Vector Podcast

This lightning session introduces a new idea in vector search - Wormhole vectors! It has deep roots in physics and allows for transcending spaces of any nature: sparse, vector and behaviour (but could theoretically be any N-dimensional space). Blog post on Medium: https://dmitry-kan.medium.com/novel-idea-in-vector-search-wormhole-vectors-6093910593b8 Session page on maven: https://maven.com/p/8c7de9/beyond-hybrid-search-with-wormhole-vectors?utm_campaign=NzI2NzIx&utm_medium=ll_share_link&utm_source=instructor To try the managed OpenSearch (multi-cloud, automatic backups, disaster recovery, vector search and more), go here: https://console.aiven.io/signup?utm_source=youtube&utm_medium&&utm_content=vectorpodcast Get credits to use Aiven's products (PG, Kafka, Valkey, OpenSearch, ClickHouse): https://aiven.io/startups Timecodes: 00:00 Intro by Dmitry 01:48 Trey's presentation 03:05 Walk to the AI-Powered Search course by Trey and Doug 07:07 Intro to vector spaces and embeddings 19:03 Disjoint vector spaces and the need of hybrid search 23:11 Different modes of search 24:49 Wormhole vectors 47:49 Q&A What you'll learn: - What are "Wormhole Vectors"? Learn how wormhole vectors work & how to use them to traverse between disparate vector spaces for better hybrid search. - Building a behavioral vector space from click stream data Learn to generate behavioral embeddings to be integrated with dense/semantic and sparse/lexical vector queries. - Traverse lexical, semantic, & behavioral vectors spaces Jump back and forth between multiple dense and sparse vector spaces in the same query - Advanced hybrid search techniques (beyond fusion algorithms) Hybrid search is more than mixing lexical + semantic search. See advanced techniques and where wormhole vectors fit in.

Turbopuffer search engine supports such products as Cursor, Notion, Linear, Superhuman and Readwise. This episode on YouTube: https://youtu.be/I8Ztqajighg If you are on Lucene / OpenSearch stack, you can go managed by signing up here: https://console.aiven.io/signup?utm_source=youtube&utm_medium=&&utm_content=vectorpodcast Time codes: 00:00 Intro 00:15 Napkin Problem 4: Throughput of Redis 01:35 Episode intro 02:45 Simon's background, including implementation of Turbopuffer 09:23 How Cursor became an early client 11:25 How to test pre-launch 14:38 Why a new vector DB deserves to exist? 20:39 Latency aspect 26:27 Implementation language for Turbopuffer 28:11 Impact of LLM coding tools on programmer craft 30:02 Engineer 2 CEO transition 35:10 Architecture of Turbopuffer 43:25 Disk vs S3 latency, NVMe disks, DRAM 48:27 Multitenancy 50:29 Recall@N benchmarking 59:38 filtered ANN and Big-ANN Benchmarks 1:00:54 What users care about more (than Recall@N benchmarking) 1:01:28 Spicy question about benchmarking in competition 1:06:01 Interesting challenges ahead to tackle 1:10:13 Simon's announcement Show notes: - Turbopuffer in Cursor: https://www.youtube.com/watch?v=oFfVt3S51T4&t=5223s transcript: https://lexfridman.com/cursor-team-transcript - https://turbopuffer.com/ - Napkin Math: https://sirupsen.com/napkin - Follow Simon on X: https://x.com/Sirupsen - Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696/

Vector Podcast website: https://vectorpodcast.com Haystack US 2025: https://haystackconf.com/2025/ Federated search, Keyword & Neural Search, ML Optimisation, Pros and Cons of Hybrid search It is fascinating and funny how things develop, but also turn around. In 2022-23 everyone was buzzing about hybrid search. In 2024 the conversation shifted to RAG, RAG, RAG. And now we are in 2025 and back to hybrid search - on a different level: finally there are strides and contributions towards making hybrid search parameters learnt with ML. How cool is that? Design: Saurabh Rai, https://www.linkedin.com/in/srbhr/ The design of this episode is inspired by a scene in Blade Runner 2049. There's a clear path leading towards where people want to go to, yet they're searching for something. 00:00 Intro 00:54 Eric's intro and Daniel's background 02:50 Importance of Hybrid search: Daniel's take 07:26 Eric's take 10:57 Dmitry's take 11:41 Eric's predictions 13:47 Doug's blog on RRF is not enough 16:18 How to not fall short of the blind picking in RRF: score normalization, combinations and weights 25:03 The role of query understanding: feature groups 35:11 Lesson 1 from Daniel: Simple models might be all you need 36:30 Lesson 2: query features might be all you need 38:30 Reasoning capabilities in search 40:02 Question from Eric: how is this different from Learning To Rank? 42:46 Carrying the past in Learning To Rank / any rank 44:21 Demo! 51:52 How to consume this in OpenSearch 55:15 What's next 58:44 Haystack US 2025

