Over time we expect the adoption of machine learning to become normalised. Machine learning will become a part of a developer’s standard toolkit, initially improving existing processes and then reinventing them.
…o make a useful prediction (the probability of the person enjoying a different film in the future). By giving ‘computers the ability to learn’, we mean passing the task of optimisation — of weighing the variables in the available data to make accurate predictions about the future — to the algorithm . Sometimes we can go further, offloading to the program the task of specifying the features to consider in the first place.
Complicated activities including making medical diagnoses, predicting when machines will fail or gauging the market value of certain assets, involve thousands of data sets and non-linear relationships between variables. In these cases, it’s difficult to use the data we have to best effect — to ‘optimise’ our predictions. In other cases, including recognising objects in images and translating languages, we can’t even develop rules to describe the features we’re looking for. How can we write a set of rules, to work in all situations, that describe the appearance of a dog?