WEBVTT

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As we transition from the broad applications of LMS in modern AI, it's crucial to address the flip

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side of this powerful technology.

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In the next section, we will guide you through four major challenges that stand at the forefront of

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ethical and effective LLM deployment.

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We will explore the phenomenon of hallucination, where llms generate information that might not be

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grounded in reality.

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Next, we tackle bias and ethical concerns, exploring how biases can shape the outputs of these models

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in ways that require careful navigation.

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Our journey continues with data privacy and security, highlighting the critical importance of safeguarding

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sensitive information in an era of expansive data usage.

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Lastly, output alignment will shed lights on, ensuring that LLM outputs match the intended goals and

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values of their applications.

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These topics are not just challenges.

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These are opportunities for innovation and responsible AI development as we explore each constraint

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in the upcoming slides.

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Keep in mind that understanding these limitations is the first step towards harnessing the full potential

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of Llms in a way that is both ethical and impactful.