https://www.vectorpodcast.com/ I had fun interacting with NotebookLM - mostly for self-educational purposes. I think this tool can help by bringing an additional perspective over a textual content. It ties to what RAG (Retrieval Augmented Generation) can do to content generation in another modality. In this case, text is used to augment the generation of a podcast episode. This episode is based on my blog post: https://dmitry-kan.medium.com/the-rise-fall-and-future-of-vector-databases-how-to-pick-the-one-that-lasts-6b9fbb43bbbe Time codes: 00:00 Intro to the topic 1:11 Dmitry's knowledge in the space 1:54 Unpacking the Rise & Fall idea 3:14 How attention got back to Vector DBs for a bit 4:18 Getting practical: Dmitry's guide for choosing the right Vector Database 4:39 FAISS 5:34 What if you need fine-grained keyword search? Look at Apache Lucene-based engines 6:41 Exception to the rule: Late-interaction models 8:30 Latency and QPS: GSI APU, Vespa, Hyperspace 9:28 Strategic approach 9:55 Cloud solutions: CosmosDB, Vertex AI, Pinecone, Weaviate Cloud 10:14 Community voice: pgvector 10:48 Picture of the fascinating future of the field 12:23 Question to the audience 12:44 Taking a step back: key points 13:45 Don't get caught up in trendy shiny new tech

Vector Podcast website: https://vectorpodcast.com Get your copy of John's new book "Prompt Engineering for LLMs: The Art and Science of Building Large Language Model–Based Applications": https://amzn.to/4fMj2Ef John Berryman is the founder and principal consultant of Arcturus Labs, where he specializes in AI application development (Agency and RAG). As an early engineer on GitHub Copilot, John contributed to the development of its completions and chat functionalities, working at the forefront of AI-assisted coding tools. John is coauthor of "Prompt Engineering for LLMs" (O'Reilly).Before his work on Copilot, John's focus was search technology. His diverse experience includes helping to develop next-generation search system for the US Patent Office, building search and recommendations for Eventbrite, and contributing to GitHub's code search infrastructure. John is also coauthor of "Relevant Search" (Manning), a book that distills his expertise in the field.John's unique background, spanning both cutting-edge AI applications and foundational search technologies, positions him at the forefront of innovation in LLM applications and information retrieval. 00:00 Intro 02:19 John's background and story in search and ML 06:03 Is RAG just a prompt engineering technique? 10:15 John's progression from a search engineer to ML researcher 13:40 LLM predictability vs more traditional programming 22:31 Code assist with GitHub Copilot 29:44 Role of keyword search for code at GitHub 35:01 GenAI: existential risk or pure magic? AI Natives 39:40 What are Artifacts 46:59 Demo! 55:13 Typed artifacts, tools, accordion artifacts 56:21 From Web 2.0 to Idea exchange 57:51 Spam will transform into Slop 58:56 John's new book and Acturus Labs intro Show notes: - John Berryman on X: https://x.com/JnBrymn - Acturus Labs: https://arcturus-labs.com/ - John's blog on Artifacts (see demo in the episode): https://arcturus-labs.com/blog/2024/11/11/cut-the-chit-chat-with-artifacts/ Watch on https://youtu.be/60HAtHVBYj8

