It was great to connect with our Keynote Speaker Josh Clark and get a preview of his talk. Josh is a UX designer, design leader, author and the founding principal of Big Medium, a New York design studio specializing in future-friendly interfaces for artificial intelligence, connected devices, and responsive websites. He’s also recently back from presenting at SXSW.
We can’t wait to hear his talk and we hope to see you there too. If you’re in the Bay Area and haven’t registered yet, what are you waiting for? Join us!
We’ve arrived at a remarkable moment when machines are suddenly able to make sense—in primitive ways at least—of 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.
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—but there are also risks and responsibilities.
I challenge all of us to embrace machine learning as an exciting new design material for working with imagery. There’s 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’re both exciting and important.
Well thanks! Here’s what I’m 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’s spent the last 15 years of his life designing the same five page templates over and over again. It’s the old saw, “Those who cannot remember the past are condemned to repeat it.”
Design systems are containers of institutional knowledge. They’re organized anthologies of solutions to common problems. But they’re more than just documentation for an organization; they’re a message to our future selves, a reminder that we’ve already solved certain problems, and here’s how we did it. We don’t have to start from scratch. In fact, it’s often foolish—unkind to ourselves—to start from scratch, though we do it all the time.
In the technology industry, we tend to convince ourselves that new is always better, or worse, that the thing we’re doing has never been tried before. We sometimes have a willful ignorance of what came before. After you’ve at it for a few decades, you see the same problems or debates come up over and over again—without any awareness of the smart thinking that emerged over the years. We tend to cover the same ground.
To be fair, it’s 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.
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’ve 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.
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.
