Andrew Ferlitsch

I strongly believe that my lifelong experience makes me one of the most ideal individuals to teach concepts of deep learning. When this book is first printed, I will be nearly 60 years old. I have a wealth of knowledge and experience that translates to expectations today in the workforce. In 1987, I got my advanced degree in artificial intelligence. I specialized in natural language processing. When I got out of college, I thought I would be writing talking books. Well, it was the AI winter.

I took other directions in my early career. First, I became an expert in government security for mainframe computers. As I became more proficient designing and coding in operating system kernels, I became a kernel developer for UNIX, being one of the contributors to today’s heavyweight UNIX kernel. In those same years, I participated in shareware (before open source) and was the founder of WINNIX, a shareware program that competed with the commercial MKS Toolkit for running the UNIX shell and commands in a DOS environment.

Subsequently, I developed low-level object code tooling. I became an expert at both secured-level computing and compiler/assemblers for massively parallel computers in the early 1990s. I developed MetaC, which provided instrumentation into the operating system kernels of both conventional operating systems and highly secured and massively parallel computers.

In the late 1990s, I made a career change and became a research scientist for Sharp Corporation of Japan. Within a couple of years, I became the company’s principal research scientist in North America. Over a 20-year period, Sharp filed over 200 US patent applications on my research, with 115 granted. My patents covered areas for solar energy, teleconferencing, imaging, digital interactive signage, and autonomous vehicles. Additionally, in 2014–2015 I was recognized as a leading world expert on open data and data ontologies, and founded the organization opengeocode.

In March of 2017, at a nudging of a friend of mine, I looked into “what’s this thing called deep learning?” It was natural for me. I had a big data background, had worked as an imaging scientist and research scientist, had an AI graduate degree, worked on autonomous vehicles—it all seemed to align. So, I made the leap.

In the summer of 2018, Google approached me about being a staff member in Google Cloud AI. I accepted a position that October. It’s been a great experience at Google. Today, I work with vast numbers of AI experts within both Google and Google’s enterprise clients, teaching, mentoring, advising, and solving challenges to make deep learning operational on a large production scale.