What is Human-in-the-Loop Machine Learning: Why & How HITL Used in AI?
In today’s era, mechanization is taking place everywhere with a new age of development in more automated systems, applications, robots, etc. Machine learning and AI are the leading cutting-edge technologies giving automation a new dimension with more tasks to be performed by machines themselves.
Though, nowadays many tasks can be independently performed by AI-enabled devices, systems, or machines without the help of humans. But developing such machines is not possible without the help of humans. So, Human-in-the-Loop or HITL is a model or concept that requires human interaction.
What is 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.
Humans annotate or label data and then give it to the machine learning algorithm to learn and take decisions from such predictions. Humans are also involved in tuning the model to improve its accuracy. Finally, these people test and validate the model by scoring its outputs, when machine learning algorithms are not able to make the right decisions.
Also Read: How To Select Suitable Machine Learning Algorithm For A Problem Statement
Why Human-in-the-Loop Machine Learning is used?
If you have a sufficient amount 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.
Also Read: How to Improve Accuracy Of Machine Learning Model
How Human-in-the-Loop Machine Learning is Used Today?
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.
Also Read: How Model Predictions are used to Increase Data Labeling Speed and Improve Accuracy
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.
Also Read: How to Validate Machine Learning Models:ML Model Validation Methods
Why Human-in-the-Loop is Important for 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.
Also Read: How To Improve Machine Learning Model Performance: Five Ways
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.
Hence, the human-in-the-loop approach is required to make such data comprehensible to machines. These data are labeled as per the desired instructions like what is seen in the images, what is spoken in the audio or video using the data labeling or image annotation techniques to label such data.
Also Read: Why Data Annotation is Important for Machine Learning and AI
When Human-in-the-loop Machine Learning is used?
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.
Also Read: How Much Training Data is Required for Machine Learning Algorithms
And using this, humans produce machine learning training data sets set to help the machine to learn from such data. Human-in-the-loop deep learning is also used when humans and machine learning processes interact to solve one or more of the following scenarios:
- Algorithms are not understanding the input.
- When data input is interpreted incorrectly.
- Algorithms don’t know how to perform the task.
- To make humans more efficient and accurate.
- To make the machine learning model more accurate.
- When the cost of errors is too high in ML development.
- When the data you’re looking for is rare or not available.
Human-in-the-Loop for Different Types of Data Labeling
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.
Also Read: How to Measure Quality While Training the Machine Learning Models
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.
And when such data is labeled, annotated or make usable to machines, chatbot or virtual assistant like AI devices are developed to communicate with humans. Humans-in-the-loop can create different types of training data sets for different types of machine learning models built for different fields.
Why Human-in-the-Loop Services by Cogito?
AI is getting integrated into almost every field around the world, but we still require Human-in-the-Loop services, especially to produce and feed the training data into the algorithms at the initial stage of model development. Here, Cogito provides wide-ranging services for human-in-the-loop machine learning and human-in-the-loop AI comprising of text, videos, data, and image annotation services for AI development.
Cogito can produce a high-volume of training datasets with a fast turnaround capability and scalable solutions with best-in-class accuracy. Cogito follows the most feasible data labeling process while following all the international data security standards to ensure the quality and privacy of data at various stages of processing. Here all the outputs are carefully reviewed by our experts before they are sent to our clients.