Seven years ago, when traveling to Italy, I experienced the vagaries of data and its weird, unimaginative influence on our lives. Since then, the absurdity of what data-driven intelligence throws at us on a daily basis has increased exponentially. I wrote about it in an essay, 40 kilometers. It was part of a series of essays I wrote about data, its implications, and the emergence of limited-intelligence algorithms. If you are interested, here are some links to those articles in my archives.
- Why data without a soul is meaningless
- With big data comes big responsibility.
- Uber, Data Darwinism and the future of work
Somehow that article, 40 kilometers, from seven years, ended up in the email inbox of my good friend Steve Crandall, who wrote a wonderful email reply in response. I thought it would be worth sharing and asked for his permission. Here it is:
The ‘data-driven world that we find all around us has little to do with science where data is highly contextualized and serendipity is welcomed and even hunted. I think the notion of art is will be, or at least should be, important.
Operating as a simple person I like to make a distinction between awe and wonder. Both have multiple definitions, so I use my own. Awe is a feeling of overwhelming majesty or even fear that seems to be beyond what we can understand or control. Wonder is a deep feeling of curiosity that leads to questions that can be addressed. It’s scale may be big or small, but it can be consuming at any scale.
Wonder is what I’m after and some of the paths have been decades long. As a student in Pasadena I’d go on a long bike ride down to one of the beaches with the cycling club once or twice a month. Being wasted from the ride and contemplating a more strenuous return I’d get lost watching gulls or the waves and surf. I’d wonder about waves and that led me down a few paths. The path I was taking wouldn’t naturally bump into fluid dynamics, but I started learning about the Navier-Stokes equation .. core in the study of fluid dynamics. There were people to talk to and papers to read. The equations look simple, but are usually too difficult to solve analytically or exactly numerically in most real-world cases. You learn tricks and the importance of the Reynolds Number as a guide for cheating. I started to understand why the waves were doing what they did, but that led to other questions including the gulls.
A few decades later I did some work on the flight of sports balls – particularly volleyballs as they’re one of the most interesting cases and that led to a friendship with Sarah Pavan and talks so far from my world that new sets of questions and thoughts sparkled into being. Those waves were a long-term serendipity gateway and there have been dozens more. I don’t know if a computer can help me in the wonder and initial serendipity part, but computer mediated communication, and synchronous is often the best kind, has certainly been an amplifier. So much of it is finding and bringing other wondering minds to the dance.
Steve’s right — what we called data-driven intelligence is not really intelligence. Instead, it is a somewhat simplistic rendering of the conclusions from the data. It lacks the ever-changing context and serendipity — something I experienced on that long drive to Siena.
July 7, 2021, San Francisco