Vector Podcast

Topics:00:00 Intro00:22 Quick demo of SWIRL on the summary transcript of this episode01:29 Sid's background08:50 Enterprise vs Federated search17:48 How vector search covers for missing folksonomy in enterprise data26:07 Relevancy from vector search standpoint31:58 How ChatGPT improves programmer's productivity32:57 Demo!45:23 Google PSE53:10 Ideal user of SWIRL57:22 Where SWIRL sits architecturally1:01:46 How to evolve SWIRL with domain expertise1:04:59 Reasons to go open source1:10:54 How SWIRL and Sid interact with ChatGPT1:23:22 The magical question of WHY1:27:58 Sid's announcements to the communityYouTube version: https://www.youtube.com/watch?v=vhQ5LM5pK_YDesign by Saurabh Rai: https://twitter.com/_srbhr_ Check out his Resume Matcher project: https://www.resumematcher.fyi/

Topics:00:00 Intro02:20 Atita's path into search engineering09:00 When it's time to contribute to open source12:08 Taking management role vs software development14:36 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 Germany26:04 Affinity to Search world and working as a Search Relevance Consultant28:39 Bringing vector search to Chorus and Querqy34:09 What Atita learnt from Eric Pugh's approach to improving Quepid36:53 Making vector search with Solr & Elasticsearch accessible through tooling and documentation41:09 Demystifying data embedding for clients (and for Java based search engines)43:10 Shifting away from generic to domain-specific in search+vector saga46:06 Hybrid search: where it will be useful to combine keyword with semantic search50:53 Choosing between new vector DBs and “old” keyword engines58:35 Women of Search1:14:03 Important (and friendly) People of Open Source1:22:38 Reinforcement learning applied to our careers1:26:57 The magical question of WHY1:29:26 AnnouncementsSee show notes on YouTube: https://www.youtube.com/watch?v=BVM6TUSfn3E

Topics:00:00 Intro01:54 Things Connor learnt in the past year that changed his perception of Vector Search02:42 Is search becoming conversational?05:46 Connor asks Dmitry: How Large Language Models will change Search?08:39 Vector Search Pyramid09:53 Large models, data, Form vs Meaning and octopus underneath the ocean13:25 Examples of getting help from ChatGPT and how it compares to web search today18:32 Classical search engines with URLs for verification vs ChatGPT-style answers20:15 Hybrid search: keywords + semantic retrieval23:12 Connor asks Dmitry about his experience with sparse retrieval28:08 SPLADE vectors34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search39:54 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-vectorization49:23 Local IDF vs global IDF and how dense search can approach this issue54:00 Realtime index59:01 Natural language to SQL1:04:47 Turning text into a causal DAG1:10:41 Engineering and Research as two highly intelligent disciplines1:18:34 Podcast search1:25:24 Ref2Vec for recommender systems1:29:48 AnnouncementsFor Show Notes, please check out the YouTube episode below.This episode on YouTube: https://www.youtube.com/watch?v=2Q-7taLZ374Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Toloka's support for Academia: grants and educator partnershipshttps://toloka.ai/collaboration-with-educators-formhttps://toloka.ai/research-grants-formThese are pages leading to them:https://toloka.ai/academy/education-partnershipshttps://toloka.ai/grantsTopics:00:00 Intro01:25 Jenny's path from graduating in ML to a Data Advocate role07:50 What goes into the labeling process with Toloka11:27 How to prepare data for labeling and design tasks16:01 Jenny's take on why Relevancy needs more data in addition to clicks in Search18:23 Dmitry plays the Devil's Advocate for a moment22:41 Implicit signals vs user behavior and offline A/B testing26:54 Dmitry goes back to advocating for good search practices27:42 Flower search as a concrete example of labeling for relevancy39:12 NDCG, ERR as ranking quality metrics44:27 Cross-annotator agreement, perfect list for NDCG and Aggregations47:17 On measuring and ensuring the quality of annotators with honeypots54:48 Deep-dive into aggregations59:55 Bias in data, SERP, labeling and A/B tests1:16:10 Is unbiased data attainable?1:23:20 AnnouncementsThis episode on YouTube: https://youtu.be/Xsw9vPFqGf4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

