We are building self-driving technology for learning: non-invasive systems that raise attention and understanding without asking humans to burn proportionally more energy.
Problem
For most people, learning is physically and mentally expensive. Attention, retention, and deep understanding still require too much effort for the output they get back.
Vision
We are building non-invasive technology that guarantees the same or more human learning with significantly less physical and mental effort.
Goal
Increase attention markers without a proportional energy cost to the user, then compound that into a full automation stack for human learning.
Self-driving learning needs a policy and two complementary layers. Epistemic foraging is the policy: search for information that reduces uncertainty, rather than chasing scores. Then we measure what an entity currently holds, and steer transformation toward useful configurations with less wasted effort.
Policy
Epistemic foraging (Friston / active inference) is an active search for information that reduces uncertainty about an environment, rather than immediately chasing rewards. Tomography measures the state being foraged; induction is later steering.
Measurement
Knowledge tomography is the family of methodologies that prompt human as well as agentic entities to try to reproduce their state of knowledge—multi-angle projections of what is held, missing, and transferable, not finals alone.
Long-horizon aim
Knowledge induction is the longer-horizon aim: technology that guides transformation through knowledge configuration space—raising proximity to useful states without asking minds to burn proportionally more energy. Tomography measures; induction transforms.
We keep the layers distinct on purpose. Epistemic foraging is the policy. Tomography externalizes and reconstructs current state. Knowledge induction tech uses that measurement to steer learning. Without tomography, induction is blind steering; without induction, measurement never compounds into self-driving learning.
