses it, even if they\u2019re not engineers. Since June, our first PAIR writer-in-residence, tech journalist David Weinberger<\/a>, has been embedded in PAIR\u2019s Cambridge, Mass. lab. He\u2019s explaining key AI concepts, like classification<\/a> and confidence levels<\/a>, and timely topics like fairness in machine learning<\/a>, for non-technical audiences.<\/p>\n Using TensorFlow.js, an open-source Javascript library created by PAIR, and other software, a group of musicians, designers, engineers and the Google Creative Lab created Seeing Music<\/a>, which makes it possible to visualize subtle textures in sound.<\/p>\n<\/div>\n<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n We believe in applying deep insights to invent, and open-source, new technologies that can be used by engineers, UX professionals, and other stakeholders who may not be experts in ML.<\/p>\n So we started TensorFlow.js<\/a>, a pure Javascript library that extends TensorFlow<\/a>\u00a0into the browser. Since open-sourcing TensorFlow.js in March, we’ve seen a variety of applications\u2013including a set of accessible creative tools<\/a> for drawing, making music and more, designed by Google\u2019s Creative Lab with collaborators from the accessibility community.<\/p>\n<\/div>\n<\/div>\nCreating a design library\u2014and learning how to design for AI<\/h3>\n
Talking about AI across disciplines<\/h3>\n
New open-source tools for engineers, UXers and beyond<\/h3>\n<\/div>\n<\/div>\n
![\"Seeing](\"https:\/\/storage.googleapis.com\/gweb-uniblog-publish-prod\/original_images\/Seeing_Music.gif\")
![\"Creatability:](\"https:\/\/img.youtube.com\/vi\/c5-bHJqtQS0\/maxresdefault.jpg\")