In this session, you'll learn how Generative AI is transforming the developer experience by speeding up tasks throughout the development lifecycle. It helps with researching and evaluating system designs, building secure and scalable applications, upgrading existing ones, and optimizing performance. Generative AI can simplify these processes.

You'll also see how developers can use Generative AI to create specification documents, generate documentation, understand complex code, and more. This enhances productivity and streamlines the development process.

Step into the future of AI with an immersive dive into the cutting-edge world of Edge Gen AI and Quantum Teleportation technologies. This session explores the intersection of multi-threaded AI workflows and the powerful anomaly detection capabilities made possible by Quantum-inspired computing.

Discover the latest trends in Gen AI ModelOps, where agile deployment strategies for domain-specific generative AI models are accelerating innovation in industries ranging from healthcare to defense. We’ll uncover how GraphRAG (Graph Retrieval-Augmented Generation) is revolutionizing data interaction, enabling real-time knowledge graphs to enhance AI inference and decision-making.

Explore Poly AI, a framework pushing the boundaries of multi-modal and multilingual AI systems, delivering unprecedented versatility across languages, data types, and platforms. Learn how these advancements are reshaping AI landscapes, paving the way for hyper-customized, scalable solutions for domain-specific applications.

Join Jonathon for this exciting journey, where quantum principles, AI, and domain-specific intelligence converge to redefine the future of automation, knowledge discovery, and beyond. Expect to walk away with actionable insights and the latest strategies for leveraging GenAI in your enterprise's edge environments.

This immersive workshop is designed for artificial intelligence engineers, professionals, testers, and developers who are eager to leverage the cutting-edge capabilities of AI to enhance their engineering strategies, automate processes, and uncover new opportunities with unparalleled accuracy. By blending theoretical knowledge with practical exercises, participants will gain firsthand experience in harnessing the power of AI-Augmented to transform their approach to software engineering, making it more efficient, effective, and innovative.

Throughout the workshop, attendees will explore the core concepts of generative AI and its application in software engineering. Through a series of hands-on exercises, interactive sessions, and live demonstrations, attendees will learn how to integrate these AI-powered tools and techniques into their daily workflows, elevating the quality of their productivity and the products they help to create.

Join us for an exclusive workshop where you’ll learn how to build your own Generative AI (GenAI) application using Retrieval-Augmented Generation (RAG). In this hands-on session, we will guide you through creating an AI chatbot that retrieves relevant information from contextualized data. You’ll gain practical experience in storing vector embeddings in a vector database, a crucial component for efficient similarity searches in AI applications. Additionally, we’ll delve into the RAG architecture, exploring how integrating retrieval mechanisms with generative models enhances the accuracy and relevance of AI-generated responses.

This workshop is designed for anyone looking to dive deeper into building practical and impactful GenAI solutions. Don’t miss this opportunity to enhance your skills and bring your ideas to life!

Agentic Systems and extensible compound AI systems are revolutionizing LLM applications, positioning themselves as critical tools in modern AI development. These advanced systems go beyond traditional automation, offering capabilities that drive significant productivity and efficiency gains in enterprise and commercial workflows. However, adopting AI Agents and Agentic Systems at scale poses unique challenges, particularly in ensuring consistent performance, reliability, and scalability.

Central to overcoming these challenges is the role of memory. Memory within AI systems is not only essential for retaining operational data but also for enabling adaptive learning, entity profiling, and customized interactions. Different types of memory, such as short-term and long-term memory, play distinct roles in supporting an agent’s functionality. This talk will delve into the architecture of Agentic Systems and examine how various forms of memory—working memory, data stores, profilers, and toolboxes—contribute to creating robust, efficient, and scalable AI solutions. Attendees will gain insight into how memory is leveraged to enable learning from past executions, personalize interactions, and enhance system capabilities in complex AI applications.

Generative AI have become essential in advancing AI, enabling remarkable capabilities in natural language processing and understanding. However, the efficient deployment of LLMs in production environments reveals a landscape of challenges and technical debt

Ethically, LLMs face issues such as bias amplification, where they might perpetuate existing stereotypes in their outputs. Misinformation is another concern, with the potential misuse of LLMs to create convincing yet false narratives. Privacy risks emerge from LLMs possibly memorizing and revealing personal data. Moreover, societal challenges include the impact on employment, as LLMs could automate tasks but also lead to job displacement. These challenges highlight the need for careful management and ethical considerations in the deployment of LLMs

In this talk, Ahmed will highlight the key challenges and technical debt associated with LLMs' deployment, which demands customization and sophisticated engineering solutions not readily available in broad-use machine learning libraries or inference engines