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Patient-Centric Predictive Solutions Start with Establishing a Common Vocabulary for Medical AI! Patient-Specific Predictive Diagnostic Even tiny changes in patient vital signs and measured values can signal a developing problem. Time is of the essence when it comes to predicting these deviations , placing them in context, understanding them, and reacting to them with a therapeutic change. These tasks are especially demanding for physicians who have little experience with the patient group. Challenges with ICU Care No reliable technology for predicting ICU patients' critical events No patient-specific predictions or risk alarms Critical ICU events may have long-term effects on patients Patient risk assessment is still dependent on the attending physician's competence and knowledge, which may vary between shifts and facilities Many critical events may be predicted if there is real-time, comprehensive patient monitoring Longer Mean Time to Respond (MTTR) after identifying a critical event (assessment, data analysis, treatment option assessments) raises patient risk High treatment cost and limited ICU capacities The Patient-Human Digital Twin (Patient-HDT) Provides automated and patient-specific predictive risk notifications Provides treatment recommendations and simulations Provides consistent and expandable knowledge base to close the knowledge gap across medical teams Compensates for the increasing lack of specialized medical personnel Expandable to cover various types of patient metrics and treatments Open architecture that can integrate with, and learn from any patient-related data source How is the Patient-HDT App Different? Patient-specific, c an be provided as a service to patients to improve risk prediction after they return to their everyday life Continuous learning from patients’ digital interactions to enrich risk assessment and recommendations Continuous and automatic updating with new medical information to improve risk predictions and mitigation recommendations Insights from diverse Patient-HDT can be incorporated into a single dynamic medical knowledge base to use anywhere to enhance treatment quality Start with the Patient-HDT Feasibility Assessment Building data-driven healthcare solutions is a challenge. Not only because of patient data privacy, but also because of the extensive specialized medical expertise required to engineer this data and provides insights from it. Our method uses raw data to define initial data-related insights to determine the feasibility and value of the Patient-HDT. ​ The first step is identifying a group of critical conditions or risks that significantly impact treatment. Next, a set of parameters and metrics associated with the conditions or risk is identified. The data associated with the parameters and metrics in scope is then obtained from historical health records. The Patient-HDT uses the obtained data to learn the information model particular to the conditions or risks under consideration (Information Heat Map, IHM ). The IHM provides initial insights into the correlations of the factors impacting a condition. Further, the IHM allows the medical team to gain insights into how controlling specific parameters would impact the condition or risk of a patient. ​ Validating such insights is the first step towards validating the value and feasibility of the Patient-HDT! Events Risk Profile Example Modeling Patients, A Key Challenge for AI Solutions in Healthcare Digital twins are expected to offer unprecedented insights into a variety of fields of medicine and healthcare. At some point, digital twins will fundamentally alter how physicians engage with their patients. Doctors would employ digital twins to diagnose patients' conditions more accurately, simulate treatment possibilities, and provide assistance during life-saving surgery. ​ On the other hand, the human body is an extraordinarily complex and dynamic system. Constructing a model of an organ or a patient remains a complex undertaking, made even more challenging due to the sensitivity around patients' health data. ​ However, utilizing the Patient-HDT Platform enables a relatively simple and staged introduction of digital twins into the medical sector. For example, the radar chart depicted here, which models a hypothetical medical risk event, can provide preliminary insights into the significance of several medical and lifestyle factors associated with that risk event and their likelihood of contributing to that event, all without application of elaborate machine learning techniques. ​ Once the model have been confirmed for its consistency, accuracy, and value in terms of risk reduction, the more elaborate implementation of the Patient-HDTs for the individual patients can commence. Patient-HDT Data Structure The patient Information Heat Map (IHM) , which captures all pertinent patient and treatment data, is the central component of the Patient-HDT. The Patient IHM is initiated using historical patient data. A cell in the heat map depicts, for instance, the degree or association between a particular dosage of a particular medicine and a particular heart rate range. Using Bayesian inference and the same set of historical data, the Patient IHM determines, for a given risk event (any combination of output values), the correlation of each input on that risk event. These values are then used by the Patient-HDT to build the Event risk profile, to determine the onset of critical events and recommend courses of actions to mitigate the risk. PATIENT HDT WHITE PAPER

Enabling Human-Machine Teams Starts with Modelling their Interactions using a Standard Vocabulary-A Set of Information Tokens! Digital Twins for Human-Machine Teaming (HMT) Human-Machine Teaming (HMT) refers to situations in which people and intelligent agents, or robots, work together to achieve a common objective. It is projected that managing the increasing complexity of many domains such as aviation and smart cities can be only achieved through human and intelligent agent teams. Such teams would consist of-for example-pilots, air traffic controllers, and ground staff working together with aircraft agents, air traffic control agents, weather agents, maintenance agents, smart drones, and self-driving ground vehicles. In an effective HMT, and given a shared objective and task environment, each team member must be able to identify the actions it can take which will enable the entire team to accomplish the shared objective. HMT Challenge-Defining a Shared Task Model Each human team member, such as a sports team, has a mental model of the game's rules, the playing field, and their colleagues' roles (the task environment). Then, each player uses their own model to "simulate" numerous interaction scenarios and identify the ideal course of action for a specific circumstance. Even though each player follows the same game plan and objectives, if there mental models are not consistent (i.e., they have different interpretations of the game rules or the roles of other players) then it is highly unlikely that their actions are consistent with those of their teammates. Accordingly, the team will be ineffective in achieving their objectives. ​ Thus, the greatest obstacle to attaining effective HMT is providing each team member with a consistent model of the Task. In addition, the task model must be updated in real-time to reflect the task progress, thus allowing each team member to base their actions on the same task status, which increases the team's efficiency significantly. 