humans. Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality.

expectations. Whether or not you agree with Harari’s theory, the connection between imagining and causal relations is almost self-evident. It is useless to ask for the causes of things unless you can imagine their consequences. Conversely, you cannot claim that Eve caused you to eat from the tree unless you can imagine a world in which, counter to

causation. In his book Sapiens, historian Yuval Harari posits that our ancestors’ capacity to imagine nonexistent things was the key to everything, for it allowed them to communicate better.

expectations. Whether or not you agree with Harari’s theory, the connection between imagining and causal relations is almost self-evident. It is useless to ask for the causes of things unless you can imagine their consequences. Conversely, you cannot claim that Eve caused you to eat from the tree unless you can imagine a world in which, counter to

causation. In his book Sapiens, historian Yuval Harari posits that our ancestors’ capacity to imagine nonexistent things was the key to everything, for it allowed them to communicate better.

from processors of data to makers of explanations was not gradual; it was a leap that required an external push from an uncommon fruit. This matched perfectly with

Say when Virgil first proclaimed, “Lucky is he who has been able to understand the causes of things” (29 BC

Say when Virgil first proclaimed, “Lucky is he who has been able to understand the causes of things” (29 BC).

instead of representing probability in huge tables, as was previously done, let’s represent it with a network of loosely coupled variables. If we only allow each variable to interact with a few neighboring variables, then we might overcome the computational hurdles that had caused other probabilists to stumble.