The term “human-in-the-loop” (HITL) is used to describe a system where humans are involved in the loop of operations for data processing.

In text analytics, human involvement may be needed for tasks such as validating results, providing feedback to the system, or labeling data. For example, a system that uses natural language processing (NLP) to extract entities from unstructured text may require a human to review and validate the entities that were extracted.

Outside of the text analytics industry, the term human-in-the-loop may have a different meaning. For example, in some cases it may refer to systems where humans are required to provide input in order for the system to operate.

Other terms that are similar to human-in-the-loop include “human-centered design” and “crowdsourcing”. However, these terms have different meanings and should not be used interchangeably with human-in-the-loop.

Human-centered design is a process where the needs of the user are placed at the center of the design process. Crowdsourcing is a technique for sourcing tasks or data from a large group of people, often via the Internet.

HITL as Artificial Intelligence

In artificial intelligence, human-in-the-loop (HITL) is a method of including humans in the training process of machine learning algorithms. This is done by incorporating feedback from humans into the algorithms to further train and improve the accuracy of the algorithm.

One example of how this can be done is through active learning, where a human is only required to label a small amount of data and the rest is automatically labeled by the machine learning algorithm. This technique can be used when there is a large amount of data that needs to be labeled, and it would not be efficient for a human to label all of it.

Another example is reinforcement learning, where a human provides feedback to the algorithm after each iteration so that the algorithm can learn from its mistakes and improve.

HITL can be used in various other ways to help train machine learning algorithms. However, it is important to note that HITL is different from human-based learning, where humans are solely responsible for training the algorithm.

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