I have always worked at the leading edge of technology, as that is what has interested me. Pushing forward into the unknown, creating the future, and discovering new ways to think about things and achieve things is tantalising.
Most of my work has been for private companies, but there have been a few publications out of that and a lot more research, which I will gradually start to share here.
I started writing code, loving the algorithmics of it: performance tuning, optimising, and finding clever ways to do things. I soon found out from my first manager that people who were asking for new business applications didn't necessarily want rocket ships, racing cars, or polymorphic transforming self-healing automata.
That's when I started drifting upwards from code into design, requirements, system architecture, enterprise architecture, and ultimately strategy, transformation and innovation.
I always wondered where it would end up and what would be next; the truth is, this is up for grabs, open to interpretation, a game of thrones, or perspectives and ideas and ideals at least.
Innovation, as vague and meaningless as that term is, which holds different truths and balances for different people and organisations, seems like the confluence of problems and solutions and thought and action and even emotion and will and motive.
As we increasingly move further into a world that once was only to be found in sci-fi novels, sharing more seems important.
- Measuring Innovation A paper exploring what innovation is, what it means to different people in different contexts, and different ways to measure it depending on your priorities and motivations. It explores the different phases of innovation, what it takes to innovate in terms of commitment, time, and resources, and provides examples from industry of leading practices, tools, and methodologies.
- Connected Car Ecosystem A paper about the future of automotive exploring ways participants in the ecosystem can work together to enable self-driving cars. This paper includes research on topics as esoteric as the number of lines of code in the space shuttle, an F35 fighter jet, the Large Hadron Collider, and 'modern connected cars', although when I wrote this, the cars were not self-driving - but they still had more lines of code inside them than those other platforms I just listed put together. Maybe AI can help us use fewer lines of code.
- Elements of Southbeach 0.9 A paper about Southbeach Notation, a visual language and notation for modelling situations in terms of their useful and harmful functions and the effects between those functions. Based on TRIZ, the engineering methodology for system improvement, I created this with Howard Smith at the point where I had hit the ceiling of abstraction in terms of finding ways to think bigger, help people remove blind spots inhibiting progress, and create more holistic ways to improve systems, processes, and ideas. After transitioning from software engineering through system design, enterprise architecture, transformation, and innovation, I was looking for ways to look more deeply into the contradictions and tensions that everyone trying to make progress faces every day. Howard came across TRIZ, and we realised it was an inspired way of looking at what problems are and how to solve them. We set about creating a superset of TRIZ that was more generally applicable across business, technology, and life and called this Southbeach Notation because we drew the initial diagrams on the sand at a beach in Southbeach, Florida, between conference sessions we were presenting at. As far as the universe is concerned, there are no problems; stuff just happens. As far as humans with needs, opinions, desires, and goals are concerned, problems are the things that stand between where you are and where you want to be. The paper is technical and includes a full description of the notation visuals and semantics, lots of examples, and enough detail to implement a formal reasoning engine based on this. The basic premise of creating a formal notation was that it could be used as input to a program that, given some basic principles of innovation, creativity, and problem solving, could identify all the problems and harmful influences in the system modelled, deconstruct them into their parts, and make many permutations of suggestions for ways to improve the system. Our goal was to cover the potential solution space. Of course, we never fully covered it and continued to think of new ways to understand challenges and improve situations and continue to discuss this to this day. A reference implementation for the notation and such a reasoning system was created by Southbeach Solutions. I have many experiments on systematic creativity and problem-solving in Python and Jupyter Notebooks, which I plan to tidy-up and share here when time permits.