00:00 / 00:55 00:00 / 01:25 The HMT Platform-Providing a Virtual Task Environment The primary objective of the HMT Platform is to provide each team member with an up-to-date model of the task they support. Each team member can then use the model to choose which actions to take based on their role. Each team member's actions, in turn, update the task reference model and accordingly influence the following decisions of all other team members. The task reference model is the Information Heat Map (IHM) . The IHM is a data model representing a task (or scenario) in terms of the many input and output parameters defining it, their correlations, and conditional dependencies. Using the IHM as the environment, each team member can then use reinforcement learning to determine the optimal course of action for attaining the team's objectives. The IHM thus provides a virtual task environment, the task reference IHM, in which all team members communicate and are connected in real-time, thus enabling an effective HMT. IHM_HMT How is the HMT Platform Different? The HMT Platform is initiated by defining the scenarios capturing the various tasks as information models, the Information Heat Maps, IHMs . A member's unique IHM combines one or more Reference IHMs to cover the asks the member needs to support. ​ The IHM's most significant innovation-and difference-is capturing scenario parameters as a range of discrete values. For example, temperature is recorded in discrete value ranges: 35-37, 38-40, or > 40. A measured value is then represented as a "1" for the matching range and a "0" for other values. ​ The discrete representation of data enables the HMT Platform to capture, describe, and correlate any metric type in a single, consistent structure: the IHM. In other words, an IHM can model and represent a multitude of complex tasks or scenarios as a single-task representation . Team members can then decide what to do based on the same, up-to-date representation, which they can access through their own apps (HDT/IDT).. 00:00 / 00:58 00:00 / 01:33 Why is the HMT Platform a Better Solution? The HMT Platform and the connected HDT/IDT Apps create a virtual task environment similar to that of multiplayer online games. According to this analogy, a team member's HDT/IDT stands for the avatar with which they interact with the environment . In addition, and as in online games, only that part of the environment is rendered which is relevant to a player, i.e., each team member's IHM represents only that part of the environment that is relevant to their role or task. . ​ On the other hand, scenario and task owners can manage a complete team's tasks by modifying the parameters of the relevant IHMs, which then propagates through all relevant HDTs/IDTs and accordingly updates their subsequent decisions. ​ Finally, mature, role-specific HDTs/IDTs can be assigned to new team members to speed their learning and adaptation to their roles, ensuring task performance consistency. Building Human-Machine Teams-How to Start? As with other HDT/IDT use cases, building digital twins-enabled HMT environment starts by identifying a set of tasks and scenarios to be carried by the HMT team and then build a prototype of the various involved roles to ensure the feasibility of the HMT Platform. Please refer to our services page for more details! TECHNOLOGY

Protecting Vulnerable People "Child maltreatment is a global problem with serious life-long consequences," the World Health Organization states. Almost every second child endures some form of abuse during childhood or adolescence in some parts of the world. The difficulty in identifying child abuse is that children frequently are unable to identify an interaction as abusive; and if they do, they withhold information out of fear and shame; and that abuse occurs from both known and unknown individuals to the child. Providing Predictive and Proactive Digital Protection There are numerous mobile applications that inform parents of their children's whereabouts, receive notifications about their precise location, access their devices remotely, enable children to quickly contact parents, trusted individuals, or authorities if they feel threatened; however, our IDT-based app, the Child Digital Twin, the C-DT App (in development), aims to predict possible abuse events , thereby reducing the chances of children from getting into an abusive situation in the first place. Help Children Avoid Abusive Events Child abuse situations vary significantly by neighborhood, culture, or country. Thus, the TwinUp app is pre-loaded with a community-specific abuse risk profile. The abuse risk profile is based on a Information Heat Map (IHM) that considers a variety of factors relating to abuse incidents and how children respond to abuse experiences in a specific environment. A parent or caregiver configures the TwinUp for their child by providing basic background information and designating individuals in the child's environment as persons of trust with whom the TwinUp can communicate when within range of the child. ​ The TwinUp learns the child's routines, provides estimates for deviations from that routine, and finally projects an abuse risk score . When the anticipated risk score exceeds a reference value (calculated by the TwinUp from the IHM), the TwinUp can initiate a series of steps based on the parents' initial configuration to minimize the possibility of an abuse events. ​ With time and the use of the TwinUp by many parents in a community, the app's prediction accuracy will improve accordingly. Help Women Avoid Abusive Events As with the TwinUp for children protection, the TwinUp for women's safety learns its user's behavior patterns and, based on a community risk profile established in an Information Heat MAP (IHM), assigns risk levels to various daily situations. ​ On the one hand, an adult's life is more complex and diversified than that of a child; on the other, this complexity provides the TwinUp with additional data and digital interactions to learn the user patterns and sentiments. ​ Similar to the TwinUp for children, a critical component of maintaining safety for adults is maintaining a digital network of trusted individuals and friends. Another consideration is determining a person's overall sentiment based on their digital interactions. This enables the TwinUp to assess its user's stress levels and rely on it to predict risk, as stressed individuals may be less aware of the ramifications of particular choices. ​ Based on the projected risk score, the TwinUp can notify their user to raise their awareness or recommend that they vary their routines and become unexpected, thus minimizing the likelihood of critical encounters. How is the C-DT App Different? There are many apps for protecting children and keeping women safe that give detailed, real-time information about where a child is and what is going on around them. ​ However, as with any risk, the key is to find ways to lessen it. We thus think a safety solution should focus on: predicting risky situations in advance by predicting the information value of possible risk situations and recommend actions to avoid them so that the child and the parents have time to act BEFORE something bad happens. ​ Long-Term Vision Protecting children or adults from abuse is a necessary first step toward supporting them in other aspects of their lives, such as predicting and lowering stress, predicting and managing addictions, or improving care for children with disabilities. CHILDREN WELLBEING WHITE PAPER

