Oftentimes we see “algorithms” referenced in various contexts, but the definition of an algorithm is often unclear. For MIT Technology Review, Kristian Lum describes what an “algorithm” means these days:
In statistics and machine learning, we usually think of the algorithm as the set of instructions a computer executes to learn from data. In these fields, the resulting structured information is typically called a model. The information the computer learns from the data via the algorithm may look like “weights” by which to multiply each input factor, or it may be much more complicated. The complexity of the algorithm itself may also vary. And the impacts of these algorithms ultimately depend on the data to which they are applied and the context in which the resulting model is deployed. The same algorithm could have a net positive impact when applied in one context and a very different effect when applied in another.