00:00 Intro 01:31 Leo's story 09:59 SPLADE: single model to solve both dense and sparse? 21:06 DeepImpact 29:58 NMSLIB: what are non-metric spaces 34:21 How HNSW and NMSLIB joined forces 41:11 Why FAISS did not choose NMSLIB's algorithm 43:36 Serendipity of discovery and the creation of industries 47:06 Vector Search: intellectually rewarding, professionally undervalued 52:37 Why RDBMS Still Struggles with Scalable Vector and Free-Text Search 1:00:16 Leo's recent favorite papers https://www.linkedin.com/in/leonidboytsov/https://x.com/srchvrshttps://scholar.google.com/citations?hl=en&user=I79y2i4AAAAJ&view_op=list_works&sortby=pubdate Lots of papers and other material from Leo: https://www.youtube.com/watch?v=gzWErcOXIKk

Alessandro's talk on Hybrid Search with Apache Solr Reciprocal Rank Fusion: https://www.youtube.com/watch?v=8x2cbT5CCEM&list=PLq-odUc2x7i8jHpa6PHGzmxfAPEz-c-on&index=5 00:00 Intro 00:50 Alessandro's take on the bbuzz'24 conference 01:25 What and value of hybrid search 04:55 Explainability of vector search results to users 09:27 Explainability of vector search results to search engineers 13:12 State of hybrid search in Apache Solr 14:32 What's in Reciprocal Rank Fusion beyond round-robin? 18:30 Open source for LLMs 22:48 How we should approach this issue in business and research 26:12 How to maintain the status of an open-source LLM / system 30:06 Prompt engineering (hope and determinism) 34:03 DSpy 35:16 What's next in Solr

Video: https://youtu.be/dVIPBxHJ1kQ 00:00 Intro 00:15 Greets for Sonam 01:02 Importance of metric learning 3:37 Sonam's background: Rasa, Qdrant 4:31 What's EmbedAnything 5:52 What a user gets 8:48 Do I need to know Rust? 10:18 Call-out to the community 10:35 Multimodality 12:32 How to evaluate quality of LLM-based systems 16:38 QA for multimodal use cases 18:17 Place for a human in the LLM craze 19:00 Use cases for EmbedAnything 20:54 Closing theme (a longer one - enjoy!) Show notes: - GitHub: https://github.com/StarlightSearch/EmbedAnything - HuggingFace Candle: https://github.com/huggingface/candle - Sonam's talk on Berlin Buzzwords 2024: https://www.youtube.com/watch?v=YfR3kuSo-XQ - Removing GIL from Python: https://peps.python.org/pep-0703 - Blind pairs in CLIP: https://arxiv.org/abs/2401.06209 - Dark matter of intelligence: https://ai.meta.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/ - Rasa chatbots: https://github.com/RasaHQ/rasa - Prometheus: https://github.com/prometheus-eval/prometheus-eval - Dino: https://github.com/facebookresearch/dino

00:00 Intro 00:30 Greets for Doug 01:46 Apache Solr and stuff 03:08 Hello LTR project 04:42 Secret sauce of Doug's continuous blogging 08:50 SearchArray 13:22 Running complex ML experiments 17:29 Efficient search orgs 22:58 Writing a book on search and AI Show notes: - Doug's talk on Learning To Rank at Reddit delivered at the Berlin Buzzwords 2024 conference: https://www.youtube.com/watch?v=gUtF1gyHsSM - Hello LTR: https://github.com/o19s/hello-ltr - Lexical search for pandas with SearchArray: https://github.com/softwaredoug/searcharray - https://softwaredoug.com/ - What AI Engineers Should Know about Search: https://softwaredoug.com/blog/2024/06/25/what-ai-engineers-need-to-know-search - AI Powered Search: https://www.manning.com/books/ai-powered-search - Quepid: https://github.com/o19s/quepid - Branching out in your ML / search experiments: https://dvc.org/doc/use-cases - Doug on Twitter: https://x.com/softwaredoug - Doug on LinkedIn: https://www.linkedin.com/in/softwaredoug/

