{"id":21714,"date":"2018-04-03T16:19:22","date_gmt":"2018-04-03T16:19:22","guid":{"rendered":"http:\/\/imagecon_2018_keynote_speaker_josh_clark_on_learning_from_machines"},"modified":"2018-04-03T16:19:22","modified_gmt":"2018-04-03T16:19:22","slug":"imagecon_2018_keynote_speaker_josh_clark_on_learning_from_machines","status":"publish","type":"post","link":"https:\/\/cloudinary.com\/blog\/imagecon_2018_keynote_speaker_josh_clark_on_learning_from_machines","title":{"rendered":"ImageCon 2018 Keynote Speaker Josh Clark on Learning from Machines"},"content":{"rendered":"

It was great to connect with our Keynote Speaker Josh Clark<\/a> and get a preview of his talk. Josh<\/a> is a UX designer, design leader, author and the founding principal of Big Medium<\/a>, a New York design studio specializing in future-friendly interfaces for artificial intelligence, connected devices, and responsive websites. He\u2019s also recently back from presenting at SXSW.<\/p>\n

We can\u2019t wait to hear his talk and we hope to see you there too. If you\u2019re in the Bay Area and haven\u2019t registered yet, what are you waiting for? Join us!<\/a><\/p>\n

Your keynote will offer insights into what the future of visual communication might hold when machines run the show. Can you give us a taste for the kinds of things you\u2019ll be exploring in your talk?<\/h3>\n

We\u2019ve arrived at a remarkable moment when machines are suddenly able to make sense\u2014in primitive ways at least\u2014of all the messy ways that we humans communicate: speech, gesture, natural language, doodle sketches, and of course images. In fact, making sense of images is an area where machine learning has made algorithms most reliable.<\/p>\n

My talk will explore what it means now that machines can not only see images and create images, but also to make decisions on our behalf based on what they understand. There are some tremendous opportunities there for us as designers and developers\u2014but there are also risks and responsibilities.<\/p>\n

I challenge all of us to embrace machine learning as an exciting new design material for working with imagery. There\u2019s so much to explore, and so much to learn. Not least, we have to learn how to deal with all the weird, error-prone results the machines still dish out. Perhaps even more, we need to establish new conventions of transparency when algorithms alter or even entirely invent images. This is new territory. There are some mind-bending ethical considerations at work, as well as some daunting design and presentation issues to figure out. I hope to engage the ImageCon audience in these puzzles; they\u2019re both exciting and important.<\/p>\n

You help prepare organizations for the emerging technologies that are likely to be really important in the next year or two \u2013 and you\u2019ve said that one of the really important things about design systems and pattern libraries is being kind to your future self. I love that idea, can you share more about what you mean?<\/h3>\n

Well thanks! Here\u2019s what I\u2019m getting at: none of us like to repeat our work, yet most of us in the tech industry find ourselves solving the same problems over and over again. A web designer at a publisher told me he\u2019s spent the last 15 years of his life designing the same five page templates over and over again. It\u2019s the old saw, \u201cThose who cannot remember the past are condemned to repeat it.\u201d<\/p>\n

Design systems are containers of institutional knowledge. They\u2019re organized anthologies of solutions to common problems. But they\u2019re more than just documentation for an organization; they\u2019re a message to our future selves, a reminder that we\u2019ve already solved certain problems, and here\u2019s how we did it. We don\u2019t have to start from scratch. In fact, it\u2019s often foolish\u2014unkind to ourselves\u2014to start from scratch, though we do it all the time.<\/p>\n

In the technology industry, we tend to convince ourselves that new is always better, or worse, that the thing we\u2019re doing has never been tried before. We sometimes have a willful ignorance of what came before. After you\u2019ve at it for a few decades, you see the same problems or debates come up over and over again\u2014without any awareness of the smart thinking that emerged over the years. We tend to cover the same ground.<\/p>\n

To be fair, it\u2019s not always easy to have productive forward-looking optimism about the future while also having a sturdy, informed view of the past. But the best results come when you do.<\/p>\n

This feels really relevant as we start to think through the ethics and responsibility for machine-generated images. How do we communicate alteration and invention of images? These are issues we\u2019ve wrestled with for centuries in art: forgery vs homage, the ethics of photo editing, the conventions of attribution. Algorithms put all of those considerations into hyper-drive, but the thinking that came before is still relevant and helpful.<\/p>\n

The past tells us more about the future than we often recognize. So here in the present, I always like to think about what I can do to leave hints for my future self.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"","protected":false},"author":41,"featured_media":21715,"comment_status":"","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_cloudinary_featured_overwrite":false,"footnotes":""},"categories":[1],"tags":[68,167],"class_list":["post-21714","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized","tag-conference","tag-imagecon"],"acf":[],"yoast_head":"\nLearning from Machines<\/title>\n<meta name=\"description\" content=\"For our third in a series of Q&A posts with our ImageCon 2018 speakers, we spoke with Josh Clark.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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