00:00 Introduction01:11 Yaniv's background and intro to Searchium & GSI04:12 Ways to consume the APU acceleration for vector search05:39 Power consumption dimension in vector search 7:40 Place of the platform in terms of applications, use cases and developer experience12:06 Advantages of APU Vector Search Plugins for Elasticsearch and OpenSearch compared to their own implementations17:54 Everyone needs to save: the economic profile of the APU solution20:51 Features and ANN algorithms in the solution24:23 Consumers most interested in dedicated hardware for vector search vs SaaS27:08 Vector Database or a relevance oriented application?33:51 Where to go with vector search?42:38 How Vector Search fits into Search48:58 Role of the human in the AI loop58:05 The missing bit in the AI/ML/Search space1:06:37 Magical WHY question1:09:54 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-9c65a3bd0696Episode on YouTube: https://youtu.be/EerdWRPuqd4Podcast design: Saurabh Rai: https://twitter.com/srvbhr

Topics:00:00 Intro01:30 Doug's story in Search04:55 How Quepid came about10:57 Relevance as product at Shopify: challenge, process, tools, evaluation15:36 Search abandonment in Ecommerce21:30 Rigor in A/B testing23:53 Turn user intent and content meaning into tokens, not words into tokens32:11 Use case for vector search in Maps. What about search in other domains?38:05 Expanding on dense approaches40:52 Sparse, dense, hybrid anyone?48:18 Role of HNSW, scalability and new vector databases vs Elasticsearch / Solr dense search52:12 Doug's advice to vector database makers58:19 Learning to Rank: how to start, how to collect data with active learning, what are the ML methods and a mindset1:12:10 Blending search and recommendation1:16:08 Search engineer role and key ingredients of managing search projects today1:20:34 What does a Product Manager do on a Search team?1:26:50 The magical question of WHY1:29:08 Doug's announcementsShow notes:Doug's course: https://www.getsphere.com/ml-engineering/ml-powered-search?source=Instructor-Other-070922-vector-podUpcoming book: https://www.manning.com/books/ai-powered-search?aaid=1&abid=e47ada24&chan=aipsDoug's post in Shopify's blog “Search at Shopify—Range in Data and Engineering is the Future”: https://shopify.engineering/search-at-shopifyDoug's own blog: https://softwaredoug.com/Using Bayesian optimization for Elasticsearch relevance: https://www.youtube.com/watch?v=yDcYi-ANJwE&t=1sHello LTR: https://github.com/o19s/hello-ltrVector Databases: https://towardsdatascience.com/milvus-pinecone-vespa-weaviate-vald-gsi-what-unites-these-buzz-words-and-what-makes-each-9c65a3bd0696Research: Search abandonment has a lasting impact on brand loyalty: https://cloud.google.com/blog/topics/retail/search-abandonment-impacts-retail-sales-brand-loyaltyQuepid: https://quepid.com/Podcast design: Saurabh Rai [https://twitter.com/srvbhr]

Topics:00:00 Introduction01:12 Malte's background07:58 NLP crossing paths with Search11:20 Product discovery: early stage repetitive use cases pre-dating Haystack16:25 Acyclic directed graph for modeling a complex search pipeline18:22 Early integrations with Vector Databases20:09 Aha!-use case in Haystack23:23 Capabilities of Haystack today30:11 Deepset Cloud: end-to-end deployment, experiment tracking, observability, evaluation, debugging and communicating with stakeholders39:00 Examples of value for the end-users of Deepset Cloud46:00 Success metrics50:35 Where Haystack is taking us beyond MLOps for search experimentation57:13 Haystack as a smart assistant to guide experiments1:02:49 Multimodality1:05:53 Future of the Vector Search / NLP field: large language models1:15:13 Incorporating knowledge into Language Models & an Open NLP Meetup on this topic1:16:25 The magical question of WHY1:23:47 Announcements from MalteShow 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

