Delve into the technical aspects of different Generative AI models, how they work, their capabilities and benefits (and drawbacks), use cases, and more. Learn how to select the right AI model for your specific needs and evaluate its performance.

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Learn about the practical challenges of deploying generative AI models in production environments and discover best practices for ensuring performance, reliability, and scalability. Explore strategies for overcoming challenges such as data quality, model bias, and explainability. Learn how to integrate AI into your existing systems and processes to drive business value.

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Explore how reinforcement learning can be combined with generative AI to create more intelligent and adaptable systems. Discover how reinforcement learning can be used to train AI models to make decisions, learn from experience, and improve over time. Understand the applications of reinforcement learning in areas such as robotics, autonomous vehicles, and game AI.

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In this session, we’ll discuss the evolution of GenAI and the risks it poses, along with and an introduction to prompt hacking (including several ral-world examples). We’ll explore different Prompt Injection Attack scenarios, as well as ways to mitigate those threats.

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Discover techniques for adapting pre-trained generative AI models to specific tasks and domains, enhancing their performance and relevance. Learn how to fine-tune models to generate more accurate and relevant outputs. Explore the benefits of transfer learning and how it can accelerate AI development and reduce costs.

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Explore the role of generative AI as a copilot in various enterprise functions, from customer service to knowledge management. Discover how AI can augment human capabilities, improve efficiency, and drive innovation. Learn how to integrate AI into your workflows and leverage its potential to enhance your business operations.

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As businesses across the globe seek to gain competitive advantages and optimize operational efficiencies, Agentic AI emerges as a pivotal technology, offering unprecedented opportunities for automation and decision-making. Consideration the next wave in Generative AI, Agentic AI has the potential to create higher levels of impact in business operations. This session will begin with an introduction to Agentic AI, discuss popular enterprise use cases for Agentic AI, and lay out the challenges in adopting Agentic AI and best practices to overcome these challenges.

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Address the challenges associated with Retrieval Augmented Generation (RAG) systems, including data quality, relevance, and hallucination. Discover strategies for improving RAG performance and ensuring that AI-generated content is accurate, informative, and trustworthy. Learn how to evaluate the quality of RAG outputs and identify areas for improvement.

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Discuss the ethical considerations surrounding generative AI, including bias, privacy, and transparency. Learn how to develop responsible AI practices that align with your organization's values and comply with ethical guidelines. Explore strategies for mitigating the risks associated with AI and ensuring that AI is used as it is intended.

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Understand the data privacy and security risks associated with generative AI and explore best practices for protecting sensitive data. Learn how to comply with data privacy regulations, mitigate data breaches, and ensure the ethical use of AI. Discover strategies for building trust with customers and maintaining data integrity in the age of AI.

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Discuss current limitations of GenAI, and how potential future advancements, including breakthroughs in AI models, computer vision, and reinforcement learning, and more, will overcome them. Explore how new innovations may impact GenAI and further revolutionize industries and create new possibilities for businesses. Understand the implications of these advancements for your organization and develop strategies to prepare for the future of AI.

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