Regrettably for many, resources, in the traditional sense, remain scarce, and, in some instances, increasingly so. How- ever, if we extend the horizon outward, beyond the remote village, crowded slum, and other types of local environments, a treasure trove of value-creating, even value-multiplying, resources awaits, ready to be tapped.
Knowledge of Indigenous medicine, often frowned upon in the modernized, high-tech West, is both cheap and effective. Modern approaches that force technical solutions from outside, although helpful, can be detrimental, as context is often lacking. Peoples who have lived in the same place for thousands of years have become one with their environment. As such, the best solutions to local problems often come from what we call human deep learning, as opposed to the more common form of deep learning present in AI and neural networks.
How do we expand our knowledge horizon to include both humans and AI? We each have at our disposal a vast network with billions of nodes and trillions of connections, located within an even greater ecosystem of energy centers, chemical plants, and colonies teaming with life. And that’s just within the individual human physiology. Add to that the many systems, networks, and ecosystems that make up the rapidly growing technical infrastructure of our planet, and the possibilities of what we can accomplish become truly infinite.
What we KM’ers can do
There is no better way to align humans and machines, the natural and the computational, than through KM. Call it next-generation KM if you like. For example, using AI for more accurate translation enables better cross-fertilization of ideas across cultures ... true diversity! Caution: Watch out for global, one-size-fits-all solutions. Rather, we need to remain open to understanding the myriad differences in each of the many localities dotting our world. Then, in the spirit of KM best practices, encourage adapting rather than forcing everyone to adopt the same paradigms.
As for filling the mind–fuel gap, AI and automation, combined with good old-fashioned human know-how, are making huge strides in addressing the world hunger/nutrition problem. Improved prediction, planning, and response to extreme weather events and supply chain disruptions are resulting in better yields, irrigation, harvesting, packaging, and distribution. The same goes for more efficient production and distribution to support the ever-growing demand for energy from robotics and AI.