The Information Digital Twin (IDT) The role of the IDT is to learn the behavior and preferences of its user, enabling them to predict their surroundings and identify the ideal course of action to achieve their objectives. Digital twins (DT) have enormous promise for automating and optimizing assets, processes, and complex operations. However, developing DTs remains a challenging and costly task in many cases. ​The difficulty can be summed up in defining and updating the model used by a DT to capture the relationships between the thousands of parameters required to model a supply chain network, a production line, or a human patient. IDT Information Digital Twin (IDT) Architecture To model and automate agent-environment interactions the Information Digital Twin relies on three major components. A representation component contains the model of the agent's interactions it supports. A learning component to learn and update the model according to the specifics and behavior of the agent. A monitoring and control component to evaluate the agent's behavior and interact with it either in the form of sending recommendations or taking over some of the actions. The Information Digital Twin Representation Component-The Information Heat Map (IHM) The Information Heat MAP (IHM) is the representation component of the IDT and it serves a single purpose: it captures the level of dependency - in bits - between the various scenario parameters and how each parameter impacts a specific scenario objective. That is, the IHM enables the IDT to quantify the degree of dependency between all scenario parameters and to calculate how changes in one or more parameters spread across the entire scenario and affect selected scenario objectives. The IHM parametrizes a process or scenario. The parameters are classified as input, context, actions and output. In the above example, an IHM is defined to manage risks associated with an ICU patient. ​ For an IHM representing of a patient, the input, context and action parameters provide information about the medications and patient-specific conditions and history. In contrast, the output parameters provide information about the various vitals and conditions observed for the patient. ​ Using inference from historical data, the IHM then calculates how well a parameter predicts specific risks. Each patient is provided with their own Patient-Human Digital Twin, or Patient-HDT, which relies on the various information values from the IHM to predict the onset of any of the risks in the scope of the IHM. ​ In a broad sense, the IHM is an information model of a scenario that depicts-in bits-how much a parameter impacts the overall scenario objective, or risk in the case of the Patient-HDT. IHM Providing Recommendations for Actions-The IDT Control (Decision) Component The Information Digital Twin (IDT) decision (or recommendation) component uses Reinforcement Learning (RL) to obtain an information value for possible user actions and, consequently, provides action recommendations. The RL algorithm relies on the IHM to calculate updated state, action, and reward values to determine the optimal course of action toward the objective of a particular scenario. ​ Based on the Information Heat Map, which captures the user interactions with their environment, the IDT "populates" the Markov Decision Process (MDP) of its user with the necessary information values. The various MDP values are updated dynamically along the user interactions with their environment. A set of RL agents, each with a specific objective, then simulate possible courses of action and provide user recommendations accordingly. ​ The IDT's learning component performs the final step towards automating user decisions. The learning component learns user-specific action-preferences based on the user interaction patterns from the Information Heat Map (IHM) and the frequency with which the user selects specific recommendations. When a user faces a familiar choice, the IDT can make that choice for them based on their previously learned preferences. Please contact us for more details, research papers, or trial algorithms RL Decisions Learning The IDT-Social AI Platform Dependency To improve the speed and effectiveness of the IDT learning, we believe it is essential for the IDT to be part of a larger environment and to learn form other IDTs as well, which is achieved by being part of a Social AI Platform. ​ Furthermore, the IDT (or the human-specific variant, the HDT) can be part of a social scenario controlled on the Social AI Platform, such as monitoring health risks or optimizing social resources. In this situation, the IDT's objectives are generated from the Social AI Platform. The IDT's task is to adjust and fine-tune user habits and activities to align them with social objectives, but based on user preferences and interests. Defining the necessary algorithms to achieve this alignment is part of our ongoing research . SERVICES

Digital Transformation with Information Digital Twins We are information architects. We provide Information and Human Digital Twins to automate and optimize complex decisions in a variety of domains such as healthcare , logistics , human wellbeing or human-machine teaming . We Offer our Services from USA | UAE | SINGAPORE How to Start-The Information Digital Twin Prototype A prototype is the most straightforward approach to assess the usefulness of a new technology. The prototype in the case of an Information Digital Twin, IDT/Human Digital Twin, HDT is an information model (the Information Heat Map, IHM) that enables the basic functions of a digital twin: anticipate performance deviation and suggest potential corrective actions. ​ A prototype of Patient-HDT , for example, would provide an early assessment of a patient's health condition, patient-specific health risks and potential treatment options, which could then be forwarded to the patient's physician to shorten the anamnesis time and improve diagnostic and treatment quality. ​ A digital twins-enabled Human-Machine Team (HMT) prototype would create the elements of an HMT Platform that mimics an online game environment in which team members communicate with one another and complete tasks using their HDT/IDT as their avatars. ​ A Shipment-DT prototype, for example, would forecast shipment progress, potential delays, or cost variances and recommend remedial actions to maintain delivery windows. ​ As shown in the preceding example, an IDT/HDT prototype illustrates the practicality of the IDT/HDT ideas in specific instances. The true synergy, however, is obtained by using IDTs/HDTs across an entire population (patients with various conditions and needs, or all shipments across a supply chain.) which necessitates the introduction of Digital Twin Platforms that enables multiple, dependent smart scenarios . From Strategy to Digital Twin Platforms Services Overview 1. Feasibility studies and Transformation Strategy to evaluate the value and impact of deploying Information/Human Digital Twins (IDT/HDT) to support specific procedures or operations. 2. An i nformation architecture that depicts how data can be delivered, managed, and analyzed to enable and integrate IDTs/HDTs in complex environments. 