Weaviate Podcast #23. Thank you so much for watching the 23rd episode of the Weaviate Podcast! This episode dives into a new technique for Self-Supervised retrieval in NLP with some incredible results!

Weaviate Podcast #22. Thank you so much for watching the 22nd Weaviate Podcast with Yaoshiang Ho! Yaoshiang is a Co-Founder of Masterful AI, a company doing incredible work in the Computer Vision model training and deployment space (https://www.masterfulai.com/). I really hope you enjoy this podcast, Yaoshiang and I went deep into some of the cutting edge Computer Vision algorithms such as Noisy Student, SimCLR, and Barlow Twins -- as well as the broader topic of Semi-Supervised Learning in which we have a small labeled dataset and a large unlabelled dataset. I am so excited about model training tools like Masterful and the integration with the Weaviate Vector Search Indexes and other Database features! More than happy to answer any questions/host any discussion on topics mentioned in the podcast! Please hit the like and subscribe to support more content like this!

Weaviate Podcast #21. Thank you for watching the 21st Weaviate Podcast with Laura Ham! Laura Ham has worked on Weaviate at SeMI Technologies for a little over 5 years. She has had a heavy influence on all things from the GraphQL User Experience design to the Graph data model, and the creation of educational content! I really enjoyed this podcast, please see the list of topics under “chapters”! Here are some examples of recent coding tutorial videos Laura has made on “How to add custom modules to Weaviate” and integrations of Weaviate with Jina AI and Haystack.

Weaviate Podcast #20. Tuana Celik, a Developer Advocate at Deepset, presented many exciting ideas around Question Answering! We began with her Game of Thrones Question Answering Demo on HuggingFace Spaces and continued to discuss all topics QA from Extractive to Abstractive, benefits of Retrieve-then-Read, and Zero-Shot Generalization, to give a quick preview. For our Weaviate users, please check out this demo from Laura Ham on how to use Haystack QA in tandem with the Weaviate Vector Search Database: https://www.youtube.com/watch?v=Bkoza.... I really hope you enjoy this podcast, please don't forget to check the Chapters to see if any topics appeal particularly to what you are working on! Please also see the links below with referenced content in the podcast!

Weaviate Podcast #19. SeMI Technologies Co-Founder and CTO Etienne Dilocker returns to the Weaviate podcast to describe what's new with Weaviate v1.14! Please see the chapter outlines if you would like to skip ahead to the update most relevant to you! Please also see this blog post lead by Sebastian Witalec describing the new changes to Weaviate!  Weaviate v1.14 Blog Post: https://weaviate.io/blog/2022/07/Weav...

Weaviate Podcast #18. Thank you for watching the 18th Weaviate Podcast with Vincent D. Warmerdam! Vincent is an engineer at Spacy working on exciting tools such as Prodigy! Vincent describes how nearest neighbor search can aid in tasks such as Data De-Duplication and Data Labeling! Vincent shared many interesting ideas from representations of text, challenges with annotator disagreement, lessons from hosting data labeling workshops to train data scientists, and many more!

Weaviate Podcast #17. Thank you for watching the 17th Weaviate Podcast with Kyle Lo! Vector Search enables us to find semantically similar items in large collections. Scientific Literature Mining is an extremely interesting case of this where we search through enormous collections of scientific papers to find evidence and ideas. Kyle has an extremely impressive resume in this application domain, tackling tasks such as Question Answering, Summarization, Fact Verification, and more! We really hope you enjoy the lessons Kyle describes from building these systems. Further, we hope that this inspires excitement in your own Vector Search applications!

Weaviate Podcast #16. ANN Benchmarks are a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. Etienne Dilocker, the CTO of SeMI Technologies, the company behind Weaviate shares some insight knowledge about this interesting topic.

Weaviate Podcast #15. Weaviate is used as a database for Jina AI's Neural Search Framework. In this podcast, Maximilian Werk, Engineering Director at Jina AI, will talk about all things related to this neural search framework together with Connor Shorten. Also, Maximilian will give a Jina Example Walkthrough... Enjoy!!

Weaviate Podcast #14. Thanks for watching the Weaviate podcast! Our 14th episode welcomes Yi-Lin Sung, Jaemin Cho, and Professor Mohit Bansal, a research team from UNC! Our guests present their work on VL Adapter, a technique to achieve full fine-tuning performance while only updating 4% of original parameters!! This is an incredibly interesting finding for the sake of cost-effective tuning of Vision and Language models based on CLIP. We additionally discussed topics around compression bottlenecks in neural architectures, V&L datasets, and the tricky question of compositional generalization. If you are curious about using CLIP in Weaviate, please check out this text-to-image search example with Unsplash images and a React frontend!