Cracking Open the Black Box of AI with Cell Biology
Computer scientists provide the framework for a neural network by setting up layers, each of which contains thousands of “neurons” that perform tiny computational tasks. The trainers feed in a dataset (millions of cat and dog photos, millions of Go moves, millions of driver actions and outcomes), and the system connects the neurons in the layers to make structured sequences of computations. The system runs the data through the neural network, then checks to see how well it performed its task (how accurately it distinguished cats from dogs, etc).
Finally it rearranges the connection patterns between the neurons and runs through the dataset again, checking to see if the new patterns produce a better result. When the neural network is able to perform its task with great accuracy, its trainers consider it a success.