Human in the Loop Machine Learning refers to the interaction between a human and machine learning model testing, helping humans and machines combine their intelligence effectively.

Human in the Loop Machine Learning refers to the interaction between a human and machine learning model testing, helping humans and machines combine their intelligence effectively. Unlike sci-fi movies, the vast majority of today’s AI cannot learn by itself. Where most of the AI applications relies on intensive human feedback. Humans are providing and/or verifying the initial labels for machine learning (ML) supervised models in 90% of machine learning use cases.

Supervised Machine Learning AI

AI can be reduced to – applying a set of rules or other mechanisms for the purpose of making decisions based on data. The most applied machine learning algorithms are called supervised machine learning algorithms, where the goal is to map some input A to an output B.

See Tesla’s Latest AI Driven Autopilot Driving

A relevant AI application that has generated a lot of public discussion is the autonomous vehicle that can drive humans safely down the street. This is possible due to AI scientists, sophisticated image recognition ML models, and thousands of human hours testing the Artificial Intelligence (AI) system’s sensors’ ability to see a pedestrian, lane markings, moving vehicle, or other relevant objects.

Humans in the loop machine learning will continue to be the norm for years to come.