On Intelligence

Our inability to tackle the issue may be related, primarily, to a mismatch between the human senses and the physical phenomena we want to understand.

And just as I can learn to understand the motivations of people with values different from mine, intelligent machines can comprehend human motivations and emotions, even if the machine doesn’t have those emotions itself.

So long as the inputs to the cortex are nonrandom and have a certain amount of richness or statistical structure, an intelligent system will form invariant memories and predictions about them.

The business of building intelligent machines could evolve along the same lines as the computer industry, with communities of people training intelligent machines to have specialized knowledge and abilities, and selling and swapping the resultant memory configurations.

Adding depth to the hierarchy will lead to deeper understanding—the ability to see higher-order patterns. Enlarging the capacity within regions will allow the machine to remember more details, or perceive with greater acuity, in the same way a blind person has a more refined sense of touch or hearing. And adding new senses and sensory hierarchies p

They are based on a conflation of intelligence—the neocortical algorithm—with the emotional drives of the old brain—things like fear, paranoia, and desire.

Electrical wires send signals much more quickly than the axons of neurons. A single wire on a chip can be shared, and therefore used for many different connections, whereas in the brain each axon belongs to just one neuron.

What makes it intelligent is that it can understand and interact with its world via a hierarchical memory model and can think about its world in a way analogous to how you and I think about our world.

Unlike humans, whose brains must accompany their bodies, the memory system of an intelligent machine might be located remotely from its sensors (and “body,” if it had one).