Cobots vs robots, and building a social innovation engine
For quite a few years I’ve been trying to understand how to generate and accelerate a societal response to climate change, which I’ve framed as ‘rapid social innovation’. I’m applying systems thinking, so my theory of change is that I need to shift the thinking underlying the system in order to shift the system. In practice this means paying more attention to enabling social innovation, rather than looking for a hardware fix.
Human Operating System upgrade
In essence we need to upgrade our cultural software to live sustainably on planet earth (environmentally and socially). Upgrading the software we operate on is just as important as transforming the hardware we use to supply our energy and do our work. Yes the effort of Elon Musk to provide the hardware we need to move away from fossil fuels has been outstanding, but climate change is but one of a number of planetary challenges, and while hardware buys us a bit more time, the underlying social and cultural aspects must also be addressed.
The point I’m trying to make is that the understanding of the role of people, what makes us tick, and how we can best work together creatively to solve tough challenges is lacking. In complex problems the people part of the equation receives far less attention than say the physical climate system, or even the economics of climate change. So well-resourced problem analysis and response formulation, while rich in sophisticated analysis, often fails to relate this to people — how we created these problems in the first place, and how we will generate a new system by working together to work together differently.
I do think there’s much more we can do to make better use of our collective intelligence. But perhaps it’s also time to acknowledge our human cognitive limits, and augment our intelligence with the help of ‘cobots’, collaborative robots of the cognitive kind. Because in a rapidly changing world, our old ways of knowing and doing are being continuously disrupted. In response, we need to accelerate our learning — to learn faster together to make sense of and navigate our unfolding future, and then be ready to re-learn again.
That’s enough to make anyone’s head spin — if a robot could take some of that cognitive load, I could use some personal time and cognitive bandwidth elsewhere.
Human cognition is limited, yet somehow we need to navigate the world we find ourselves in. People have always invented tools to help them do work, as physical work has evolved from manpower to horsepower to automotive power. We’re now in the knowledge economy and developing artificial intelligence to help us to do cognitive work. But just like tools to do physical work became stronger than us, machines to do cognitive work are becoming smarter than us (which is the scary part).
A few years back I had the opportunity to apply participatory systems modelling as part of a research project on climate change adaptation. The process I used helped a group of people with diverse and conflicting views work together to build a shared understanding of the water supply system for Wellington. I was seeking insights on opportunities for system innovation and transformation and we produced a map to show the underlying structures within the system. A more detailed look at the dynamics illustrated by the map highlighted the importance of building trust and creating space for people to experiment and learn together.
Systems and design thinking approaches put people at ‘the root’ and ‘the centre’ of the problem at hand. But the work of getting diverse groups of people together on social and environmental challenges is time-intensive and under-resourced. That’s why I think we need a digital platform that helps us pool our collective intelligence, getting to the first principles of what works and what doesn’t, including socially and culturally, both for analysing challenges and implementing effective responses. We live in a data-rich world, but we need to structure the data into information so we can use it to make better decisions.
My current thinking is this might look and function something like a mashup of Wikipedia and Loomio, with visualisations such as generated by Kumu, and be used to develop and show underlying structures of systems. In his recent Medium post Connor Turland also list Metamaps as a platform to structure information toward insight and action. Pol.is already combines interactive data visualisations and machine learning to show how public discourse is structured.
At this point my intent is discovery. This is something I have been thinking about for a while and is now becoming buildable. I’ve been chipping away at it by immersing myself in the world of problem solving and decision making. So I’m keen to hear from other people who are working in this space. I’ve also put some guiding questions on Quora (testing our current methods for crowdsourcing answers):
Augmented Human Intelligence (AHI)
As mentioned I also think we could leverage the rapidly advancing opportunities of AI — cobots could do some of this cognitive work for us, and also learn from the information generated. We would still be in the driving seat, responsible for working together to transform our systems, but with collective and augmented cognitive assistance. Here’s my questions on adding AI to the mix:
How might AI help people ‘doing stuff that matters’ to be more effective?
What might AI teach us about being human?