Generative AI and Agentic AI Course
                     ARTIFICIAL 
INATRETLLIFIGICEIANLC E 
INATRETLG(LIeAFnIGIeIC)raEItiANveLC A IE 
INTEL•(RLevAoluItioGnIizi)ng TEechnoNlogy andC BusinEessARTIFICIAL  
INTEL(LAIGI)ENCE 
(AI)
Introduction
Generative AI is transforming the digital landscape by enabling machines to 
create content autonomously. This technology leverages advanced algorithms to 
produce text, images, and other media, opening new opportunities for innovation. 
Understanding its core principles and applications is crucial Afor businResses aiTming IFI
to stay competitive in a rapidly evolving market.
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Definition and Key 
Concepts
Generative AI refers to artificial intelligence systems 
designed to generate new data similar to existing 
data. It uses models like neural networks to create 
text, images, audio, or code. Key concepts include 
machine learning, deep learning, and neural 
networks, which enable AI to learn patterns and 
produce meaningful outputs autonomously.
ARTIFICIAL 
INTELLIGENCE 
(AI)
Applications Across Industries
Generative AI is widely applied in sectors such as marketing, entertainment, 
healthcare, and software development. It powers tools for content creation, 
personalized recommendations, drug discovery, and automated coding. 
Businesses leverage this technology to enhance productivitAy, innovaRte servicTes, IFI
and improve customer engagement through automated creative processes.
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Challenges and Ethical Considerations
Generative AI presents unique challenges including data privacy, bias, and 
misinformation. Ensuring ethical use requires transparency, accountability, and 
robust regulatory frameworks. Addressing these issues is esAsential to mitigate risks such as algorithmic bias and misuse of content, while fostering Rtrust in ATI IFI
systems across industries.
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Characteristics of Agentic AI
Agentic AI systems possess autonomy, decision-making capabilities, and goal-
oriented behavior. They can operate independently, adapt to new situations, and 
improve their performance over time. This type of AI mimics human agency, 
enabling more sophisticated and flexible interactions with cAomplex eRnvironmTents. IFI
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Current Trends and Innovations
Recent advances in agentic AI include reinforcement learning, self-supervised 
learning, and multi-agent systems. Innovations focus on enhanced autonomy, 
improved reasoning, and ethical frameworks for safe deployment. These trends 
drive progress in sectors like robotics, autonomous vehiclesA, and inteRlligent TIFI
decision support.
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Future Prospects and 
Impacts
Agentic AI is expected to revolutionize industries by 
enabling smarter automation, personalized services, 
and enhanced problem-solving. Its impact will 
extend to workforce transformation, economic 
growth, and ethical governance. Preparing for these 
changes involves embracing interdisciplinary 
collaboration and proactive policy development.
ARTIFICIAL 
INTELLIGENCE 
(AI)
Conclusions
Generative and agentic AI represent transformative technologies with significant 
potential and challenges. Ethical considerations and continuous innovation are 
critical to harness their benefits responsibly. Organizations must stay informed 
and adaptable to leverage AI’s evolving capabilities for sustAainable sRuccess.TIFI
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INTELL
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ART
THANKS IFICI
AL 
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ELLI
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(AI)
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