3. The core applications, algorithms, and methodologies necessary to enable IDTs/HDTs, their incorporation into Social AI Platforms and their continuous adaptation and learning. Digital Twins Feasibility | DT-Transformation Strategy | Scenario & Information Map Design | Scenario Optimization | Scenario Automation Fesability Information Digital Twins Feasibility Because digital twins (DT) are still a relatively new technology, we start by assisting our customers in answering a few basic questions to enable informed transformation strategy definition. For instance: ​ - Are DT the best technology for our transformation goals? - What value can we expect from DT? - Do we have the right data to support DT? - Do we have the infrastructure to deploy and sustain DT? - What are the change management implications? ​ This evaluation is provided as a short report, without obligation as it also helps us better understand our customers and their domains. AI Scenario Design Digital Twins-Based Transformation Strategy Deliverables , DT-Transformation Strategy, Scenario architecture, Scenario events and events ' supporting parameters Digital transformation can be done in many ways, but using digital twins (DT) to digitize operations need to be based on scenarios . That is, the area that needs to be digitalized must first be described in terms of different specific scenarios. Then, one or more digital twins are used to support the various scenarios. This is necessary to ensure effectiveness and scalability of the approach. ​ From the point of view of AI, identifying scenarios makes it much easier to learn the patterns and regularities that any AI solution needs to work effectively. Accordingly, our first step is to define an initial DT-Transformation strategy scope, expected value and required governance. ​ Define the scenarios within the scope. For each scenario in scope, identify the events to be managed by the digital twins. (See the Shipment Digital Twin or the Human-Machine Teaming use cases as examples for scenario designs) Define the parameters/data relevant to each event, their sources and access/transformation requirements. ​ The DT-Transformation strategy provides structured insights necessary for effective decisions-making regarding transformation feasibility, expected value, involved effort and project structure. ​ Defining and scoping the "right" scenarios and events to digitize and automate remains an art we have mastered over many years of expertise in architecting and integrating complex enterprise and business intelligence solutions Scenario Information Model Deliverables , Scenario information architecture, Information Heat Map (IHM) After defining a scenario or a use case to be enabled and automated by digital twins, and once the events in scope have been identified, the next step is to develop a Information Heat Map (IHM) , which captures and represents the scenario parameters (Supply Chain Example ). Depending on the number of events in a scenario and the related parameters, the IHM can be defined in a table-based environment (Excel, Sheets, or a small database). However, IHMs representing multiple scenarios are defined in a business intelligence environment (BI) as multidimensional information cubes. Once the parameters' historical data is uploaded according to the IHM data structure, the significance of each parameter in predicting the scenario desired outcomes are then provided and used to calculate the best course of action to achieve the scenario objective. Information Design Learning & Optimization Information Digital Twins-Based Sceanrio Optimization Deliverables , Deployed scenarios in the Social AI Platform with necessary Information Digital Twins As illustrated in the detailed use cases, scenario events (e.g., specific illness risk, child abuse, shipment delay, or human casualty events) result from the interactions of hundreds of parameters over an extended time period. The learning and optimization phase is thus focused on learning the patterns of interaction between the numerous parameters that affect an event and rely on them to enhance the event prediction and optimize the scenario performance by manipulating the identified patterns. This step relies heavily on machine learning to derive knowledge from the IHM, the numerous IDTs and their overall interactions with the platform. Information Digital Twins-Based Automation Deliverables , Automated social AI scenarios After identifying and optimizing the many interaction patterns underlying the various events, the final step in maturing the Social AI Platform is to automate some of the decisions required to improve a scenario's outcome, or minimize risk events occurrences. The logic for decision automation is implemented across the various IDTs. Once an IDT identifies an event of interest and based on the IDT's user-specific interaction patterns, the IDT provides a recommendation or initiates an action to raise or decrease the likelihood of that event according to the user's preferences and the scenario's objective. USE CASES Decision Automation

The Social AI Platform Enabling Individuals-Centric Social Resilience How to get there? ... use Information Digital Twins! The Social AI Platform uses a new type of digital twins - the Information Digital Twin (IDT) - to enable people and organizations to predict and automate their choices along complex interactions or scenarios, The outcome is thus a highly coordinated, automated and user-centric complex and resilient society. Each Information Digital Twin (IDT) in the environment learns its user's-either a human or a machine-complex interaction patterns and preferences. Given an objective, each IDT recommends to its user the best actions to achieve the objective, but according to the user's unique preferences. ​ In a mature environment with digital twins, the IDTs become the "avatars" that people and machines use to join the digital world to manage their tasks and reach their goals. ​ The Social AI Platform is not just a solution, rather, It's a technology framework to manage and automate many types of processes involving people, machines, and organizations. ​ The Social AI Platform places the active agent of a scenario - whether human or machine - at the center of all decisions. As a result, process coordination and optimization reflect each agent's unique choices and preferences, which is critical for achieving adaptation and resilience. Healthcare Digital Twins How patient-specific digital twins, the Patient Human Digital Twin (Patient-HDT), can reduce the risks of ICU patients by continuously monitoring them predicting potential critical events , and alerting medical personnel. The critical events predictions provide the medical staff with sufficient time to further examine the unfolding situation and mitigate potential risks, thus improving treatment effectiveness and reducing patients' risks. ​ The Patient HDT can be provided as a service to patients to keep monitoring their risks even after they leave the hospital. Healthcare Use Case Wellbeing Digital Twins How a personal digital twins (the TwinUp app) can reduce children's risk of abuse by monitoring their social interactions, predicting potential abuse events, alerting them to change their behavior, or involving their caregivers to intervene and mitigate an abuse event before it happens, thereby reducing abuse in a community and ensuring children's safety. ​ The TwinUp creates a digital safety network for its users that links them with people they trust so they may get support whenever needed. ​ Because the TwinUp can predict a wide range of events, it can assist individuals in avoiding undesired encounters and ensure their safety. Human Wellbeing Use Case SC Digital Twins How a Shipment Digital Twin (Shipment-DT) monitors a shipment's progress through the supply chain, predicting shipment-specific delays , and alerting operators with mitigation options to minimize delays and ensuring delivery timeframes and costs. ​ The Shipment DT is a virtual instance that is terminated after the shipment has been completed. However, the predictions and risk assessments from several Shipment DTs are aggregated to improve SC-level predictions for events like delays or costs ​ Shipment DTs may be selectively aggregated to create Container DTs, Vessel DTs, or complete SC DTs. Shipment Digital Twin Use Case Use Cases Human-Machine Teaming with Digital Twins How to use a combination of Human and Information Digital Twins to coordinate complex human-machine interactions toward a common goal. Each digital twin keeps is the user's avatar in the shared digital environment and it track of its user interactions, whether human or machine, predicts