A Thousand Brains: A New Theory of Intelligence

by Jeff Hawkins

To endow a machine with goals and motivations requires that we design specific mechanisms for goals and motivations and then embed them into the embodiment of the machine. The goals could be fixed, like our genetically determined desire to eat, or they could be learned, like our societally determined goals for how to live a good life. Of course, any goals must be built on top of safety measures such as Asimov’s first two laws. In summary, an intelligent machine will need some form of goals and motivations; however, goals and motivations are not a consequence of intelligence, and will not appear on their own.

When we are born, our neocortex has an overabundance of wiring. This is pared down significantly during the first few years of life. Presumably the brain is learning which connections are useful and which are not based on the early life experiences of the child. The removal of unused wiring has a downside, though; it makes it difficult to learn new types of knowledge later in life. For example, if a child is not exposed to multiple languages early in life, then the ability to become fluent in multiple languages is diminished. Similarly, a child whose eyes do not function early in life will permanently lose the ability to see, even if the eyes are later repaired. This is probably because some of the connections that are needed for being multilingual and for seeing were lost because they weren’t being used.

To survive long enough to become a galaxy-faring species means that they somehow got past these risks. Therefore, it is likely that whatever brain equivalent they have now would no longer be dominated by false beliefs or dangerously aggressive behavior. There is no guarantee this would happen, but it makes it less likely that they would harm us.

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This next paper introduced our proposal that most dendritic spikes act as predictions, and that 90 percent of the synapses on pyramidal neurons are dedicated to recognizing contexts for predictions. The paper also described how a layer of neurons organized into minicolumns creates a predictive sequence memory. The paper explains many aspects of biological neurons that cannot be explained by other theories. It is a detailed paper that includes simulations, a mathematical description of our algorithm, and a pointer to source code. Hawkins, Jeff, and Subutai Ahmad. “Why Neurons Have Thousands of Synapses, a Theory of Sequence Memory in Neocortex.” Frontiers in Neural Circuits 10, no. 23 (March 2016): 1–13.

Lewis, Marcus, Scott Purdy, Subutai Ahmad, and Jeff Hawkins. “Locations in the Neocortex: A Theory of Sensorimotor Object Recognition Using Cortical Grid Cells.” Frontiers in Neural Circuits 13 (April 2019): 22.