Artificial intelligence consists of large collections of connected computational units called artificial neurons, loosely analogous to the neurons in our brains. To train this network to “think”, scientists provide it with many solved examples of a given problem.

Suppose we have a collection of medical-tissue images, each coupled with a diagnosis of cancer or no-cancer. We would pass each image through the network, asking the connected “neurons” to compute the probability of cancer. https://chow420.com/forum/chow420-hemp-general-store-for-vetted-cbd-products-NjQ3 We then compare the network’s responses with the correct answers, adjusting connections between “neurons” with each failed match. We repeat the process, fine-tuning all along, until most responses match the correct answers. Eventually, this neural network will be ready to do what a pathologist normally does: examine images of tissue to predict cancer. https://chow420.com/forum/chowpods-smart-cbd-vending-machines-near-you-NjAz This is not unlike how a child learns to play a musical instrument: she practices and repeats a tune until perfection. The knowledge is stored in the neural network, but it is not easy to explain the mechanics.