deviations from task objectives, and alerts their user or other team members so they can alter their actions accordingly, resulting in better overall team collaboration and goal achievement. ​ Over time, the models of the various digital twins (e.g., avatars) may be combined seamlessly to define more general, role-specific models (e.g., driver, pilot, or operator.) Such models are then used to establish trust between humans and machines and automate their complex interactions. Human-Machine Teaming Use Case The Social AI Platform Overview The Social AI Platform is the realization of an information architecture framework for modeling, managing, and automating hundreds of highly complex scenarios and procedures. The key to achieving this level of coordination is to represent all involved data according to a consistent data structure, which is defined by the platform information architecture. ​ In other words, the information architecture captures the communication - exchange of information - between the various scenarios and the users in a consistent manner thus-ultimately-enable quantifying user actions according to their information value. This quantification-in return-enable the effective use of algorithms to predict user interactions and provide recommendations for best actions . The Information Digital Twin (IDT) The Social AI Platform's central component is a universal type of digital twins, the Information Digital Twin (IDT). ​ The IDT learns from its users' interactions with their environment, predicts possible outcomes, and guides its users to the optimal course of action to achieve their goals. ​ The IDT learns its user preferences and automates some of their digital interactions. Thus, the IDT significantly boosts individuals' capacity to connect with society while freeing more of their attention to focus on their personal wellbeing. ​ Depending on the domain or use case, the Information Digital Twin (IDT) can be deployed as a mobile or desktop application that connects people to the platform, in which case it is referred to as a Human Digital Twin (HDT) . The IDT can also be deployed as a standalone device equipped with sensors and actuators to integrate robots or other types of agents into the platform ( read more! ). IDT_Home AI SCENARIOS

Shipment Digital Twin As with healthcare, digital twins are expected to enable significant improvements in the performance and resilience of supply chains (SCs) while lowering operational costs. This expectation is backed up by the fact that SCs have amassed a plethora of data about every aspect of their operations. SCs, on the other hand, are open systems that span different operators and countries, making them susceptible to a variety of interruptions such as weather, labor shortages, or social issues such as pandemics or political instability. Despite the abundance of SC data, defining a SC model to build a reliable and manageable SC digital twins remains a challenge Start with a "Shipment" not a "Supply Chain" ! We advocate beginning with building Shipments digital twins (Shipm-DT) and then aggregating their insights at the supply chain level to manage various performance objectives ​ That is, a Shipm-DT is solely responsible for monitoring a particular shipment for certain events such as delay or cost. As a shipment passes through the SC, the Shipm-DT predicts the development of deviations from reference values for the various events of interest. When a deviation is identified, the Shipm-DT notifies an operator (or a planning system!) with the specific parameters out of range. Additionally, the Shipm-DT can be equipped with its own decision logic for initiating mitigation measures. ​ The prediction signals from the various Shipm-DTs can then be pooled on a SC node or across multiple SC levels to predict - or simulate - SC-level performance. Shipment Information Heat Map (IHM) An example of an Information Heat MAP (IHM) for hypothetical supply chain to manage shipment delays. The "Input" represents the shipment parameters, while the "Output" indicates parameters unique to each node of interest along the supply chain. An actual model can incorporate hundreds of parameters related to shipments, context, and nodes. The shipment information map is defined to manage a specific event, in this case, shipments delay. Our algorithm then relies on historical data to assign a probability to each parameter, which indicates how well that parameter predicts a shipment delay . ​ Many of these probabilities provide direct insights into the delay risk, for example: Line 17—A delivery worth more than $500 is projected to be delayed 35.3% of the time. Column I—When Hub A is working at more than 80% of its personnel capacity, a shipment going through that hub is expected to still have a 11.9% probability of missing the promised delivery date. Line 31—During severe weather, there is a 37.1% chance that a shipment will be delayed. However, a shipment delay is usually affected by hundreds of dependent parameters. Identifying how such parameters interact to predict the optimal shipment-route configuration and minimize delays is thus a challenging task, made all the more difficult when performed under constantly changing conditions, lack of information and with limited decision-making time. ​ This is where the Shipment Digital Twin (Shipm-DT) comes into play: it continuously calculates the complex probabilities associated with a specific shipment to simulate and predict its delay risk . The Shipm-DT also learns how the various parameters interact and influence one another and uses the learned dependencies to improve the delay predictions. Further, the Shipm-DT relies on decision logic and algorithms to provide recommendations for the optimal configuration of a shipment-route combination to minimize delays. ​ In comparison to the shipment IHM a SC Information Heat Map (SC IHM) would capture dozens of SC events, not just delays. In this situation, the SC IHM is a multi-dimensional information cube that provides the bearing of a parameter—for example, shipment weight—on predicting a variety of events (e.g., delays, cost factors, perfect order rates, etc.) In this scenario, a Supply Chain-DT would then rely on the SC IHM to monitor the various events, predict deviations from planned performance and provide insights into mitigation and optimization IHM Exampe SHIPMENT DIGITAL TWIN WHITE PAPER

Contact & Information Material We are situated in Virginia, near Washington, DC, but we also provide our services from Dubai, the United Arab Emirates, and Singapore. The Social AI Platform and the Information Digital Twin are unique concepts in technology and research. As a result, we are eager to hear your thoughts, impressions, and questions concerning our services and research. At this point of development, we are particularly interested in working with our customers and partners on brief feasibility studies to explore the potential of the Information Digital Twin concept. HDT@semarx.com Name Email Message Send Thanks for submitting! Contact White Papers & Videos Social AI Platform White Paper Healthcare ICU White Paper Child Wellbeing White Paper Shipment Digital Twin White Paper Human Digital Twin Video Information as Entanglement Video White Papers HOME

