Day 1

In this talk, we explore the remarkable journey of a young, dynamic team at the Indian Space Research Organisation (ISRO) as they pioneer Artificial Intelligence solutions for both space and terrestrial applications. We delve into the unique challenges of deploying AI systems in orbit—from creating robust models that function in extreme conditions to ensuring reliable decision-making under stringent resource constraints.
On Earth, these innovations fuel breakthroughs in advanced computer vision—ushering in new standards for space exploration and satellite technology. We also highlight GYAAN, an outstanding AI Assistant that reaches millions of users including students in Bihar, demonstrating how ISRO’s AI-driven achievements directly contribute to India’s growth.

Throughout this session, we reveal the step-by-step innovation process—identifying mission-critical needs, deploying generative AI for research and development, and implementing health-monitoring solutions for space missions.. We examine the successes, lessons learned, and evolving strategies that will shape ISRO’s AI-driven future. This journey of experimentation, perseverance, and creative problem-solving offers invaluable insights for any organization looking to harness AI for impact on Earth and beyond.

A big agentic switch is happening globally due to AI (artificial intelligence), whereby consumers and enterprises are switching to AI agents for task completion in an autonomous way. This paradigm shift due to AI is leading to the proliferation of AI agents in enterprises requiring innovative agent network architectures for better discovery, communication and security of these agents. Here we present a novel agent network architecture where we introduce innovative concepts like agent registry, agent name server, agent text transfer protocol (ATTP), agent private cloud and agent gateway. Quite a lot of parallels have been drawn from the current world wide web (www) Internet architecture, where similar components exist to support discovery, security and communication.

In this session, I’ll dive deep into the technical challenges and solutions in building enterprise-grade speech AI systems that power content globalization. We’ll explore how Dubverse has developed and scaled speech foundation models, including text-to-speech, speech-to-speech and speech-to-text technologies. I’ll share insights on optimizing model throughput, handling low-resource languages, and building robust APIs. The talk will cover practical approaches to solving critical problems in voice technology, making it relevant for developers working on speech, audio, or multilingual applications.

This research explores AI Twins, lifelike digital avatars enabling real-time interaction through advanced AI-driven speech synthesis, expressions, and contextual understanding. It compares two implementations: a closed-source system utilizing tools like Synthesia and Eleven Labs for high-quality video and audio, and an open-source alternative with Nvidia Riva, Nemotron LLM, Wav2Lip, and Sadtalker for speech and lip-sync. Both use Retrieval-Augmented Generation (RAG) and AI Agents for enhanced domain intelligence, offering applications in personalized education, training, customer support, and virtual companionship.

GenAI is real. Business transformation using GenAI is also real. It is powerful and pervasive. But a disruptive technologies like GenAI cannot be devoid of issues and more so on biases and ethics. This session is to bring awareness on some of the biases and ethical issues.

Generative AI is no longer just for experts—Low-Code/No-Code platforms are revolutionizing how AI is built and deployed, empowering anyone to create impactful solutions without writing a single line of code. This session unveils how these tools are breaking barriers, enabling faster innovation, and driving inclusivity in AI adoption. Through real-world examples and actionable insights, discover how to harness the power of GenAI to transform industries and democratize innovation for the masses.

Statistically more than 50% data science problem fails to get implemented successfully in real business scenario. This is especially more in case of Indian traditional business houses. More than the solution approaches, major reasons for this failure are – lack of explain ability to business stakeholders and lack of knowledge on bringing human factors from the actual execution team. This session will talk about how a comprehensive ML based solution is developed and successfully implemented in real business scenario with demonstrated value benefits.

This research details the creation of an innovative biomedical chatbot aimed at improving patient question-answering. It employs a mixture of expert models—fine-tuning one LLaMA3 on patient-doctor interactions and another on general biomedical datasets—and combines them via a router. A vector database supports Retrieval-Augmented Generation (RAG) for accurate, context-aware responses. Rigorous evaluation across biomedical datasets highlights its ability to enhance patient engagement and healthcare delivery, showcasing the transformative potential of generative AI in medicine.