00:00 Introduction01:10 Max's deep experience in search and how he transitioned from structured data08:28 Query-term dependence problem and Max's perception of the Vector Search field12:46 Is vector search a solution looking for a problem?20:16 How to move embeddings computation from GPU to CPU and retain GPU latency?27:51 Plug-in neural model into Java? Example with a Hugging Face model33:02 Web-server Mighty and its philosophy35:33 How Mighty compares to in-DB embedding layer, like Weavite or Vespa39:40 The importance of fault-tolerance in search backends43:31 Unit economics of Mighty50:18 Mighty distribution and supported operating systems54:57 The secret sauce behind Mighty's insane fast-ness59:48 What a customer is paying for when buying Mighty1:01:45 How will Max track the usage of Mighty: is it commercial or research use?1:04:39 Role of Open Source Community to grow business1:10:58 Max's vision for Mighty connectors to popular vector databases1:18:09 What tooling is missing beyond Mighty in vector search pipelines1:22:34 Fine-tuning models, metric learning and Max's call for partnerships1:26:37 MLOps perspective of neural pipelines and Mighty's role in it1:30:04 Mighty vs AWS Inferentia vs Hugging Face Infinity1:35:50 What's left in ML for those who are not into Python1:40:50 The philosophical (and magical) question of WHY1:48:15 Announcements from Max25% discount for the first year of using Mighty in your great product / project with promo code VECTOR:https://bit.ly/3QekTWEShow notes:- Max's blog about BERT and search relevance: https://opensourceconnections.com/blog/2019/11/05/understanding-bert-and-search-relevance/- Case study and unit economics of Mighty: https://max.io/blog/encoding-the-federal-register.html- 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-9c65a3bd0696Watch on YouTube: https://youtu.be/LnF4hbl1cE4

Vector Podcast LiveTopics:00:00 Kick-off introducing co:rise study platform03:03 Grant's background04:58 Principle of 3 C's in the life of a CTO: Code, Conferences and Customers07:16 Principle of 3 C's in the Search Engine development: Content, Collaboration and Context11:51 Balance between manual tuning in pursuit to learn and Machine Learning15:42 How to nurture intuition in building search engine algorithms18:51 How to change the approach of organizations to true experimentation23:17 Where should one start in approaching the data (like click logs) for developing a search engine29:36 How to measure the success of your search engine 33:50 The role of manual query rating to improve search result relevancy36:56 What are the available datasets, tools and algorithms, that allow us to build a search engine?41:56 Vector search and its role in broad search engine development and how the profession is shaping up49:01 The magical question of WHY: what motivates Grant to stay in the space52:09 Announcement from Grant: course discount code DGSEARCH1054:55 Questions from the audienceShow notes:- Grant's interview at Berlin Buzzwords 2016: https://www.youtube.com/watch?v=Y13gZM5EGdc- “BM25 is so Yesterday: Modern Techniques for Better Search”: https://www.youtube.com/watch?v=CRZfc9lj7Po- “Taming text” - book co-authored by Grant: https://www.manning.com/books/taming-text- Search Fundamentals course - https://corise.com/course/search-fundamentals- Search with ML course - https://corise.com/course/search-with-machine-learning- Click Models for Web Search: https://github.com/markovi/PyClick- Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing, book by Ron Kohavi et al: https://www.amazon.com/Trustworthy-Online-Controlled-Experiments-Practical-ebook/dp/B0845Y3DJV- Quepid, open source tool and free service for query rating and relevancy tuning: https://quepid.com/- Grant's talk in 2013 where he discussed the need of a vector field in Lucene and Solr: https://www.youtube.com/watch?v=dCCqauwMWFE- CLIP model for multimodal search: https://openai.com/blog/clip/- Demo of multimodal search with CLIP: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78- Learning to Boost: https://www.youtube.com/watch?v=af1dyamySCs- Dmitry's Medium List on Vector Search: https://medium.com/@dmitry-kan/list/vector-search-e9b564d14274

