Solving Problems with Knowledge Graphs and GCI
The digital revolution has likely driven the single greatest transformation in the history of human civilization, but it might pale in comparison to the next great transformation: human-centric knowledge graphs (KGs) and functional computing approaches like general collective intelligence (GCI) that leverage such graphs.
The digital revolution arose from the discovery that problems and solutions could be modeled digitally (i.e., in terms of ones and zeros) and from mass production of the transistors required to compute solutions to digital problems at exponentially greater speed and scale. But computer software or hardware created to solve one problem must be reengineered by people to solve another problem. Unlike the human brain, which has general problem-solving ability, computer hardware has only narrow problem-solving ability.
Human-centric functional modeling (HCFM) is a way to allow computers to solve general problems. HCFM represents problems via constructs called “functional state spaces.” These hypothetical functional state spaces are special types of KGs used to model systems. Functional state spaces are required for GCI and are of unprecedented importance if, as predicted, GCI can exponentially increase our capacity to understand systems.
For example, GCI has the potential to automatically reapply existing solutions to an exponentially greater number of different problems without any additional programming. Furthermore, the exponential increase in general problem-solving ability predicted to be possible through GCI applies to every process from design to recycling for every product or service that can be modeled with functional state spaces.
Taken a step further, GCI might be required to solve several key societal challenges corporations face. For example, humans can’t reliably discern social good — we tend to discern whatever matches the ideology to which our cognitive bias is predisposed. This may be why our current, non-GCI corporate environmental, social, and governance programs have had limited success.
Additionally, as technology advances, a phenomenon called the “technology gravity well” is expected to cause decision-making to prioritize the interests of an ever-decreasing minority of individuals and businesses at the expense of achieving collective social good.
It can take a long time for groups to understand when an individual is about to make (or has made) a decision that serves the group poorly (because of the sheer volume of decisions made by individuals within any large group and the often intractable number of potential interactions between those decisions). Looking out only for oneself is much easier, and those doing so might see benefit quickly.
Therefore, any force that acts to continually centralize decision-making to an ever-decreasing minority of individuals and businesses would be expected to work to serve the interests of that minority far faster than collectively optimal choices can be understood and made by any group: a technology gravity well.
[For more from the author on this topic, see “Knowledge Graphs & General Collective Intelligence: Shifting to Industry 5.0.”]