August 28, 2019

Soon I'll turn 65, so I better get on with digitizing my life. My granddaughter is 5, my son and daughter are in their 30s, and if plans work out, future interactions with them might include conversations with me as a digital avatar.

Several of my colleagues have written their autobiographies, either humorous stories published in book format, or they have blogged for a while. I prefer leaving an interactive copy of myself wired to know some of my best stories and answer questions. It won't be an easy task — I've been digitizing steadily since the late 1970s once I built my first computer and started measuring and mapping the outdoors using gadgets and widgets. You might say I have led a digitally abundant life.

As an explorer — a digital naturalist — I've been figuring out ways to digitize nature and amplify our understanding of the complexity of biological diversity. I've worked with interdisciplinary teams of faculty and students to design, engineer, and deploy new tools for remote sensing of ecosystems using specialized cameras, environmental sensors, and robots. The robots we've designed were each specialized for different tasks, capable of autonomous flight surveys of vegetation, collecting water samples, navigating along cables strung through the forest canopy, collecting microscopic images of root hairs and fungi, or floating and swimming as they measure water quality and assess coral reef health. We have learned a lot through the use of digital tech, but what has motivated me the most was the sense of urgency to digitize as much of nature as possible. It has been an impossible task.

Now I'm retired, I have a new urge to reprocess my life's work and recover as much of the original data, or its metadata, to re-purpose those studies into environmental story-telling, and hopefully, to entertain. Better yet, if my stories can motivate young people to carry on with our duty to protect and study nature — and protect ourselves. Working against me has been the rapid change in digital formats, so many digital files or media types older than ten years are nearly impossible to open or convert — that is unless you saved the original hardware and software. I've also found that tools to back up files can be flawed, leaving unfortunate gaps that become, well, extinct.

Thanks to my career at Cornell University, Cal Poly University Pomona, and the University of California, I have used many computers starting with an IBM 360 mainframe, DEC PDP-11 minicomputer, and too many desktop PCs to count! With the evolution of programming languages, operating systems, file formats, and applications, I've found the technology learning curve to be steep but fun. The digital archive of my professional life contained the punch cards used in graduate school, back-up tape drives, large floppy disks, then smaller ones. My first hard disc drive was a 10MB attached to an Apple IIe, then a 20MB drive in my first Mac, 40MB in the Mac II. There was a progression from 1GB to 500GB drives in the subsequent 10 years. My current SD card stores 256GB, while my portable USB-C external drives hold 5 TB of data… yet, they keep filling up! Operating systems and file formats evolve, eventually becoming incompatible with older versions. On a Mac, for example, without knowledge about the resource fork, you cannot open a file from an old operating system with a new one.

My first foray into artificial intelligence came when a computer language called Lisp, inference engines, knowledge bases, and expert systems were innovative. Not the same as the fuzzy-logic, Bayesian neural-net machine learning approaches of today — expert systems were all about digesting domains of knowledge into collections of if-then rules, or decision-trees and then applying an inference system to resolve patterns — not unlike a mirror of our logical thinking process. The Apple Technology group, led by Alan Kay, had discovered my early work and seeded our lab with Macs and a software program called Smalltalk, written on top of Lisp. We also received beta copies of Bill Atkinson's newly written HyperCard. Without deep-diving into the computer science weeds, both programs are well-suited for modeling the hierarchical classifications common to plant and animal taxonomy. With these tools, my students and I wrote programs to identify common wildflowers and their habitats using a visual question-and-answer approach that took on the metaphor of a nature walk.