00:00 Intro 00:21 Guest Introduction: Eric Pugh 03:00 Eric's story in search and the evolution of search technology 7:27 Quepid: Improving Search Relevancy 10:08 When to use Quepid 14:53 Flash back to Apache Solr 1.4 and the book (of which Eric is one author) 17:49 Quepid Demo and Future Enhancements 23:57 Real-Time Query Doc Pairs with WebSockets 24:16 Integrating Quepid with Search Engines 25:57 Introducing LLM-Based Judgments 28:05 Scaling Up Judgments with AI 28:48 Data Science Notebooks in Quepid 33:23 Custom Scoring in Quepid 39:23 API and Developer Tools 42:17 The Future of Search and Personal Reflections Show notes: - Hosted Quepid: https://app.quepid.com/ - Ragas: Evaluation framework for your Retrieval Augmented Generation (RAG) pipelines https://github.com/explodinggradients... - Why Quepid: https://quepid.com/why-quepid/ - Quepid on Github: https://github.com/o19s/quepid

00:00 Intro 01:54 Reflection on the past year in AI 08:08 Reader LLM (and RAG) 12:36 Does it need fine-tuning to a domain? 14:20 How LLMs can lie 17:32 What if data isn't perfect 21:21 SWIRL's secret sauce with Reader LLM 23:55 Feedback loop 26:14 Some surprising client perspective 31:17 How Gen AI can change communication interfaces 34:11 Call-out to the Community

00:00 Intro 00:42 Louis's background 05:39 From Facebook to Rockset 07:41 Embeddings prior to deep learning / LLM era 12:35 What's Rockset as a product 15:27 Use cases 18:04 RocksDB as part of Rockset 20:33 AI capabilities: ANN index, hybrid search 25:11 Types of hybrid search 28:05 Can one learn the alpha? 30:03 Louis's prediction of the future of vector search 33:55 RAG and other AI capabilities 41:46 Call out to the Vector Search community 46:16 Vector Databases vs Databases 49:16 Question of WHY

Topics: 00:00 Intro - how do you like our new design? 00:52 Greets 01:55 Saurabh's background 03:04 Resume Matcher: 4.5K stars, 800 community members, 1.5K forks 04:11 How did you grow the project? 05:42 Target audience and how to use Resume Matcher 09:00 How did you attract so many contributors? 12:47 Architecture aspects 15:10 Cloud or not 16:12 Challenges in maintaining OS projects 17:56 Developer marketing with Swirl AI Connect 21:13 What you (listener) can help with 22:52 What drives you? Show notes: - Resume Matcher: https://github.com/srbhr/Resume-Matcher website: https://resumematcher.fyi/ - Ultimate CV by Martin John Yate: https://www.amazon.com/Ultimate-CV-Cr... - fastembed: https://github.com/qdrant/fastembed - Swirl: https://github.com/swirlai/swirl-search

Topics: 00:00 Intro 00:22 Quick demo of SWIRL on the summary transcript of this episode 01:29 Sid’s background 08:50 Enterprise vs Federated search 17:48 How vector search covers for missing folksonomy in enterprise data 26:07 Relevancy from vector search standpoint 31:58 How ChatGPT improves programmer’s productivity 32:57 Demo! 45:23 Google PSE 53:10 Ideal user of SWIRL 57:22 Where SWIRL sits architecturally 1:01:46 How to evolve SWIRL with domain expertise 1:04:59 Reasons to go open source 1:10:54 How SWIRL and Sid interact with ChatGPT 1:23:22 The magical question of WHY 1:27:58 Sid’s announcements to the community YouTube version: https://www.youtube.com/watch?v=vhQ5LM5pK_Y Design by Saurabh Rai: https://twitter.com/_srbhr_ Check out his Resume Matcher project: https://www.resumematcher.fyi/

Topics: 00:00 Intro 02:20 Atita’s path into search engineering 09:00 When it’s time to contribute to open source 12:08 Taking management role vs software development 14:36 Knowing what you like (and coming up with a Solr course) 19:16 Read the source code (and cook) 23:32 Open Bistro Innovations Lab and moving to Germany 26:04 Affinity to Search world and working as a Search Relevance Consultant 28:39 Bringing vector search to Chorus and Querqy 34:09 What Atita learnt from Eric Pugh’s approach to improving Quepid 36:53 Making vector search with Solr & Elasticsearch accessible through tooling and documentation 41:09 Demystifying data embedding for clients (and for Java based search engines) 43:10 Shifting away from generic to domain-specific in search+vector saga 46:06 Hybrid search: where it will be useful to combine keyword with semantic search 50:53 Choosing between new vector DBs and “old” keyword engines 58:35 Women of Search 1:14:03 Important (and friendly) People of Open Source 1:22:38 Reinforcement learning applied to our careers 1:26:57 The magical question of WHY 1:29:26 Announcements See show notes on YouTube: https://www.youtube.com/watch?v=BVM6TUSfn3E