Topics:00:00 Kick-off by Judy Zhu01:33 Introduction by Dmitry Kan and his bio!03:03 Daniel's background04:46 “Science is the difference between instinct and strategy” 07:41 Search as a personal learning experience11:53 Why do we need Machine Learning in Search, or can we use manually curated features?16:47 Swimming up-stream from relevancy: query / content understanding and where to start?23:49 Rule-based vs Machine Learning approaches to Query Understanding: Pareto principle29:05 How content understanding can significantly improve your search engine experience32:02 Available datasets, tools and algorithms to train models for content understanding38:20 Daniel's take on the role of vector search in modern search engine design as the path to language of users45:17 Mystical question of WHY: what drives Daniel in the search space today49:50 Announcements from Daniel51:15 Questions from the audienceShow notes:[What is Content Understanding?. Content understanding is the foundation… | by Daniel Tunkelang | Content Understanding | Medium](https://medium.com/content-understanding/what-is-content-understanding-4da20e925974)[Query Understanding: An Introduction | by Daniel Tunkelang | Query Understanding](https://queryunderstanding.com/introduction-c98740502103)Science as Strategy [YouTube](https://www.youtube.com/watch?v=dftt6Yqgnuw)Search Fundamentals course - https://corise.com/course/search-fundamentalsSearch with ML course - https://corise.com/course/search-with-machine-learningBooks:Faceted Search, by Daniel Tunkelang: https://www.amazon.com/Synthesis-Lectures-Information-Concepts-Retrieval/dp/1598299999Modern Information Retrieval: The Concepts and Technology Behind Search, by Ricardo Baeza-Yates: https://www.amazon.com/Modern-Information-Retrieval-Concepts-Technology/dp/0321416910/ref=sr11?qid=1653144684&refinements=p_27%3ARicardo+Baeza-Yates&s=books&sr=1-1Introduction to Information Retrieval, by Chris Manning: https://www.amazon.com/Introduction-Information-Retrieval-Christopher-Manning/dp/0521865719/ref=sr1fkmr0_1?crid=2GIR19OTZ8QFJ&keywords=chris+manning+information+retrieval&qid=1653144967&s=books&sprefix=chris+manning+information+retrieval%2Cstripbooks-intl-ship%2C141&sr=1-1-fkmr0Query Understanding for Search Engines, by Yi Chang and Hongbo Deng: https://www.amazon.com/Understanding-Search-Engines-Information-Retrieval/dp/3030583333

Topics:00:00 Intro01:03 Yusuf's background03:00 Multimodal search in tech and humans08:53 CLIP: discovering hidden semantics13:02 Where to start to apply metric learning in practice. AutoEncoder architecture included!19:00 Unpacking it further: what is metric learning and the difference with deep metric learning?28:50 How Deep Learning allowed us to transition from pixels to meaning in the images32:05 Increasing efficiency: vector compression and quantization aspects34:25 Yusuf gives a practical use-case with Conversational AI of where metric learning can prove to be useful. And tools!40:59 A few words on how the podcast is made :) Yusuf's explanation of how Gmail smart reply feature works internally51:19 Metric learning helps us learn the best vector representation for the given task52:16 Metric learning shines in data scarce regimes. Positive impact on the planet58:30 Yusuf's motivation to work in the space of vector search, Qdrant, deep learning and metric learning — the question of Why1:05:02 Announcements from Yusuf- Join discussions at Discord: https://discord.qdrant.tech - Yusuf's Medium: https://medium.com/@yusufsarigoz and LinkedIn: https://www.linkedin.com/in/yusufsarigoz/ - GSOC 2022: TensorFlow Similarity - project led by Yusuf: https://docs.google.com/document/d/1fLDLwIhnwDUz3uUV8RyUZiOlmTN9Uzy5ZuvI8iDDFf8/edit#heading=h.zftd93u5hfnp - Dmitry's Twitter: https://twitter.com/DmitryKanFull Show Notes: https://www.youtube.com/watch?v=AU0O_6-EY6s

Topics:00:00 Introduction01:21 Jo Kristian's background in Search / Recommendations since 2001 in Fast Search & Transfer (FAST)03:16 Nice words about Trondheim04:37 Role of NTNU in supplying search talent and having roots in FAST 05:33 History of Vespa from keyword search09:00 Architecture of Vespa and programming language choice: C++ (content layer), Java (HTTP requests and search plugins) and Python (pyvespa)13:45 How Python API enables evaluation of the latest ML models with Vespa and ONNX support17:04 Tensor data structure in Vespa and its use cases22:23 Multi-stage ranking pipeline use cases with Vespa24:37 Optimizing your ranker for top 1. Bonus: cool search course mentioned!30:18 Fascination of Query Understanding, ways to implement and its role in search UX33:34 You need to have investment to get great results in search35:30 Game-changing vector search in Vespa and impact of MS Marco Passage Ranking38:44 User aspect of vector search algorithms43:19 Approximate vs exact nearest neighbor search tradeoffs47:58 Misconceptions in neural search52:06 Ranking competitions, idea generation and BERT bi-encoder dream56:19 Helping wider community through improving search over CORD-19 dataset58:13 Multimodal search is where vector search shines1:01:14 Power of building fully-fledged demos1:04:47 How to combine vector search with sparse search: Reciprocal Rank Fusion1:10:37 The philosophical WHY question: Jo Kristian's drive in the search field1:21:43 Announcement on the coming features from Vespa- Jo Kristian's Twitter: https://twitter.com/jobergum- Dmitry's Twitter: https://twitter.com/DmitryKanFor the Show Notes check: https://www.youtube.com/watch?v=UxEdoXtA9oM

