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| 1 | +--- |
| 2 | +date: 2024-09-29 |
| 3 | +time: 20h:00min |
| 4 | +duration: "1:27:53" |
| 5 | +title: "Vector Databases" |
| 6 | +tags: ["AI"] |
| 7 | +category: "AI" |
| 8 | +isNext: false |
| 9 | +youtube: hhttps://www.youtube.com/watch?v=7LNKYIIO-0M |
| 10 | +published: true |
| 11 | +featured: false |
| 12 | +--- |
| 13 | + |
| 14 | +In this episode, we will explore Vector databases, a cutting-edge technology revolutionizing data storage and retrieval. We'll examine how these systems efficiently handle high-dimensional data, enabling advanced search capabilities and powering modern AI applications across various industries. |
| 15 | + |
| 16 | +## Guests |
| 17 | + |
| 18 | +- [Oumayma Essarhi](https://twitter.com/oumayma_es_) |
| 19 | +- [Taha Bouhsine](https://twitter.com/Tahabsn) |
| 20 | +- [Merouane Zouaid](https://x.com/merouanezouaid) |
| 21 | + |
| 22 | + |
| 23 | +## Notes |
| 24 | + |
| 25 | +0:00:00 - Introduction and welcoming |
| 26 | + |
| 27 | +0:03:47 - What is a Vector database? |
| 28 | + |
| 29 | +0:13:47 - Why using a Vector Database, and what are the differences with classic Databases? |
| 30 | + |
| 31 | +0:19:15 - Techniques used to speed search with Vector Databases |
| 32 | + |
| 33 | +0:31:58 - Vector databases and AI |
| 34 | + |
| 35 | +0:46:30 - Dealing with "curse of dimensionality" in Vector Databases |
| 36 | + |
| 37 | +0:55:02 - Privacy concerns while using Vector databases |
| 38 | + |
| 39 | +1:03:56 - Scalability of Vector Databases |
| 40 | + |
| 41 | +1:12:54 - Q&A and Giveaay |
| 42 | + |
| 43 | +1:27:00 - Conclusion |
| 44 | + |
| 45 | +## Links |
| 46 | + |
| 47 | +- [Extracting Training Data from Large Language Models](https://arxiv.org/pdf/2012.07805) |
| 48 | +- [30 days of ML](https://30daysofml.framer.ai/) |
| 49 | +- [Strategy of pickinga Vector database](https://www.reddit.com/r/LangChain/comments/170jigz/my_strategy_for_picking_a_vector_database_a/ ) |
| 50 | +- [PGrounding AI in reality with a little help from Data Commons](https://research.google/blog/grounding-ai-in-reality-with-a-little-help-from-data-commons/?linkId=10989162) |
| 51 | +- [DataGemma](https://ai.google.dev/gemma/docs/datagemma) |
| 52 | +- [Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://arxiv.org/pdf/2208.06366) |
| 53 | +- [An Any-to-Any Vision Model for Tens of Tasks and Modalities](https://arxiv.org/abs/2406.09406) |
| 54 | + |
| 55 | + |
| 56 | + |
| 57 | +## Prepared and Presented by |
| 58 | + |
| 59 | +- [Meriem Zaid](https://www.linkedin.com/in/meriem-zaid-652852187/) |
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