​​💡 Before registering for this event, please read the note about event recording and photography at the end of the event details.

​​Welcome to the Unstructured Data Meetup by Zilliz 🙌

​Details

​Topic: Connecting your unstructured data with LLMs

​We are meeting for our first happy hour/discussion group about Unstructured Data and its future in machine learning and LLM apps!

What we’ll do:
Have some snacks and refreshments. Have a couple of talks and then unstructured networking.

​Schedule

​6:00-6:45 - Open Doors, food and open networking
6:55-7:15 - Fact vs. Fiction: Autodetecting Hallucinations in LLMs - Morena Bastiaansen
7:15-7:35 - Vector Databases 101  - Stephen Batifol
7:35-8:00 - On training state-of-the-art general text embedding - Bo Wang
8:00-9:30 - Networking

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

​When:
April 16th, 2024
6:00pm
GetYourGuide office

Sponsored by Zilliz and GetYourGuide

​Tech Talk 1: Fact vs. Fiction: Autodetecting Hallucinations in LLMs
Speaker: Morena Bastiaansen, GetYourGuide
Abstract: The rise of Large Language Models has revolutionized the landscape of AI, unlocking huge potential across society. However, it has also introduced the challenge of hallucinations - instances where the model generates rather trippy content in a scarily convincing way. Rest assured, Morena will guide you through an exploration of how we can automatically detect these instances of hallucination to fully unleash the potential of LLMs.

​Tech Talk 2: Vector Databases 101 - An introduction to the world of Vector Databases 
Speaker: Stephen Batifol, Zilliz 
Abstract: An introduction to Unstructured Data and the world of Vector Databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. 

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Tech Talk 3:  On training state-of-the-art general text embedding
Speaker: Bo Wang, Jina AI
Abstract: Embeddings have become a crucial component in contemporary vector search and Retrieval Augmented Generation (RAG) systems. In this talk, I aim to provide a comprehensive overview of training a versatile embedding model, strategies for encoding longer information within such models, along their benefits and limitations. Additionally, I'll delve into various forms of deep learning-powered retrievers.

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​📸​​​Important note: Please be advised that this event will be recorded and photographed. If you prefer not to be included in any recordings or photographs, please do not hesitate to let us know during the event. Your comfort and privacy are important to us.