Update: ZIR.AI has relaunched as Vectara: https://vectara.com/Topics:00:00 Intro00:54 Amin's background at Google Research and affinity to NLP and vector search field05:28 Main focus areas of ZIR.AI in neural search07:26 Does the company offer neural network training to clients? Other support provided with ranking and document format conversions08:51 Usage of open source vs developing own tech10:17 The core of ZIR.AI product14:36 API support, communication protocols and P95/P99 SLAs, dedicated pools of encoders17:13 Speeding up single node / single customer throughput and challenge of productionizing off the shelf models, like BERT23:01 Distilling transformer models and why it can be out of reach of smaller companies25:07 Techniques for data augmentation from Amin's and Dmitry's practice (key search team: margin loss)30:03 Vector search algorithms used in ZIR.AI and the need for boolean logic in company's client base33:51 Dynamics of open source in vector search space and cloud players: Google, Amazon, Microsoft36:03 Implementing a multilingual search with BM25 vs neural search and impact on business38:56 Is vector search a hype similar to big data few years ago? Prediction for vector search algorithms influence relations databases43:09 Is there a need to combine BM25 with neural search? Ideas from Amin and features offered in ZIR.AI product51:31 Increasing the robustness of search — or simply making it to work55:10 How will Search Engineer profession change with neural search in the game?Get a $100 discount (first month free) for a 50mb plan, using the code VectorPodcast (no lock-in, you can cancel any time): https://zir-ai.com/signup/user

Topics:00:00 Introduction01:04 Yury's background in laser physics, computer vision and startups05:14 How Yury entered the field of nearest neighbor search and his impression of it09:03 “Not all Small Worlds are Navigable”10:10 Gentle introduction into the theory of Small World Navigable Graphs and related concepts13:55 Further clarification on the input constraints for the NN search algorithm design15:03 What did not work in NSW algorithm and how did Yury set up to invent new algorithm called HNSW24:06 Collaboration with Leo Boytsov on integrating HNSW in nmslib26:01 Differences between HNSW and NSW27:55 Does algorithm always converge?31:56 How FAISS's implementation is different from the original HNSW33:13 Could Yury predict that his algorithm would be implemented in so many frameworks and vector databases in languages like Go and Rust?36:51 How our perception of high-dimensional spaces change compared to 3D?38:30 ANN Benchmarks41:33 Feeling proud of the invention and publication process during 2,5 years!48:10 Yury's effort to maintain HNSW and its GitHub community and the algorithm's design principles53:29 Dmitry's ANN algorithm KANNDI, which uses HNSW as a building block1:02:16 Java / Python Virtual Machines, profiling and benchmarking. “Your analysis of performance contradicts the profiler”1:05:36 What are Yury's hopes and goals for HNSW and role of symbolic filtering in ANN in general1:13:05 The future of ANN field: search inside a neural network, graph ANN1:15:14 Multistage ranking with graph based nearest neighbor search1:18:18 Do we have the “best” ANN algorithm? How ANN algorithms influence each other1:21:27 Yury's plans on publishing his ideas1:23:42 The intriguing question of WhyShow notes:- HNSW library: https://github.com/nmslib/hnswlib/- HNSW paper Malkov, Y. A., & Yashunin, D. A. (2018). Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs. TPAMI, 42(4), 824-836. (arxiv:1603.09320)- NSW paper Malkov, Y., Ponomarenko, A., Logvinov, A., & Krylov, V. (2014). Approximate nearest neighbor algorithm based on navigable small world graphs. Information Systems, 45, 61-68.- Yury Lifshits's paper: https://yury.name/papers/lifshits2009combinatorial.pdf- Sergey Brin's work in nearest neighbour search: GNAT - Geometric Near-neighbour Access Tree: [CiteSeerX — Near neighbor search in large metric spaces](http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.173.8156)- Podcast with Leo Boytsov: https://rare-technologies.com/rrp-4-leo-boytsov-knn-search/- Million-Scale ANN Benchmarks: http://ann-benchmarks.com/- Billion Scale ANN Benchmarks: https://github.com/harsha-simhadri/big-ann-benchmarks- FALCONN algorithm: https://github.com/falconn-lib/falconn- Mentioned navigable small world papers: Kleinberg, J. M. (2000). Navigation in a small world. Nature, 406(6798), 845-845.; Boguna, M., Krioukov, D., & Claffy, K. C. (2009). Navigability of complex networks. Nature Physics, 5(1), 74-80.

