Can AI Learn Faster from Children?
Modeling human behavior remains an immensely challenging task. However, such models are necessary. For example, to build completely driverless cars, the autonomous "AI driver" must be able to safely engage the human driver when necessary. The necessary AI-human engagement requires the AI to have a model of the human driver. Although a driving behavior is actually rather limited in comparison to the full scope of human behavior, it is still challenging to capture in a model, and is the subject of active research.
Our projects and research indicate that we are developing a Human Digital Twin (HDT), which, of course, requires a model of the human it supports. However, we will begin with capturing simpler versions of human behavior: children's behavior! For example, in the case of the Patient-Human Digital Twin, the beginning is with Neonatology, where the measured parameters are limited in contrast to an adult patient, the medical history is considerably shorter, and the intervention possibilities are somewhat limited. As a result, we anticipate that the models will be simpler and more reliable. The same concept underpins the TwinUp app, which predicts child abuse risk. Because children's habits, routines, and movement profiles are limited, the model necessary for the app to predict abuse is also limited, and thus the predictability is more reliable.
With more data, use cases, and models, the goal is to gradually expand the Human Digital Twin initital models to capture more complex behaviors and eventually maturing them to capture more aspects of a person's life in the same way that children gradually develop their concepts and skills over many years.