Weaviate Podcast Weaviate

Hey everyone! Thank you so much for watching the 101st episode of the Weaviate Podcast with Devin Petersohn! Devin is the creator of Modin, one of the world's most advanced systems for scaling Pandas! Devin then went onto co-found Ponder, which was acquired by Snowflake in early 2023. This was one of my favorite podcasts of all time, I learned so much about the internals of Data Systems and I hope you do as well!

What is an AI-native application? This has been one of the questions we are most interested in answering at Weaviate! This podcast explores this question with Weaviate Co-founder Bob van Luijt and Lucas Negritto. Formerly at OpenAI, Lucas is now building Odapt, a remarkable example of such an application where we no longer use front-end code, rather rendering the UI entirely within the generative model!! There are many interesting topics covered such as of course, firstly how this works and how you build these systems, as well as native multimodality, subjective feedback, and more! I hope you find the podcast interesting and useful!

Liana Patel is a Ph.D. student at Stanford University who is the lead author of ACORN, a breakthrough in Approximate Nearest Neighbor Search with Filters! Also joining the podcast is Abdel Rodriguez, a Vector Index Researcher and Engineer at Weaviate. This podcast dives into all sorts of details behind ACORN. Starting with how Liana developed her interest in Approximate Nearest Neighbor Search algorithms and then transitioning into how ACORN differs from previous approaches, the Two-Hop Neighborhood Heuristic, Predicate Subgraphs, Experimental Details, and many more topics! Major thank you to Liana and Abdel for joining the podcast, this was such a fun conversation packed with insights about Proximity Graph algorithms for Vector Search with Filtering!

Josh Engels is a Ph.D. student at MIT who has published several works advancing the state of the art in Vector Search. Josh has recently developed the Window Search Tree, a new algorithm particularly targeted for improving Filtered Vector Search. Even more particularly than that, the WST algorithm targets Filtered Search with continuous-valued filters such as "price" or "date", also known as range filters. This is a huge application for Vector Databases and it was incredible getting to pick Josh's brain on how this works and the state of Approximate Nearest Neighbor Search!

Hey everyone! I am SUPER excited to publish our 97th Weaviate Podcast on the state of AI-powered Search technology featuring Nils Reimers and Erika Cardenas! Erika and I have been super excited about Cohere's latest works to advance RAG and Search and it was amazing getting to pick Nils' brain about all these topics!



We began with the development of Compass! Nils explains the current problem with embeddings as a soup!! For example, imagine embedding this video description, the first part is about the launch of a podcast, whereas this part is about an embedding algorithm -- how do we form representations of multi-aspect chunks of text?



We dove into all the details of this from the distinction of multi-aspect embeddings with LLM or "smart" chunkers, ColBERT, "Embed Small, Retrieve Big", and many other topics as well from Cross Encoder Re-rankers to Data Cleaning with Generative Feedback Loops, RAG Evaluation, Vector Quantization, and more!



I really hope you enjoy the podcast! It was such an educational experience for Erika and I and we really hope you enjoy it as well!

Hey everyone! Thank you so much for watching the 96th episode of the Weaviate podcast featuring Letitia Parcalabescu! While completing her Ph.D. studies at the University of Heidelberg, Letitia started her YouTube channel: AI Coffee Break with Letitia! Her videos break down complex concepts in AI with a creative mix of technical expertise and visualizations unlike anyone else in the space!We began the podcast by discussing our shared background in creating content on YouTube from starting, to plans for the future, and everything else in between!We then discussed the evolution of Deep Learning over the last few years -- from neural network architectures to datasets, tasks, learning algorithms, and more! I think particularly we are at a really interesting time in the future of learning algorithms! We discussed DSPy and new ways of thinking about instruction tuning, example production, gradient descent, and the future of SFT vs. DPO-style techniques!