Topics: 00:00 Intro 01:54 Things Connor learnt in the past year that changed his perception of Vector Search 02:42 Is search becoming conversational? 05:46 Connor asks Dmitry: How Large Language Models will change Search? 08:39 Vector Search Pyramid 09:53 Large models, data, Form vs Meaning and octopus underneath the ocean 13:25 Examples of getting help from ChatGPT and how it compares to web search today 18:32 Classical search engines with URLs for verification vs ChatGPT-style answers 20:15 Hybrid search: keywords + semantic retrieval 23:12 Connor asks Dmitry about his experience with sparse retrieval 28:08 SPLADE vectors 34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search 39:54 Ways to debug a query case in dense retrieval (spoiler: it is a challenge!) 44:47 Intricacies of teaching ML models to understand your data and re-vectorization 49:23 Local IDF vs global IDF and how dense search can approach this issue 54:00 Realtime index 59:01 Natural language to SQL 1:04:47 Turning text into a causal DAG 1:10:41 Engineering and Research as two highly intelligent disciplines 1:18:34 Podcast search 1:25:24 Ref2Vec for recommender systems 1:29:48 Announcements For Show Notes, please check out the YouTube episode below. This episode on YouTube: https://www.youtube.com/watch?v=2Q-7taLZ374 Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Toloka’s support for Academia: grants and educator partnerships https://toloka.ai/collaboration-with-educators-form https://toloka.ai/research-grants-form These are pages leading to them: https://toloka.ai/academy/education-partnerships https://toloka.ai/grants Topics: 00:00 Intro 01:25 Jenny’s path from graduating in ML to a Data Advocate role 07:50 What goes into the labeling process with Toloka 11:27 How to prepare data for labeling and design tasks 16:01 Jenny’s take on why Relevancy needs more data in addition to clicks in Search 18:23 Dmitry plays the Devil’s Advocate for a moment 22:41 Implicit signals vs user behavior and offline A/B testing 26:54 Dmitry goes back to advocating for good search practices 27:42 Flower search as a concrete example of labeling for relevancy 39:12 NDCG, ERR as ranking quality metrics 44:27 Cross-annotator agreement, perfect list for NDCG and Aggregations 47:17 On measuring and ensuring the quality of annotators with honeypots 54:48 Deep-dive into aggregations 59:55 Bias in data, SERP, labeling and A/B tests 1:16:10 Is unbiased data attainable? 1:23:20 Announcements This episode on YouTube: https://youtu.be/Xsw9vPFqGf4 Podcast design: Saurabh Rai: https://twitter.com/srvbhr