Topics:00:00 Intro00:42 Joan's background01:46 What attracted Joan's attention in Jina as a company and product?04:39 Main area of focus for Joan in the product05:46 How Open Source model works for Jina?08:38 Deeper dive into Jina.AI as a product and technology stack11:57 Does Jina fit the use cases of smaller / mid-size players with smaller amount of data?13:45 KNN/ANN algorithms available in Jina16:05 BigANN competition and BuddyPQ, increasing 12% in recall over FAISS17:07 Does Jina support customers in model training? Finetuner20:46 How does Jina framework compare to Vector Databases?26:46 Jina's investment in user-friendly APIs31:04 Applications of Jina beyond search engines, like question answering systems33:20 How to bring bits of neural search into traditional keyword retrieval? Connection to model interpretability41:14 Does Jina allow going multimodal, including images / audio etc?46:03 The magical question of Why55:20 Product announcement from JoanOrder your Jina swag https://docs.google.com/forms/d/e/1FAIpQLSedYVfqiwvdzWPX-blCpVu-tQoiFiUJQz2QnIHU1ggy1oyg/ Use this promo code: vectorPodcastxJinaAIShow notes:- Jina.AI: https://jina.ai/- HNSW + PostgreSQL Indexer: [GitHub - jina-ai/executor-hnsw-postgres: A production-ready, scalable Indexer for the Jina neural search framework, based on HNSW and PSQL](https://github.com/jina-ai/executor-h...)- pqlite: [GitHub - jina-ai/pqlite: A fast embedded library for Approximate Nearest Neighbor Search integrated with the Jina ecosystem](https://github.com/jina-ai/pqlite)- BuddyPQ: [Billion-Scale Vector Search: Team Sisu and BuddyPQ | by Dmitry Kan | Big-ANN-Benchmarks | Nov, 2021 | Medium](https://medium.com/big-ann-benchmarks...)- PaddlePaddle: [GitHub - PaddlePaddle/Paddle: PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice （『飞桨』核心框架，深度学习&机器学习高性能单机、分布式训练和跨平台部署）](https://github.com/PaddlePaddle/Paddle)- Jina Finetuner: [Finetuner 0.3.1 documentation](https://finetuner.jina.ai/)- [Not All Vector Databases Are Made Equal | by Dmitry Kan | Towards Data Science](https://towardsdatascience.com/milvus...)- Fluent interface (method chaining): [Fluent interfaces in Python | Florian Einfalt – Developer](https://florianeinfalt.de/posts/fluen...)- Sujit Pal's blog: [Salmon Run](http://sujitpal.blogspot.com/)- ByT5: Towards a token-free future with pre-trained byte-to-byte models https://arxiv.org/abs/2105.13626Special thanks to Saurabh Rai for the Podcast Thumbnail: https://twitter.com/srbhr_ https://www.linkedin.com/in/srbh077/

Show notes:- The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction https://research.google/pubs/pub46555/- IEEE MLOps Standard for Ethical AI https://docs.google.com/document/d/1x...- Qdrant: https://qdrant.tech/- Elixir connector for Qdrant by Tom: https://github.com/tlack/exqdr- Other 6 vector databases: https://towardsdatascience.com/milvus...- ByT5: Towards a token-free future with pre-trained byte-to-byte models https://arxiv.org/abs/2105.13626- Tantivy: https://github.com/quickwit-inc/tantivy- Papers with code: https://paperswithcode.com/

