In machine learning, we provide data to with features and labels to the algorithms. For example, a bunch of data about individuals containing features such as age, gender, marital status, height, weight, nationality mapped alongside a label of income. An algorithm loops through the data and learns the combinations of factors that influences a person’s income. The machine is capable of factoring numerous features and processing data at massive scales. The objective here being that the model can predict incomes of people based on a set of features.
This underlying principle of features, labels applies to all supervised machine learning algorithms. This enables us to build systems such as autonomous cars, medical diagnosis, facial recognition, chat bots etc., Because these systems are built on data that we already have, the machines essentially are mimicking human behaviour to begin with.
As a result, the existing systems are very efficient at doing a narrow task well. Machine can beat the world’s top chess player, same goes with the Go game. A machine can detect faces in a crowd in an instant. The machine can answer a specific query satisfactorily.
The rate at which progress is being made, its a matter of time wherein the machines are going to surpass their ability in terms of narrow tasks. True AI is when the machines can start doing broader tasks through decision making. For example, a camera detects a face and then checks against a data base for criminal records, and then sends a message to the nearest police etc., The possibilities for what the machine can do becomes impossible to predict. This is the stage of Technology Singularity, a term used to describe the point wherein AI becomes smarter than human intelligence.
If machines get smarter than humans, a lot of our world