The talk titled “Introduction to OpenClaw in AI: Concepts and Real-World Use Cases”, delivered on 26th March 2026 by Dr. Venkata Gopal E, Associate Professor from the AIML Department, provided an insightful overview of OpenClaw and its role in modern artificial intelligence. The session covered fundamental concepts, key features, and how OpenClaw can be integrated into AI workflows to enhance performance and scalability. Dr. Venkata Gopal also highlighted several real-world applications across different industries, helping students understand its practical relevance. The talk was attended by around 50 students, who actively engaged in the session and gained valuable knowledge about emerging tools and technologies in AI.
The event explores the historical development and emerging trends in Natural Language Processing, highlighting the transition from manually crafted rule-based systems to data-driven machine learning models and finally to powerful generative AI and large language models. The talk discussed key breakthroughs such as statistical NLP, word embeddings, transformer architectures, and generative models that enable applications like chatbots, translation, summarization, and intelligent assistants. By examining both past developments and current innovations, the session provided insights into how NLP is transforming human–computer interaction and the future possibilities of language-based AI systems.
The expert talk by Subham Kumar, AI Consultant at Amazon Web Services, provided an insightful overview of the evolution of Artificial Intelligence from traditional rule-based systems to advanced generative models such as GPT, LLaMA, and multimodal architectures. The session highlighted the transformative impact of Generative AI across sectors including healthcare, finance, education, entertainment, and automation, while also explaining core concepts like NLP techniques, transformer architectures, large-scale datasets, and self-supervised learning. Key challenges such as data privacy, hallucination in AI models, bias mitigation, computational costs, and responsible AI governance were discussed alongside emerging trends like autonomous AI agents, open-source foundation models, low-code/no-code AI platforms, and hybrid human-AI collaboration. The talk concluded with an engaging interactive Q&A session where participants explored practical implementation challenges, cloud-based AI solutions, and industry-oriented project ideas.
In the talk, Ms. Durga Bhawani Gajula from Bosch Global Software Technologies embarked on a comprehensive discussion about working of an Large Language Models (LLMs), cutting-edge advancements in LLMs, Large Language Models (LLMs) are powerful but prone to hallucinations—producing fluent yet factually incorrect answers. Retrieval-Augmented Generation (RAG) offers a practical solution by grounding LLM outputs in external, trusted knowledge sources. Instead of relying solely on parametric memory, RAG pipelines retrieve relevant documents or data at query time and use them to guide generation, significantly reducing hallucinations.
In the talk, Mr. Sahil Verma and Mr. Eby Kuriakose embarked on a comprehensive discussion abot working of an Large Language Models (LLMs), cutting-edge advancements in LLMs, Agenting AI, Tasks needed to be resolved/acheived for an Multi Agentic environment and career landscape in GenAI , NLP and Artificial Intelligence domain. They taught the students what goes in the background of Large Language Models like ChatGPT, Grok etc. The session started with an overview of the fundamentals of LLMs and then transitioned to the latest innovations in LLMs, behind the scenes of ChatGPT, Agentic AI and Multi Agentic AI.
They also gave the perspective of what kind of challenges they are dealing with in AWS to make Multi Agentic AI a reality.
They discussed the Carrier Impact GenAI and NLP can create. Also discussed open domains in GenAI and NLP which the students can solve and become a successful AI engineer/ researcher.
The event began with a short presentation by Dr. Richa Tengshe (Head CoE NLP) about the history, current trends and the future direction of NLP. After the presentation six student teams presented their posters and explained the idea behind their creation.
Innovative talk was conducted by CoE NLP in association with Department of AIML CMRIT. It was held on 13th Feb 2025 from 2 pm to 4 p.m. in AV HALL, the fourth floor of the D Block, CMRIT. The session started with an introduction session about the introductory workflow in NLP and need of error correction in Kannada text by Prof. Sushmitha R AP, AIML CMRIT. Her research areas included Kannada Textual error correction using T5. This was a part of her MTech thesis. A total of 23 including students and faculties from CMRIT attended the event.
The presentation began by providing an overview of LLMs and their capabilities in natural language understanding and generation. It then delved into the concept of fine-tuning, wherein pre-trained LLMs are adapted to domain-specific tasks by further training on task-specific data. In the context of healthcare, fine-tuning enables LLMs to understand medical terminology, comprehend clinical notes, and generate contextually relevant responses.
In his talk, Dr. Rajath embarked on a comprehensive journey through the evolution of Natural Language Processing (NLP), from foundational embedding techniques to the cutting-edge advancements in Large Language Models (LLMs). The session started with an overview of the fundamental concepts of NLP, exploring the development of embeddings such as Word2Vec, GloVe, and transformer-based embeddings like BERT. The talk then transitioned to the latest innovations in LLMs, focusing on the Mistral model, a state-of-the-art language model designed for complex NLP tasks.
A live demonstration of the the practical application of these concepts using Streamlit, showcasing the use of Mistral LLM for question answering in the domain of material science is done. The demo highlighted how Mistral can be integrated with Retrieval-Augmented Generation (RAG) techniques to enhance document matching. The talk also demonstrated how to retrieve relevant context from a corpus of saved documents and effectively use this context to craft prompts for the Mistral model, thereby improving the accuracy and relevance of the generated answers.
The session started with an introduction session about the objective and requirement of the Gen AI by Dr. Chandrika Senior Scientist at ABB Bengaluru. Exploring the potential of generative AI for prototyping and productivity environments can be both transformational and exciting. This Innovative Session unlocked new levels of efficiency, creativity, and innovation. Provides insights into the latest patent trends and applications on generative AI algorithms, AI education on tracking student progress and AI in Autonomous Vehicles etc. Important articles in the field were referred to in order to give an understanding of the research being done in generative AI. How to use generative AI for particular tasks by sharing domain-specific system designs.