The attributes of HITL (Human-in-the-loop)

Human-in-the-loop (HITL), basically you can say, is the process of leveraging the power of the machine and human intelligence to create machine learning-based AI models. HITL describes the process when the machine or computer system is unable to solve a problem and needs human intervention like being involved in both the training and testing stages of building an algorithm, for creating a continuous feedback loop allowing the algorithm to give better results every time.

If you have a sufficient number of datasets, an ML algorithm can easily make decisions with accuracy, just after learning from these datasets. But before that, the machine needs to learn from a certain amount and quality of data sets, how to properly identify the right criteria and thus come to the right results.

This is where human-in-the-loop machine learning is used with the combination of human and machine intelligence creating a continuous circle where ML algorithms are trained, tested, tuned, and validated. In this loop, with the help of humans, the machine becomes smarter as well as more trained and confident to take quick and accurate decisions when used in real-life and also helps to train the algorithms.

Applications

Human-in-the-loop has integrated two machine learning algorithm processes – supervised and unsupervised learning. In supervised machine learning, labeled or annotated data sets are used by ML experts to train the algorithms so that they can make the right predictions when used in real-life. While on the other hand, in unsupervised machine learning there are no labels given to the learning algorithm. It is left on its own to find structure in its input and memorize the data in its own way. In HITL, initially, humans label the training data for the algorithm which is later fed into the algorithms to make the various scenarios understandable to machines. Later on, humans also check and evaluate the results or predictions for ML model validation and if results are inaccurate humans tune the algorithms or the data is re-checked and fed again into the algorithm to make the right predictions.

Impact on Machine Learning

Doing a machine learning process without human inputs is not possible. Algorithms cannot learn everything unless provided as per their compatibility. For example, a machine learning model cannot understand raw data unless humans explain and make it understandable to machines. Here, the data labeling process is the first step in creating a reliable model trained through algorithms, especially when data is available in an unstructured format. An algorithm cannot understand the unstructured data like texts, audio, video, images, and other contents that are not properly labeled.

Human-in-the-loop is not a concept you can implement in every machine learning project. HITL approach is mainly used when there is not much data available. Human-in-the-loop is suitable because, at this stage, people can initially make much better judgments than machines are capable of. And using this, humans produce machine learning training data sets set to help the machine learn from such data.

As per the algorithms, different types of datasets in machine learning training are required. The human-in-the-loop approach is used for such different types of data labeling processes. If you want to train your model to identify or recognize the shape of objects like an animal on the road or other objects, then bounding box annotation is best suitable to make them recognizable to machines. While on the other hand, if you have to classify the objects in a single class, you have to use the semantic segmentation annotation suitable for computer vision to train the visual perception-based ML model. Similarly, to create facial recognition training data sets, landmark annotation is used. In language or voice-recognition machine learning training, text annotation, NLP annotation, audio annotation, and sentiment analysis is used to understand what humans are trying to say in different scenarios.