An actual revolution in learning

Humans are so clever that we’ve coaxed silicon (a kind of dirt) to reason like us. We’ve managed this with what is often regarded as “the most complex structure in the known universe:” the human brain. Why, then, have we lavished so much more energy and innovation on dirt than our own brains? The first revolution in learning is born of information theory and computer science. We’ve modeled AI after the human brain. Mnovum trains a human brain like a human would train an LLM.

Books were scarce until the printing press. So when people had access to them, they had to make the most of it. While its popularity waxed and waned, techniques under the vague category of “art of memory” were the only recourse to apprehend and take ownership of the knowledge contained in them. Like the devices we use today, printed books were a means of cognitively offloading memorization. And like the devices we use today, we are all too aware of the dangers and dependence engendered by ubiquitous access instead of native possession of knowledge. This is the second revolution, a return to memory as the foundation of thought.

There are many cliches that extol the virtues of education. We know it’s important, but unlike other important things, it is plagued by vague aspirations, vague outcomes, and vague prospects for the future. For education to fulfill its promise, its impact on ethics, agency, economy, society and self determination must be lifted from the vagaries of conventional learning. Through a mnemonic-cognitive interface, we have adopted, translated, and applied a computing layer to human learning. This layer directly addresses all of the vagaries of education. It enables us to work like machines, but in a way that is exquisitely human- that is, weird, fun, and brilliant.