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Unstructured Data in LLMs

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Christy B.
Unstructured Data in LLMs

Details

Please make sure you're registered by clicking here. Github will email you a form before the event, which you will need to complete for your access pass.

Topic: Connecting your unstructured data with Generative LLMs

What we’ll do:
Have some food and refreshments. Hear three exciting talks about LLMs and unstructured data.
5:30 - 6:30 - Welcome/Networking/Registration
6:35 - 7:00 - Jon Bennion, MLE, Fox
7:05 - 7:30 - Filip Haltmayer, SWE, Zilliz
7:35 - 8:00 - Rob Crystal-Ornelas,PhD, Data Analyst III and Kasia Sitkiewicz, Growth Product Manager, GitHub
8:00 - 8:30 - Networking

Who Should attend:
Anyone interested in talking and learning about Unstructured Data and LLM Apps.

When:
October 24th, 2023
5:30PM

Where:
This is an in-person event! To secure your spot, kindly Register using this link (registering here on Meetup won't guarantee your entry). Thank you!
Co-sponsored by Zilliz and Confluent.

Tech Talk 1: How to Detect and Correct Logical Fallacies in LLM model output
Speaker: Jon Bennion, MLE, Fox
Abstract: Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model’s outputs. LM models are optimized to perform well on specific metrics like cosine similarity, safety, or helpfulness. However, optimizing for metrics alone does not guarantee logically sound reasoning. Users cannot depend on such outputs - if left unchecked, propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks.

Tech Talk 2: How to stream data with Kafka into your RAG application
Speaker: Filip Haltmayer, SWE, Zilliz
Abstract: In this talk, Filip will delve into the integration of Kafka (Confluent Cloud) with Zilliz Cloud (Hosted Milvus), showcasing how this synergy enables real-time data ingestion, parsing, and processing. The primary objective is to enhance the accuracy and performance of your Retrieval Augmented Generation (RAG) applications. Additionally, Filip will provide a comprehensive demonstration, offering practical insights into the capabilities and benefits of this integration.

Tech Talk 3: AI-Powered Pair Programming with GitHub Copilot
Speakers: Rob Crystal-Ornelas and Kasia Sitkiewicz, GitHub
Abstract: Learn how GitHub’s AI-powered code assistant can help you save time and innovate at any career stage. We’ll demo GitHub Copilot and show the newly beta-released Copilot Chat. Also, we’ll share tips on how to prompt Copilot to make the most of this AI pair-programmer.

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