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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.

One of the risks a child might face is abuse, which can happen in many different ways and in all different kinds of social groups, cultures, and countries. Every parent wants their kids to be safe and accordingly there are many apps that provide parents with various possibilities to connect to their children, manage their digital interactions or monitor their locations, or get alerts if their kids are out of specific zones. On the other hand, risk management and mitigation are heavily reliant on assigning a level of probability—a prediction—to the incidents that an organization wants to limit or avoid, such as accidents, losses, or project failures. In comparison, to provide successful child protection, a parent must gain insights into the risks they believe are relevant to their child and an early assessment of the child's level of risk exposure to mitigate risks before they occur. This is the primary goal of our TwinUp app: to learn a child's social interactions and routines, apply a community-specific abuse risk profile to predict a risk score to various child activities. Based on the score, the TwinUp app then can inform the child, parents, or caregivers of potential high-risk circumstances to mitigate the situation. In addition, the TwinUp app provides a digital network that connects a child's device to what we call "persons of trust" at the many places a child frequents to speed up mitigation and assistance when needed. It should be said that the TwinUp app doesn't need any data that other child protection apps don't already use.

Business intelligence (BI) provides insights on four levels: descriptive (what happened), diagnostic (why did happen), predictive (what will happen) and prescriptive (how to make it happen). The most crucial point to remember is that each level builds on the one before it. For example, a prescriptive task to determine the pricing for a service or product which would improve sales by 15%, is contingent on accurate sales volume predictions, which are based on knowledge (diagnostic) of what factors influence sales and consumer purchasing decisions, which is contingent on access to previous sales numbers. There is a challenge, however, with many intelligent mobile apps that prescribe actions to their users to reach some goals (for example, fitness, health or financial goals): they struggle with the second level, the diagnostic, and accordingly, their recommendations are not as effective as might be expected by the users. To "prescribe" training or diet regimes, a fitness or health app needs reliable diagnostic about why did a user achieve or miss their target. One way of providing this diagnostic is to rely on assumptions learned across multiple users which are then refined using user input via questionnaires. The problem with that technique is that it is tedious, and the users may be unaware of factors influencing their behavior and choices. Another technique for providing a more reliable diagnostic of users is to collect a broader range of user interactions across different aspects of their lives and rely on these interactions to learn user-specific patterns and preferences. Such user-specific patterns would reveal insights into the user's lifestyle and commitments, which might then be used by an app to enhance Its diagnostic and provide more effective recommendations. This is what the Information Digital Twin (IDT) is attempting to accomplish: observe user digital interactions, learn their specific routines, infer and classify their interaction preferences, and provide the user—and other apps, if desired—with a better insights Into the user's lifestyle and thus enable the users to achieving their goals more effectively. Our assumption is that many aspects of our lives are revealed through our digital interactions. Which app (or application) categories we use, how often, for how long, when, where, and in what order we use them—regardless of content—can reveal insights about a person's mood, lifestyle, and preferences, and enable accurate and personalized assumptions about why-type questions.

Welcome to the Social AI Platform and the Information Digital Twin (IDT) Usually, you also need new tools when you start making something new. The Social AI Platform and the Information Digital Twin (IDT) are new ideas that have never been used before. Our main challenge has been - and still is - to figure out and build the new tools we need to build and spread these ideas. We don't just mean new algorithms or technologies when talking about tools, but mainly new terminology to show what is new about the two concepts, how they are different, and what they can do. For this reason, our blog is called the Social AI Platform Blog. For example, when we talk about an "Information" Digital Twin, what exactly do we mean by "information"? Actually, the term "information" still means different things in different contexts, and there is an ongoing debate over developing a unified and formal definition that can be applied uniformly across all fields and sciences. The same is true for "intelligence"; there is currently no clear, consistent, and unified definition of what intelligence entails. As an analogy, it was impossible to build and mature all sorts of heat engines without consistent formalization of notions such as energy, heat, and entropy. The same is true for aviation; only with the advancement of aerodynamics was it possible to design planes as we know them today. As a result, we think that to get a grip on our new ideas, and to be able to differentiate and communicate them, we also need to make sure that our terminology is precise and formal. We will try to do this through a series of posts on this blog. To start, we will talk about "what is information?", and what it means In the context of the Information Digital Twin.