Show notes:- On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained) [YouTube](https://www.youtube.com/watch?v=3_qGr...)- [2108.07258 On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258)- [2005.11401 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)- Negative Data Augmentation: https://arxiv.org/abs/2102.05113- Beyond Accuracy: Behavioral Testing of NLP models with CheckList: [2005.04118 Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://arxiv.org/abs/2005.04118)- Symbolic AI vs Deep Learning battle https://www.technologyreview.com/2020...- Dense Passage Retrieval for Open-Domain Question Answering https://arxiv.org/abs/2004.04906- Data Augmentation Can Improve Robustness https://arxiv.org/abs/2111.05328- Contrastive Loss Explained. Contrastive loss has been used recently… | by Brian Williams | Towards Data Science https://towardsdatascience.com/contra...- Keras Code examples https://keras.io/examples/- https://you.com/ -- new web search engine by Richard Socher- The Book of Why: The New Science of Cause and Effect: Pearl, Judea, Mackenzie, Dana: 9780465097609: Amazon.com: Books https://www.amazon.com/Book-Why-Scien...- Chelsea Finn: https://twitter.com/chelseabfinn- Jeff Clune: https://twitter.com/jeffclune- Michael Bronstein (Geometric Deep Learning): https://twitter.com/mmbronstein https://arxiv.org/abs/2104.13478- Connor's Twitter: https://twitter.com/CShorten30- Dmitry's Twitter: https://twitter.com/DmitryKan

Order your Milvus t-shirt / hoodie! https://milvus.typeform.com/to/IrnLAgui Thanks Filip for arranging.Show notes: - Milvus DB: https://milvus.io/ - Not All Vector Databases Are Made Equal: https://towardsdatascience.com/milvus... - Milvus talk at Haystack: https://www.youtube.com/watch?v=MLSMs... - BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models https://arxiv.org/abs/2104.08663 - End-to-End Environmental Sound Classification using a 1D Convolutional Neural Network: https://arxiv.org/abs/1904.08990 - What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language models https://arxiv.org/abs/1907.13528 - NVIDIA Triton Inference Server: https://developer.nvidia.com/nvidia-t... - Towhee -- ML / Embedding pipeline making steps before Milvus easier: https://github.com/towhee-io/towhee - Being at the leading edge: http://paulgraham.com/startupideas.html

1. Layering problem: www.edge.org/conversation/sean_…-layers-of-reality2. Podcast with Etienne Dilocker (SeMI Technologies Co-Founder & CTO): www.youtube.com/watch?v=6lkanzOqhDs3. SOC2: linfordco.com/blog/soc-1-vs-soc-2-audit-reports/4. Dmitry's post on 7 Vector Databases: towardsdatascience.com/milvus-pineco…-9c65a3bd06965. Billion-Scale ANN Challenge: big-ann-benchmarks.com/index.html6. Weaviate Introduction: www.semi.technology/developers/weaviate/current/ Newsletter: www.semi.technology/newsletter/7. Use case: Scalable Knowledge Graph Search for 60+ million academic papers with Weaviate: medium.com/keenious/knowledge-…aviate-7964657ec9118. Bob's Twitter: twitter.com/bobvanluijt9. Dmitry's Twitter: twitter.com/DmitryKan10. Dmitry's tech blog: dmitry-kan.medium.com/

Show notes:1. Pinecone 2.0: https://www.pinecone.io/learn/pinecon... It is GA and free: https://www.pinecone.io/learn/v2-pric...2. Get your “Love Thy Nearest Neighbour” t-shirt :) shoot an email to greg@pinecone.io3. Billion-Scale Approximate Nearest Neighbour Search Challenge: https://big-ann-benchmarks.com/index.... 4. ANNOY: https://github.com/spotify/annoy5. FAISS: https://github.com/facebookresearch/f... 6. HNSW: https://github.com/nmslib/hnswlib 7. “How Zero Results Are Killing Ecommerce Conversions” https://lucidworks.com/post/how-zero-... 8. Try out Pinecone vector DB: https://app.pinecone.io/ 9. Twitter: https://twitter.com/Pinecone_io 10. LinkedIn: https://www.linkedin.com/company/pine... 11. Greg's Twitter: https://twitter.com/grigoriy_kogan 12. Dmitry's Twitter: https://twitter.com/DmitryKanWatch on YouTube: https://www.youtube.com/watch?v=jT3i7NLwJ8w