Human Digital Twin (HDT) The Human-Specific Variant of the Information Digital Twin A User's Avatar in the Digital World The Human Digital Twin (HDT) is an AI-based application that learns its user's behavior and preferences across multiple, complex interactions with their environment. The HDT thus enables users to optimize and automate their digital interactions based on their preferences and interests, allowing them to lead balanced and healthy social and professional lives. ​ The HDT is a kind of a "meta-app" which observes how its user interacts with digital applications and digital systems, the context of these interactions, and learns the user preferences and interaction habits. Given specific user interests and objectives, the HDT calculates and assigns an information value to various user choices. The HDT then relies on this information value to calculate how user actions might affect their objectives. It then recommends a course of action that maintains user interests while preserving their balance and well-being. The Opportunity In every part of their lives, modern people leave behind a vast amount of digital traces. Many businesses are already gathering and examining these digital footprints in an effort to modify user behavior to meet their goals. Most user-oriented apps are still extremely specialized (financial, health, fitness, etc.) and don't provide users with insights reflecting their overall preferences and objectives. To gain the holistic and individualized insights needed to balance and negotiate the complexity of modern life, individuals need access to all the digital data they generate . ​ The HDT offers users data-based insights that take into account a user's unique characteristics, interests, and goals. Users are then able to navigate complex social interactions with resilience. Thanks to such personalized insights, businesses can offer users a tailored user experience and efficient, optimized user-organization interactions. A Life-Long Companion The Human Digital Twin is intended to assist and learn from its user throughout most of their digital encounters or scenarios . The more scenarios the HDT manages, the more accurate are its recommendations and the more insights the user has about their own behaviors, choices, and the implications of those choices. ​ The HDT offers its user a personalized viewpoint on their digital interactions and activities. Therefore, a user can rely on their HDT to provide them with individualized recommendations which takes into account their preferences and long-term interests. ​ In addition, when connected to a Social AI Platform , the HDT can further align its user with social objectives and increase their social resilience. The HDT Lifecycle The HDT's core techniques are based on reinforcement learning and Bayesian inference (RL). In summary, the HDT employs Bayesian inference to user interaction data to define the parameters necessary for an RL agent to evaluate and recommend actions. These parameters include the states relevant to an objective, their degree of relevance to the objective, the relevant actions and their value, and the action rewards. However, the defining characteristic of the HDT is the dynamic nature of the RL environment, in which states, states' values, actions, and rewards are continuously updated to reflect changes in the user's environment, objectives and preferences. In addition, more advanced versions of the HDT rely on unsupervised learning to identify user preferences and decisions patterns, which improves the accuracy of the HDT recommendations. Please contact us for more details, research papers, or trial algorithms Automating Digital Choices Once users start to follow the HDT recommendations, the HDT can begin to handle many of the user's digital decisions, thus giving the users more time and attention to dedicate to creative and social activities. ​ The HDT has the same features as the more general agent, the Information Digital Twin (IDT) . As such, the HDT can also connect to centrally managed scenarios (e.g., health, traffic, pandemics management, or operational risk management). This connectivity enables the HDT to update its user " action information values " and thus improve the effectiveness of the user actions and increase their alignment with social objectives when necessary. How to Start?-Defining User Scenarios The HDT is implement according to user scenarios. Each user scenario is specific to the user and captures the interaction information relevant to the user. Each scenario and its features is structured in the form of an Information Heat Map (IHM) which indicates the information values-in bits-of the various interactions taking place in the scenario. The HDT can then learn the user preferences and use the IHM to determine best options for actions. The user can then keep joining/initiating other scenarios, which translates into expanding the IHM to include new inputs, actions and output features. The HDT's unique approach to data architecture enables the seamless, effective, and almost endless integration of any kind of data into the user information heat map. Current Human Digital Twin (HDT) Use Cases At this point of the HDT's development, we have defined the algorithms underlying scenario representations, Bayesian inference, and the RL agent environment definition, which enables managing selected user pattens/risks. Based on the developed algorithms, we are maturing the HDT for two use cases: ​ Patients-HDT , which assists neonatology ICU patients, Children-HDT which assures children's safety and minimizes their risk of abuse. ​ ​ Both use cases focus on children because we believe modeling and forecasting risks for children are more achievable due to children less complex nature and behavior. ​ Please contact us for more details, research papers, or trial algorithms INFORMATION DIGITAL TWIN

The Social AI Platform is Scenario-Based Imagine the Following Smart Scenarios AI Scenarios A Patient Human Digital Twin App predicts that a patient is approaching a critical condition and warns medical staff to assist at an early stage, lowering the patient's risk and limiting potential repercussions. Following the evaluation of the intervention, the Patient DT updates a shared risk profile for patients with comparable diseases, thereby improving diagnostic and medical knowledge across many hospitals. Read More A Maintenance Operator's Human Digital Twin App on an oil rig predicts an operational risk for the operator based on a procedure execution deviation in the operation of new equipment. The HDT notifies the operator of the deviation and risk and suggests potential risk-mitigation activities. If desired, the HDT shares the event with operational management to adjust processes to minimize risks for all operators. A Shipment Digital Twin App in Singapore anticipating weather-related delays to its shipment and updating a "weather delay profile" that triggers each shipment passing through that warehouse—worldwide—to revise its projected delivery time—on its own! —thus avoiding costly unplanned delays and ensuring supply chain resilience. Read More A Worker's Human Digital Twin App learns "task profiles" that indicate talents, locations, competencies, or rates, allowing the worker to best match their skills and abilities to the various tasks. The Human Digital Twin will then replan the workers' social activities to help them achieve their professional objectives while maintaining a balanced lifestyle. A CHild's Digital Twin App l earns the child daily activities and routines and relying on an abuse risk profile to nudge the child through a smart band to adjust their actions to reduce their risk of being mistreated while also connecting them to others they trust throughout their entire environment, therefore protecting them digitally and minimizing their risk of abuse. Read More A Driver Human Digital Twin App relies on a city-specific "accident risk profile," to monitor its driver risk exposure level based on the driver habits and condition, alerting them to change their driving patterns according to minimize their risk, but according to their own preferences, thereby minimizing their risk and reduce accidents in the city. A