00:00 Introduction 01:11 Yaniv’s background and intro to Searchium & GSI 04:12 Ways to consume the APU acceleration for vector search 05:39 Power consumption dimension in vector search 7:40 Place of the platform in terms of applications, use cases and developer experience 12:06 Advantages of APU Vector Search Plugins for Elasticsearch and OpenSearch compared to their own implementations 17:54 Everyone needs to save: the economic profile of the APU solution 20:51 Features and ANN algorithms in the solution 24:23 Consumers most interested in dedicated hardware for vector search vs SaaS 27:08 Vector Database or a relevance oriented application? 33:51 Where to go with vector search? 42:38 How Vector Search fits into Search 48:58 Role of the human in the AI loop 58:05 The missing bit in the AI/ML/Search space 1:06:37 Magical WHY question 1:09:54 Announcements - Searchium vector search: https://searchium.ai/ - Dr. Avidan Akerib, founder behind the APU technology: https://www.linkedin.com/in/avidan-akerib-phd-bbb35b12/ - OpenSearch benchmark for performance tuning: https://betterprogramming.pub/tired-of-troubleshooting-idle-search-resources-use-opensearch-benchmark-for-performance-tuning-d4277c9f724 - APU KNN plugin for OpenSearch: https://towardsdatascience.com/bolster-opensearch-performance-with-5-simple-steps-ca7d21234f6b - Multilingual and Multimodal Search with Hardware Acceleration: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78 - Muves talk at Berlin Buzzwords, where we have utilized GSI APU: https://blog.muves.io/muves-at-berlin-buzzwords-2022-3150eef01c4 - Not All Vector Databases are made equal: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696 Episode on YouTube: https://youtu.be/EerdWRPuqd4 Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Topics: 00:00 Intro 01:30 Doug’s story in Search 04:55 How Quepid came about 10:57 Relevance as product at Shopify: challenge, process, tools, evaluation 15:36 Search abandonment in Ecommerce 21:30 Rigor in A/B testing 23:53 Turn user intent and content meaning into tokens, not words into tokens 32:11 Use case for vector search in Maps. What about search in other domains? 38:05 Expanding on dense approaches 40:52 Sparse, dense, hybrid anyone? 48:18 Role of HNSW, scalability and new vector databases vs Elasticsearch / Solr dense search 52:12 Doug’s advice to vector database makers 58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset 1:12:10 Blending search and recommendation 1:16:08 Search engineer role and key ingredients of managing search projects today 1:20:34 What does a Product Manager do on a Search team? 1:26:50 The magical question of WHY 1:29:08 Doug’s announcements Show notes: Doug’s course: https://www.getsphere.com/ml-engineering/ml-powered-search?source=Instructor-Other-070922-vector-pod Upcoming book: https://www.manning.com/books/ai-powered-search?aaid=1&abid=e47ada24&chan=aips Doug’s post in Shopify’s blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopify Doug’s own blog: https://softwaredoug.com/ Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1s Hello LTR: https://github.com/o19s/hello-ltr Vector Databases: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696 Research: Search abandonment has a lasting impact on brand loyalty: https://cloud.google.com/blog/topics/retail/search-abandonment-impacts-retail-sales-brand-loyalty Quepid: https://quepid.com/ Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

Topics: 00:00 Introduction 01:12 Malte’s background 07:58 NLP crossing paths with Search 11:20 Product discovery: early stage repetitive use cases pre-dating Haystack 16:25 Acyclic directed graph for modeling a complex search pipeline 18:22 Early integrations with Vector Databases 20:09 Aha!-use case in Haystack 23:23 Capabilities of Haystack today 30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders 39:00 Examples of value for the end-users of Deepset Cloud 46:00 Success metrics 50:35 Where Haystack is taking us beyond MLOps for search experimentation 57:13 Haystack as a smart assistant to guide experiments 1:02:49 Multimodality 1:05:53 Future of the Vector Search / NLP field: large language models 1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic 1:16:25 The magical question of WHY 1:23:47 Announcements from Malte Show notes: - Haystack: https://github.com/deepset-ai/haystack/ - Deepset Cloud: https://www.deepset.ai/deepset-cloud - Tutorial: Build Your First QA System: https://haystack.deepset.ai/tutorials/v0.5.0/first-qa-system - Open NLP Meetup on Sep 29th (Nils Reimers talking about “Incorporating New Knowledge Into LMs”): https://www.meetup.com/open-nlp-meetup/events/287159377/ - Atlas Paper (Few shot learning with retrieval augmented large language models): https://arxiv.org/abs/2208.03299 - Tweet from Patrick Lewis: https://twitter.com/PSH_Lewis/status/1556642671569125378 - Zero click search: https://www.searchmetrics.com/glossary/zero-click-searches/ Very large LMs: - 540B PaLM by Google: https://lnkd.in/eajsjCMr - 11B Atlas by Meta: https://lnkd.in/eENzNkrG - 20B AlexaTM by Amazon: https://lnkd.in/eyBaZDTy - Players in Vector Search: https://www.youtube.com/watch?v=8IOpgmXf5r8 https://dmitry-kan.medium.com/players-in-vector-search-video-2fd390d00d6 - Click Residual: A Query Success Metric: https://observer.wunderwood.org/2022/08/08/click-residual-a-query-success-metric/ - Tutorials and papers around incorporating Knowledge into Language Models: https://cs.stanford.edu/people/cgzhu/ Podcast design: Saurabh Rai https://twitter.com/srvbhr