Working To Understand The Digital World Around Us

My partner in crime Audrey Watters and I recently rebranded our umbrella company as Contrafabulists, and along the way, we worked with our friend Bryan Mathers to help us develop some graphics that would help define our work. Bryan quickly developed a logo for Contrafabulists that I think represents what we do--embedding ourselves within the gears of the machine, pushing back on the daily stories from the technology sector.

Bryan has a unique approach to conducting his work. He spent time wth us on a video call discussing our vision, listening to both of us speak, while also applying some of what he already knew of Audrey's Hack Education work, as well as my API Evangelist and Drone Recovery work. From this discussion, he created a banner image, that we use as the banner for the Contrafabulists website -- providing another great visualization of our work.

I love staring into the eyes of the owl, which stares back at you with its mechanical gaze, forcing you to ask the hard questions about how you are using technology. Maybe you are complicit in the stories coming out of the technology sector, or maybe you are just a listener or narrator of these stories being--either way, the owl's eyes quickly get to work understanding you, and what defines you from a technical view.

After we launched the Contrafabulists website, Bryan was listening to our podcast, where Audrey and I rant about the week and he produced an image that was unexpected and resonates with me in some powerful ways. Bryans work illustrates where we are at when it comes to defining who we are in the digital world unfolding around us, while the machines are all learning about us as well.

I do not know which conversation inspired Bryan's work, but I'm assuming it was our discussion around what machine learning technology can do, and what it can't do. Machine learning is a very (intentionally so) abstract term that is being used across the latest wave of rhetoric coming out of the technology sector, that often invokes magical visions in your head about what the machines are learning. Understanding more about what is machine learning is, and what it isn't, is a significant portion of my work as API Evangelist, overlapping with Audrey's work on Hack Education--Bryan's work is extremely relevant and continues to help augment our storytelling in an important way.

There are three significant things going on in his image for me. At first glance, it feels like a representation of what the machine sees of us, when trying to interpret a photo of us using facial or object recognition, defining our face, the space and context around us, while also linking that to other aspects of our social and digital footprint. Then I'm overwhelmed with feelings of my own efforts to define who I am, with each blog post, social media post, or image uploaded--in which the machine is working so hard to understand in the same moment. Then there is the intersection of these two worlds, and the struggle to understand, connect, find meaning, and deliver value--the struggle to define our digital self, something we either do ourselves, or it will be done for us by the technological platforms we operate on.

As I process these thoughts, I would add a fourth dimension to this struggle, something that is very API driven--the role 3rd parties play in defining us, and the world around us, in an increasingly digital world. Our world is increasingly being shaped by platforms, and the 3rd parties who have learned how to p0wn these platforms, whether for ideological or financial gain. Our understanding of the immigration debate is perpetually being shaped by platforms like Twitter or Facebook, and a small group of 3rd party influencers who have learned to shape and game the algorithm.

As we are learning, the machine's are also learning about us, something that is being used against us in real-time, by those who understand how to manipulate the algorithms to achieve their objectives. Helping people understand what we mean when we mean when we say machine learning is difficult--this is because machine learning is technically complicated, but it is also designed to provide a smoke screen for any exploitation and manipulation that is occurring behind the scenes. Machine learning is designed to be understood by a handful of wizards, leaving everyone else to bask in the glow of the personalization and convenience it delivers, leaving no questions regarding the magical capabilities of the machine.

Machine learning is increasingly defining us in the online world, watching everything we do on Facebook, Instagram, Twiter, and via search engines like Google, but it is also beginning to define how we see the physical world around us, helping shape how we see other cities, countries, and places we may never actually visit, and experience in person--algorithmically painting a picture of how we see the world.

Audrey and I are dedicated to understanding the stories coming out of the tech sector, cutting through the marketing, hype, and storytelling accompanying each wave of technology. Machine learning is just one of many areas we work to understand, in an increasingly complex landscape of magic and wizardry being sold via the Internet and applications that are infiltrating our mobile phones, televisions, automobiles, and every other corner of our personal and professional lives. 

I'm thankful to have folks like Bryan Mathers along for the ride, assisting us in crafting images for the stories we tell. I feel like our words are critical, but it is equally important to have meaningful images to go along with the words we write each day. Amidst the constant assault of information each day, sometimes all we have time for is just a couple seconds to absorb a single image, making the photo and image gallery an important section in our Contrafabulists toolbox. I imagine using Bryan's machine learning photos in dozens of stories over the next couple of years, and I'm hoping that eventually, it will continue to come into focus, helping us better connect the dots, and see our digital reflection in this pool we have waded into.