Human Digital Twin App of a person at risk of a specific disease relies on a "disease risk profile" from a trusted health organization to provide the user with real-time advice—on how to adjust their choices and behavior to reduce their risks for that disease, while considering their preferences, habits, and lifestyle, thereby lowering their own risk as well as the risk of the entire community Following a natural disaster; a Rescue Team Comprising Humans, Robots, and Drones is underway. The digital twin App of each member, whether human or machine, relies on the mission risk profile to predict the role-specific risk exposure based on their task and circumstances. Upon predicting a possible risk, the Digital Twin alerts the user of potential risks, provides action recommendations, and eventually updates the mission risk profile (and everyone else's) risk assumptions with real insights, reducing team risk and enhancing mission success. Read More A Citizen Human Digital Twins observe and learn about their users' social interactions, movement patterns, and preferences. Using a city-specific "pandemic risk profile," the Digital Twin warns their users about potential infection risks and suggests alternative social interaction patterns to reduce user exposure according to the user's individual needs. The individual changes add up at the city level to increase distancing and avoid pandemics, thus reducing the need for lockdowns. Now Imagine All the above scenarios occurring proactively, simultaneously , and with little to no human coordination We can support hundreds of such scenarios on a single platform with millions of users Where each user can choose which scenario to subscribe to using the same smart app , the Information Digital Twin (IDT) , or the human-specific version, the Human Digital Twin (HDT) , and where the IDT tailors each scenario to its user’s preferences and objectives If you can imagine all of the above, then now you know how the Social AI Platform could enable people-centric social resilience The Human Digital Twin (HDT) The HDT is the Information Digital Twin's human-specific counterpart. In any of the smart scenarios described above involving a human actor, the HDT would assist its user in traversing the various scenarios, automating some of their decisions, and providing recommendations for appropriate actions based on the user's preferences and objectives. Do you have additional smart scenario ideas but have difficulty describing or scoping them? We can assist with the design and structure of smart scenarios to enable their digitalization and automation using Information Digital Twins (IDT). INFORMATION DIGITAL TWIN

What is Information? What is Intelligence? Building Artificial General Intelligence (AGI) Agents We developed the Social AI Platform and the Information Digital Twin (IDT) to provide commercial AGI capabilities and solutions. As such, the Social AI Platform is intended to support a wide range of intelligent scenarios using the same agent: the Information Digital Twin (IDT). ​ The Social AI Platform technology is the realization of our novel concepts and approaches to information and intelligence. In its mature form, the IDT can be considered as a general reinforcement learning (RL) agent Information and Intelligence-Unified Definitions* We believe that the path towards building Artificial General Intelligence (AGI) agents is based on unified-and dependent-definitions of information and intelligence ​ Artificial approaches to intelligence depend on computational models to process information and provide intelligent capabilities. Due to the lack of uniform definitions of what constitutes intelligence and what is information, the capabilities such models provide differ according to their interpretations of intelligence and implicit assumptions about what is information. The variety of interpretations of intelligence and information also indicate that existing intelligence computational models provide specialized rather than general capabilities. The following are the unified definitions we have developed so far and use to mature the IDT and the Social AI Platform. Human-Machine Teaming (HMT) is the prominent study area for unified information and intelligence definitions, focusing on enabling intelligent collaboration across fundamentally distinct entities. Hafez, W., (2022) Information as Entanglement—A Framework for Artificial General Intelligence, In: Goertzel, B., et. al. (eds) Artificial General Intelligence. AGI 2022. Lecture Notes in Computer Science, vol 13539, pp 20-29, Springer. DOI: 10.1007/978-3-031-19907-3_3 What_is_info Information A system's degree of entanglement with its environment measured in bits. That is, its capacity to predict its environment and be predictable by it Information (and intelligence) are apparent through interactions. If an agent or a system does not interact with its environment, it is impossible to tell if it processes information or if it is intelligent. Interaction, or more precisely communication, is thus a prerequisite for understanding and defining concepts like information and intelligence. ​ To communicate, an agent relies on a set of symbols, alphabet, or code and some rules to construct messages and to send them to its environment, and to deconstruct the messages it receives from other agents. ​ If the various agents depend on one another in reaching their goals, then the set of symbols used by each agent to build its messages will start developing some degree of correlations. Although each agent might use a completely arbitrary set of symbols, they need to make sure to use these symbols consistently in order to convey their messages effectively. This consistency is what achieves the correlations among the various sets of symbols. ​ Once correlations are established, predictability follows. When an agent sends a specific message, it predicts receiving a particular return message. In the other direction, when an agent receives a message, the level of correspondence of its response message determines the environment's capacity to predict its behavior. Our research assumption is that the level of correlations and the resulting agent-environment predictability is a unique, general property of a system interacting with its environment, similar to properties such as energy or mass. Consequently, we attempt to demonstrate that information is the metric for measuring this level of correlations, or entanglement . To formalize the information metric, we then rely on concepts from communication theory. Intelligence The activities of a system to maximize its entanglement with its environment, That is, the system strive to maximize its information, and accordingly its capacity to predict and be predictable The relationship between intelligence and information, or level of predictability as we define it here, is rooted in the nature of communication. Communication theory (Shannon and Weaver, 1949) defines communication as "the procedures by which one mind may affect another." To include "non-mental" systems, communication is thus the procedure by which one entity influences the actions of another in a particular direction. An example is a thermostat sending a control signal to a heater to change its degree of transferring energy into heat. Developing an effective communication system is a highly complex task. An agent needs to define a limited set of unique symbols, define rules to structure them into signals and messages (syntax), and map them to the set of concepts necessary to achieve its objectives (semantics). However, the most challenging aspect of communication is the activity of maintaining, updating, and optimizing the symbols, syntax, and semantics rules to achieve effective