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    Making AI Understandable: Demystifying the Black Box


    Explainable AI (XAI) is an emerging field in machine learning that aims to address how black box decisions of AI systems are made

    More than 75% of organizations trying to achieve Digital Transformation – despite collectively spending more than a trillion dollars – have failed to achieve their transformation goals, evidence that they and their strategic partners are still climbing the transformation learning curve.
     
    Organizations are increasingly relying on artificial intelligence (AI) systems to make decisions that can significantly affect individual rights, human safety, and critical business operations. But how do these models derive their conclusions? What data do they use? And can we trust the results?
     
    Artificial-Intelligence-mintmeshEven as explainability gains importance, it is becoming significantly harder. Modeling techniques that today power many AI applications, such as deep learning and neural networks, are inherently more difficult for humans to understand. For all the predictive insights AI can deliver, advanced machine learning engines often remain a black box. The solution isn’t simply finding better ways to convey how a system works; rather, it’s about creating tools and processes that can help even the deep expert understand the outcome and then explain it to others.
     
    To shed light on these systems and meet the needs of customers, employees, and regulators, organizations need to master the fundamentals of explainability. Gaining that mastery requires establishing a governance framework, putting in place the right practices, and investing in the right set of tools.
     
    Major corporations around the world have trusted Mintmesh to bring our unique expertise on cloud based AI digital platform solution, to guide their Digital Transformation efforts.
    Mintmesh has a proven track record in helping clients not only achieve their transformation goals, but to outpace their traditional competitors by saving up to 41% technical bid evaluation time. We’ve worked side-by-side with our customers to tackle some of the most daunting transformation challenges imaginable, resulting in demonstrated, documented ROI.
     
    Mintmesh’s, "Making AI Understandable: Demystifying the black box”, series is about assisting the EPC organizations to decode the use cases of AI and help establish a good fit for explainability technology, in an easy-to-follow educational format.
     
    The Webinars are free, but registration is required.

    Upcoming Webinar

    LLM for EPC
    Chapter 5: Transforming Enterprise Knowledge Management using Large Language Models (LLMs)

    In today's fast-paced business landscape, harnessing the power of knowledge is paramount. Explore the untapped treasure trove of historical data with LLM, especially for EPCs, and understand how it can reshape your strategies.

    Date: 18th October 2023
    Time: 10am EST (7:30pm IST)

    Register Now

    Past Webinar

    AI
    Chapter 1: Unlocking the Potential of AI; Digital Transformation: A Revolution in the EPC World

    If you are feeling stuck in your digital transformation journey, you are not alone. One of the hardest questions in digital transformation is how to get over the initial humps from vision to execution.

    Access Webinar
    Explainable AI-2
    Chapter 2: Benefits and applications of Explainable AI

    Continued advances promise to produce autonomous systems that will perceive, learn, decide, and act on their own. However, the effectiveness of these systems is limited by its inability to explain their decisions and actions to human users.

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    Deconstructing AI Decisions
    Chapter 3: Deconstructing and making sense of AI Decisions

    This webinar will delve into the decision-making processes of AI systems, elaborate on techniques to make AI systems more transparent and interpretable, and explore how Human In The Loop (HITL) AI solutions foster trust between users and systems.

    Access Webinar
    The role of AI in Technical Knowledge Management
    Chapter 4: Empowering Engineers: The role of AI in Technical Knowledge Management  

    Ever wondered how AI could spice up your engineering game?

    Delve into the lives of the engineers, just like you, swapping their toolkit for the smart world of AI.

    Access Webinar

    Industry Voice

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    According to a report by MarketsandMarkets, the global AI in the construction market is expected to grow from $407 million in 2018 to $1.8 billion by 2024, at a compound annual growth rate (CAGR) of 34.0%

    McKinsey-LogoAccording to a report by McKinsey, companies that adopt AI and ML in the construction industry can improve productivity by 50% and reduce project timelines by 20%

    World_Economic_Forum_logo.svgAccording to a study by the World Economic Forum, AI and ML can help the construction industry save $1.6 trillion by 2030

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