I was thinking about intelligence, how humans and AIs articulate intelligence, trying to understand what intelligence actually is; and I had an intuitive insight that all of intelligence and reasoning can be mapped to generalized problems like A* and TSP; that is problems that are instantiated as a graph of nodes with connections between them, where each node can be seen as state with an outcome value and that the connection paths articulate traversal costs.

In this domain, intelligence essentially maps to optimal path finding. An algorithm that is able to find a better, more optimal path between the nodes that lowers the cost or with a higher score is a better, smarter, more intelligent algorithm.

That is not to say that all the graphs are simple or that all possible states are known or that or that the cost might not change dynamically. Real world intelligence requires all those things; in extremely large and complex graphs, where not all of the nodes are well know or if at all, and where the traversal interconnections may have multiple competing cost values and so on.

But my overall intuition was that all intelligence maps to this graphing problem, that all knowledge domains can essentially be mapped to this paradigm, and how well an algorithm navigates these graphs fundamentally equals and articulates its intelligence.

There isn’t more to it. This is what intelligence fundamentally is.

And yet all that fells sterile and empty.

And then I had the second intuitive epiphany. Yes, that’s what intelligence is, but in this paradigm, what are emotions?

And then I realized, in human and biological brains, emotions are updates or changes to the node map’s underlying values. So for example, it might make perfect sense to navigate a given path to reach an outcome, but then if danger arises (we see a grizzly bear) we will feel fear, concern or worry that updates the cost value of that node traversal, and suddenly it no longer makes sense, it’s no longer intelligent, to continue this path. The map has changed.

Emotions are the state changes of updating the node and path values.

“Emotions” is just a word we use for this underlying complex phenomenon of node and path cost values changing. We also use words like desire, and values and so on. These articulate the phenomenon of updating the cost/reward values within the node map.

So that was my double epiphany -

• Intelligence can be articulated as route optimization problems over highly complex node value graphs (including unknowns and probabilistic domains).

• Emotions and desire refer to value changes of and within these existing graphs.

These insights could be a paradigm to better understand the human condition, how and why we have different value judgments, how to build AI system that better serve humanity’s goals, and so on.