S.B. Divya’s new science fiction thriller, Machinehood, is set in a not-too-distant future when people have access to tabletop biotech labs that churn out everything from cures for new diseases to performance-enhancing drugs. But they find that taking such drugs is all but mandatory as they compete for paying gig work in an economy where more and more jobs can be done by artificial intelligence.
Before she was a published author, Divya was an engineer with a background in computational neuroscience and data science, as well as computing hardware and software. She talked to Fast Company about how her work has shaped her writing, the not-quite-dystopian world she envisions in Machinehood, and why she’s still optimistic about the future.
The interview has been edited for length and clarity.
How did your tech career inform your writing?
I actually started college intending to go into astrophysics, and after a couple of years I got sideswiped by a really interesting new department at Caltech at the time, which was computational neuroscience. That was kind of an interesting hybrid of physics, electrical engineering, and neurobiology. I found it really fascinating. I felt like the brain was as mysterious as the far reaches of our universe, which was surprising and intriguing. I ended up working in medical devices, and I was still able to put a lot of my machine-learning knowledge to use there in the applications of pattern recognition.
Then I went on to getting a master’s degree in signal processing and high-speed communications, which led me to a couple of interesting jobs. I had a stint at a startup doing digital music fingerprinting, which was really fun. We were trying to take an MP3 file and match it up to identify what song it was, which is obviously an application that is in use today. At the time, we intended to use it as a plug-in for peer-to-peer services like Napster and Scour, which are totally gone and have been replaced by paid music.
Then, I went on to working in chip design, so the high-speed semiconductor industry. I was working on VDSL, and then Ethernet, and then I cycled back to very specifically machine learning with the rise of data science over the past few years. I switched from being more in the hardware side to purely back into the software domain.