communication. This aspect is where our research assumption about the nature of intelligence comes into play. Our research assumption is that intelligence is the continuous activity of an agent to evaluate its communication effectiveness and increase its capacity to influence its environment toward achieving its objectives. This translates into monitoring the usage of its communication symbols, updating the rules to build messages out of these symbols, updating the messages to reflect its objectives, and ultimately evaluating its overall success in influencing its environment or adapting to it. ​ The question here is, what is the control signal that helps the system to accomplish these activities? Our assumption is that one possible way for the system to measure its effectiveness in completing these tasks is its overall information: its ability to predict and be predictable. That is, the intelligence of an agent can be assessed by its predictability capacity and if it can maintain it or increase it against changes in its environment. Research Opportunities: Integrating & Managing Heterogeneous Systems One method to substantiate the definition of information as a measure of entanglement is to demonstrate how to use information as entanglement to i ntegrate, control, automate, and optimize heterogeneous processes that span systems of fundamentally different natures (humans, machines, smart agents as in the case of Human Machine Teaming ). The Information Digital Twin, IDT (and its human-specific counterpart, the Human Digital Twin, HDT ) enables there capabilities by relying on capturing their level of dependencies: i.e., information. We thus seek research and partnership opportunities to expand and mature the concept and application of the IDT/HDT. ​ A typical output of such research is an IDT/HDT prototype that automates and optimizes heterogeneous processes based on an information model (the Information Heat Map, IHM ) that captures the correlations between the parameters of the various systems, notwithstanding their nature. Please contact us for more details, research papers, or trial algorithms Realizing General Intelligent Solutions: The Information Digital Twin (IDT) & The Social AI Platform The Information or Human Digital Twins (IDT/HDT) are the technical implementation of the unified information and intelligence concepts. The IDT/HDT architecture and algorithms enable them to assist their users in measuring and assessing the level of information (entanglement) involved in their interactions with their environment and help them optimize their actions towards increasing their information (i.e., entanglement) over time. Entanglement, Predictability & Information Our assumptions concerning the unified definitions of information and intelligence are founded on the concept of entanglement, a fundamental characteristic in quantum mechanics. According to Erwin Schrödinger, 1948, "entanglement consists in the fact that a single observable (or set of commuting observables) of one system is uniquely determined by a single observable (or set of commuting observables) of the other " For two entangled systems, say A and B, knowing the state of one of its parameters provides system A with complete knowledge (prediction!) of the corresponding parameter in system B. The higher the entanglement between the two systems, the higher their ability to predict and influence one another. ​ As we propose, information is thus an indication and a measure of the level of entanglement between two systems, which corresponds directly to their ability to predict and be predictable. Entanglemnt How the IDT uses Information to Enable Intelligence As an AGI agent , the Information Digital Twin, IDT, or the human-specific version, the Human Digital Twin, HDT, enable their users to measure their level of information (entanglement or predictability) as they interact with their environment. This measurement is based on calculating the probabilities of the various parameters involved in the user-environment interactions as captured in the Information Heat Map (IHM) . The various use cases (Shipment Digital Twin , Human-Machine Teaming ) provide examples of the information map structure used to calculate the user-environment information. As indicated in the two examples, any change of any involved probability would change the value of the user-environment information. The IDT can then use the information change to update its search for alternative actions to maintain or increase the user-environment information, which reflects the IDT intelligence. Currently, we have two pending patents covering the Social AI Platform's (and the Information Digital Twin) novel techniques, architecture, and algorithms Research Focus-Improving RL Agents Performance and Adaptability Current RL (reinforcement learning) agents rely on an interpreter* (or the agent designer) to establish the task states, rewards associated with different states, and agent objectives (e.g., end state). Changes in the environment and/or the agent's objectives necessitate the interpreter to intervene and adjust the states and/or rewards to adjust the agent's behavior accordingly. Suppose the agent can independently update its states and rewards based on its objective. In that case, it will become more adaptable to changes in the task environment and more general because it can adjust its behavior to multiple objectives. ​ * https://en.wikipedia.org/wiki/Reinforcement_learning General RL Agent Using Agent Information (its Level of Entanglement with the Environment!) to Optimize its Actions Replacing the observer with the Information Heat Map (IHM) , which captures agent-environment interaction information, will allow the agent to dynamically update its states and rewards based on its objectives and thus be able to adapt to changes in the environment. ​ The current emphasis of our research is to validate this assumption. Furthermore, suppose we assume that the agent's ultimate objective is to improve its total information. In that case, the agent can use changes in interaction information to simulate numerous actions and choose the ones that will increase its information in the long run. INFORMATION DIGITAL TWIN

Vision Provide individuals with AI means to navigate social challenges effectively while maintaining their individuality and independence. Mission Information Digital Twins Everywhere Semarx is about Using AI for the Good of the Individual Semarx stands for "Semantic Matrix." That's what we do: capture semantic correlation across complex modalities, experiment, predict and prescribe actions for automated and effective decision-making. ​ With the increasing digitization and use of AI in most areas of life, we became interested in how to enable individuals to establish a balance of attention between immediate and long-term interests. This is the motivation behind the Information Digital Twins (IDT) technology: to let a person choose how to deal with the growing demands of society based on their own preferences and interests. We are a team of technology professionals focused on innovating using IDT and AI, derive personalized data-driven insights to enable everyone to make balanced choices, proactively navigate social & commercial challenges, and pursue their interests in an informed, healthy manner. ​ In Social AI, our aim is to empower individuals and leverage their social involvement in the face of social challenges; we also aim to offer social institutions the AI tools they need to reflect individuals' sentiments, interests, and preferences in planning for social changes and decisions. ​ We partner with various system integrators and technology vendors to provide our solution, the Social AI Platform. INFORMATION DIGITAL TWIN