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Workshops / Tutorials
Mastering AI Image Generation for Creatives
From Concept to Stunning Visuals by Dr. Seema Chokshi
This hands-on tutorial will empower creative professionals and content creators to leverage AI image generation tools like Midjourney and Stable Diffusion to rapidly ideate, prototype, and produce stunning visuals. Through guided exercises and expert tips, participants will learn to craft effective text prompts, iterate on AI-generated images, and integrate them into professional design projects. By the end of the session, attendees will have gained practical skills to start using AI to amplify their creative output and streamline their workflows.
Participants will leave with a solid understanding of the creative possibilities and ethical considerations around this exciting new technology.
Graph Data-Driven Recommendation Systems Empowered by Generative AI
by Ravi Ranjan
This showcase session unveils how we are using Generative AI to take a giant leap forward, revolutionizing how we approach these issues. Despite the remarkable strides made in recommendation systems over recent years, they often fall short when tasked with the complexities inherent in travel planning. Conventional systems, such as collaborative filtering or content-based filtering, struggle to encapsulate the diverse factors influencing individual user preferences, from potential destinations and activity types to distinct times of travel. Furthermore, these systems frequently overlook the social dynamics inherent in travel planning, failing to factor in user communities and the evolving landscape of travel trends.
Going From Unstructured Data to Vector Similarity Search
by Steven Pousty, PhD
One of the key concepts used in AI modeling is the storage and query of vectors. This workshop will start with 2 examples of unstructured data, images and journal abstracts. Participants will then work this data all the way through to a usable data store with an application on top it.
We will cover things such as transformers, embeddings, HuggingFace, choosing a vectorization model, and effective query composition. The python code needed to do this is quite easy to understand. If you want to work on your own laptop, there will be prerequisites published beforehand. If not, we will use a cloud-hosted environment for you to do all your work. After this workshop you should be able to understand some of the concepts being used in these new AI architectures and have a better grasp on the work involved with building “AI” applications.
Talks
Operationalizing GenAI
Effective LLM Compression and Optimization Methods by Nijesh Kanjinghat
In today's rapidly evolving AI landscape, deploying large language models (LLMs) efficiently is a significant challenge, especially when balancing performance with cost. "Operationalizing GenAI: Effective LLM Compression and Optimization Methods" delves into cutting-edge techniques to streamline the deployment of generative AI applications. This session is designed for AI practitioners, data scientists, and machine learning engineers who are keen on enhancing their expertise in model optimization and seeking practical solutions for real-world applications.
Attendees will explore a range of LLM compression techniques, including quantization, pruning, and knowledge distillation, and understand how these methods can significantly reduce model size and inference costs without sacrificing performance. The session will also cover optimization strategies that improve model efficiency and speed, making it easier to deploy LLMs across various platforms and environments.
Retrieval-Augmented Generation (RAG)
A Synergistic Approach to Natural Language Understanding and Generation by Shalvi Mahajan
The Retrieval-Augmented Generation (RAG) paradigm represents a novel architecture at the intersection of retrieval and generation models, designed to address the challenges in natural language understanding and generation tasks. This approach seamlessly integrates the strengths of information retrieval techniques with the creative capabilities of natural language generation, thereby achieving enhanced performance across a spectrum of language-based applications.
Multi-channel Multi-granularity Forecasting on High Velocity Data
by Thejas Bhat and Naveen Rathani
n the advent of real time delivery many customer facing industries have a enormous data flowing into the system every minute. Businesses are forced to use these data to efficiently manage daily operations, to forecast future demands and to make strategic and tactical decisions to maintain competitive advantage within the industry. Forecasting is one such use case that has continued to have its place in monitoring, managing and planning in every industry. In large scale organisations each stakeholder or business vertical have their own requirement in terms of aggregation level as well as time-grandlarity of each forecast.
Securing Mobile Transactions
Large Language Models and the Future of FinTech Apps by Sanjaikanth Pillai
Digital Payment Transactions (DPT) have become an essential aspect of our daily lives, rendering traditional methods of carrying hard currency obsolete. Mobile handheld devices are now commonly utilized for transactions, which unfortunately opens unlimited possibilities for hackers and scammers. The discussion delves into various Low Latency Verification (LLV) trends in FinTech on mobile handheld devices, exploring the applications of LLM in FinTech and the potential of LLM alongside AI/ML in FinTech. Moreover, it addresses the limitations of LLM in achieving full implementation strength.
Ascribing Responsibility for Unintended
Consequences of AI Influence by Dr. Seema Chokshi
This talk focuses on the crucial issue of how to attribute responsibility when AI systems lead to unintended consequences by influencing human decision making.
I will introduce the concept of the "decision point dilemma," where it becomes unclear whether the human or the AI should be held accountable for negative outcomes resulting from AI-swayed decisions.
Leveraging LLMs for Internal Knowledge Discovery
by Raghav Bali and Timothy Low
Efficient knowledge discovery is crucial yet challenging in today's fast-paced, globally distributed work environments. This talk introduces a Retrieval Augmented Generation (RAG) based bot integrated with internal communication tools such as slack. It is designed to streamline access and discovery of documentation spread across multiple internal knowledge platforms and code-repositories and multiple languages. We will explore the chatbot's innovative architecture and practical applications.
Emergent Intelligence
Towards Ecosystem Operating Systems by Dr. Shailesh Kumar
