What is Human-in-the-Loop?
A human-in-the-loop is a human who trains, tests and optimises an AI system to achieve more reliable results. The AI, like a normal student, makes mistakes or gets certain details wrong when starting a new activity. For example, a system can be taught to detect animals in the sea and it can distinguish an octopus with its particular shape from other animals.
However, difficulties may arise if other fish have a similar shape and colour. In such cases, there is the possibility to intervene with a human-in-the-loop and enter different characteristics in the system to search for. In such a way, the system can arrive at more accurate answers. A major advantage of human-instruction input is that two different types of intelligence can be used virtually simultaneously. This way, data can be provided and the AI system can check and evaluate its progress.
Humans can contribute their own knowledge, with which they have learned themselves, and can combine this with the speed of the computer. Thus, there is a fantastically large potential with this artificial intelligence and through dynamic cooperation, disadvantages of humans and machines can easily be compensated and thus more accurate results can be comfortably achieved.
What human-in-the-loop simulators are there?
There are quite a few different simulators for this concept:
- Flight simulators
- Vehicle simulators
- Marine simulators
- Most diverse video games
- Supply Chain Management Simulators
- Digital puppetry
When is a human involved in the calculation process?
Human-in-the-loop systems work together with a human supervisor. This supervisor can help in critical situations. The system usually implements what it has learned on its own. In this way, many processes are automated. However, in difficult and unknown situations, a human can be asked to make difficult decisions or to explain a new situation to the machine.
How does Human-in-the-Loop work?
Human-in-the-loop combined Supervised Learning with Active Learning and is an essential component of AI applications. Predictions should be made as accurately as possible. The Human in the Loop system provides efficient and fast procedures for model training and prediction. It is a useful method for identifying informative examples. There are ergonomic tools for labelling appropriate training data.
