Day 2

The talk will focus on how Dyota AI is delivering AI-based services to the Indian state government. The talk will also focus on the process of integrating such services with challenges and the core part of AI integration with 2 D & 4 D Radar.

“The Future of Conversational AI” explores the evolution of chatbots and virtual assistants driven by advancements in NLP. This talk highlights how these systems are becoming increasingly sophisticated, offering more human-like interactions. It delves into the implications for customer service, mental health support, and personal productivity. The presentation will also discuss emerging trends, the integration of multimodal AI, and the ethical considerations in developing AI that closely mimics human conversation, shaping the future of human-AI interaction.

Utilising large language models, businesses can construct powerful conversational business intelligence systems, enabling natural language interactions for seamless data analysis and informed decision-making. This integration enhances accessibility and efficiency in extracting insights from complex datasets.

Gen EYE is an innovative solution in addressing data security and confidentiality in Large Language Models (LLMs). This cutting-edge solution implements an advanced security and observability framework, ensuring the protection of sensitive data from inadvertent exposure & meticulously monitors user prompts. Additionally, it features robust activity logging, empowering users with comprehensive insights for cost and billing management. Elevate your Gen AI/ LLM experience with Gen EYE, where enhanced security seamlessly intertwines with observability, setting a new standard in safeguarding data integrity and confidentiality.

This talk will cover the practical lessons learned from the speaker’s experience in developing large-scale ML systems for search and recommendations. Both search and recommendations are the most impactful and yet the most complex applications of ML in the industry. Interestingly, most of these challenges do not arise from the algorithmic complexity but from the constraints related to business, technology and people. The talk will focus on a few key challenges and potential ways to overcome or avoid those.

The talk will focus on the below aspects:

1. The importance and relevance of Large Language Models (LLMs) in today’s artificial intelligence landscape.

2. Optimizing the performance of LLMs: Techniques such as prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG) will be discussed.

3. Challenges and ethical concerns associated with using LLMs in real-world applications. will address the mitigations and methods to counter these issues.

4. Tackling the issue of trust in language models, with a focus on developing models that are reliable and less prone to hallucinations. We will explore strategies and techniques to mitigate hallucinations in language models.

5. The impact of LLMs on enhancing productivity, customer satisfaction, and decision-making processes, along with discussing the limitations and potential areas for future improvement in the application of LLMs across various domains.