In machine learning, data with a local representation typically has 1 unit per element. A 5-word vocabulary might be defined by a 5-dimensional vector, with $[1,0,0,0,0{]}^T$ denoting the first word, $[0,1,0,0,0{]}^T$ denoting the second word, and so forth.

Distributed representations are the opposite, instead of concentrating the meaning of a data point into one component or one “element”, the meaning of the data is distributed across the whole vector.

The word that is $[1,0,0,0,0{]}^T$ in a local representation might look like $[-0.150,-0.024,-0.233,-0.253,-0.183{]}^T$ in a distributed representation.