{"id":35359,"date":"2024-06-13T14:37:21","date_gmt":"2024-06-13T13:37:21","guid":{"rendered":"https:\/\/digileaders.com\/?p=35359"},"modified":"2024-06-13T14:37:21","modified_gmt":"2024-06-13T13:37:21","slug":"digital-triplets-extending-digital-twins-to-create-an-ai-powered-virtual-third-party-advisor","status":"publish","type":"post","link":"https:\/\/digileaders.com\/digital-triplets-extending-digital-twins-to-create-an-ai-powered-virtual-third-party-advisor\/","title":{"rendered":"Digital triplets: Extending digital twins to create an AI-powered virtual third-party advisor"},"content":{"rendered":"

Advancements in artificial intelligence (AI) and machine learning have been transforming business operations across industries over the past three decades. Used responsibly, these technologies enable innovative solutions for evidence-based decision-making, predictive and prescriptive actions, intelligent automation and robotics to achieve trusted outcomes.<\/p>\n

However, in applying AI, organizations face challenges in terms of usability, interpretability, and the end user experience. Such challenges require innovative approaches, and this is where digital triplets come into play.<\/p>\n

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What is a digital triplet?<\/h3>\n

A\u00a0digital triplet<\/a>\u00a0extends the\u00a0digital twin<\/a>\u00a0model to enable a decision-maker to use advancements in AI to interrogate the digital twin data. This includes the ability to request more situational information and to simulate and optimize outcomes under different scenarios.<\/p>\n

The term digital triplet was first introduced in 2019 as a framework for combining the digital twin with two AI components\u2014one for generating and comparing scenarios and another for explaining and justifying the recommendations. The concept was further developed in 2020 and applied in the context of precision medicine and chronic disease management.<\/p>\n

CGI\u2019s digital triplet approach applies AI-driven analysis to increase the usability and interpretability of the insights. By synthesizing data quickly and communicating outcomes clearly, digital triplets help organizations improve decision-making.<\/p>\n

The objective is to enhance both the AI capabilities and the human-AI interaction across multiple sectors. This allows for more personalized, evidence-based, and transparent decision-making and recommendations. In this way, the digital triplet serves as a virtual third-party advisor to the digital twin end user or decision-maker.<\/p>\n

CGI\u2019s Digital Triplet Approach: Third-party advisor for decision-makers<\/strong><\/span><\/p>\n

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As shown above, a digital triplet has three core components:<\/p>\n

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  1. The physical entity<\/strong>, the entity being evaluated to improve the function or outcomes.<\/li>\n
  2. The digital twin<\/strong>, which models the physical entity using data-driven and knowledge-based methods. It integrates real-time and in-context data from various sources (e.g., operations, equipment, devices and media) to help decision-makers better understand the current (as-is) and future (could-be) states of a target physical entity. The digital twin predicts the effects of different scenarios to improve risk mitigation and shape the desired future state.<\/li>\n
  3. The digital triplet<\/strong>, the intelligent advisor using generative AI (GenAI) and explainable AI (XAI) \u00a0to allow the user to interrogate the digital twin to make complex decisions.<\/li>\n<\/ol>\n<\/blockquote>\n

    Today, digital twins help researchers conduct experiments and test hypotheses via computer simulation, without exposing the real entity to any risks or harm. Therefore, they are powerful tools for predictive maintenance, algorithmic operations, evidence-based decision-making, and more.<\/p>\n

    However, a digital twin alone may not be sufficient to provide optimal guidance for decision-makers who need to consider the implications of the information provided\u2014not only the current and future state of a physical entity, but also the trade-offs, uncertainties, and preferences involved in making complex decisions related to that entity.<\/p>\n

    The digital triplet extends the digital twin to compare scenarios and make recommendations<\/h3>\n

    The digital triplet adds another layer of AI to generate and compare multiple scenarios, recommend the next best actions (options\/advisor), interpret outcomes of scenarios in multiple states and contexts, and explain the reasoning behind digital twin interpretations. It can also provide more context for any resulting recommendations.<\/p>\n

    Additionally, the digital triplet enables interactions and scenario evaluations to be conducted in natural language through text or speech. It thus acts as an intelligent assistant, a virtual third-party advisor, that supports the human expert in making the best possible choices for any scenario.<\/p>\n

    Digital Triplet Interactions<\/strong><\/span><\/p>\n

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    Examples of digital triplets in action<\/h3>\n

    While a relatively new concept, the digital triplet is not merely theoretical. It is a practical solution that has been implemented across multiple environments. For example:<\/p>\n

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