Vector Podcast

YouTube: https://www.youtube.com/watch?v=e2tZ6HD4I44

Update: ZIR.AI has relaunched as Vectara: https://vectara.com/

Topics:

00:00 Intro

00:54 Amin’s background at Google Research and affinity to NLP and vector search field

05:28 Main focus areas of ZIR.AI in neural search

07:26 Does the company offer neural network training to clients? Other support provided with ranking and document format conversions

08:51 Usage of open source vs developing own tech

10:17 The core of ZIR.AI product

14:36 API support, communication protocols and P95/P99 SLAs, dedicated pools of encoders

17:13 Speeding up single node / single customer throughput and challenge of productionizing off the shelf models, like BERT

23:01 Distilling transformer models and why it can be out of reach of smaller companies

25:07 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 base

33:51 Dynamics of open source in vector search space and cloud players: Google, Amazon, Microsoft

36:03 Implementing a multilingual search with BM25 vs neural search and impact on business

38:56 Is vector search a hype similar to big data few years ago? Prediction for vector search algorithms influence relations databases

43:09 Is there a need to combine BM25 with neural search? Ideas from Amin and features offered in ZIR.AI product

51:31 Increasing the robustness of search — or simply making it to work

55:10 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

YouTube: https://www.youtube.com/watch?v=gvgD98jWrJM

Topics:

00:00 Introduction

01:04 Yury’s background in laser physics, computer vision and startups

05:14 How Yury entered the field of nearest neighbor search and his impression of it

09:03 “Not all Small Worlds are Navigable”

10:10 Gentle introduction into the theory of Small World Navigable Graphs and related concepts

13:55 Further clarification on the input constraints for the NN search algorithm design

15:03 What did not work in NSW algorithm and how did Yury set up to invent new algorithm called HNSW

24:06 Collaboration with Leo Boytsov on integrating HNSW in nmslib

26:01 Differences between HNSW and NSW

27:55 Does algorithm always converge?

31:56 How FAISS’s implementation is different from the original HNSW

33:13 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 Benchmarks

41:33 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 principles

53:29 Dmitry’s ANN algorithm KANNDI, which uses HNSW as a building block

1:02:16 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 general

1:13:05 The future of ANN field: search inside a neural network, graph ANN

1:15:14 Multistage ranking with graph based nearest neighbor search

1:18:18 Do we have the “best” ANN algorithm? How ANN algorithms influence each other

1:21:27 Yury’s plans on publishing his ideas

1:23:42 The intriguing question of Why

Show 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/

- 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 Intro

00:42 Joan's background

01:46 What attracted Joan's attention in Jina as a company and product?

04:39 Main area of focus for Joan in the product

05:46 How Open Source model works for Jina?

08:38 Deeper dive into Jina.AI as a product and technology stack

11:57 Does Jina fit the use cases of smaller / mid-size players with smaller amount of data?

13:45 KNN/ANN algorithms available in Jina

16:05 BigANN competition and BuddyPQ, increasing 12% in recall over FAISS

17:07 Does Jina support customers in model training? Finetuner

20:46 How does Jina framework compare to Vector Databases?

26:46 Jina's investment in user-friendly APIs

31:04 Applications of Jina beyond search engines, like question answering systems

33:20 How to bring bits of neural search into traditional keyword retrieval? Connection to model interpretability

41:14 Does Jina allow going multimodal, including images / audio etc?

46:03 The magical question of Why

55:20 Product announcement from Joan

Order your Jina swag https://docs.google.com/forms/d/e/1FAIpQLSedYVfqiwvdzWPX-blCpVu-tQoiFiUJQz2QnIHU1ggy1oyg/ Use this promo code: vectorPodcastxJinaAI

Show 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/2105.13626

Special thanks to Saurabh Rai for the Podcast Thumbnail: https://twitter.com/srbhr_ https://www.linkedin.com/in/srbh077/

YouTube: https://www.youtube.com/watch?v=kVCIDTmiZyk

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/

YouTube: https://www.youtube.com/watch?v=FQAT6E3EX6g

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

YouTube: https://www.youtube.com/watch?v=fHu8b-EzOzU

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

YouTube: https://www.youtube.com/watch?v=iHC5oeAN29o

Show notes:

1. Layering problem: www.edge.org/conversation/sean_…-layers-of-reality

2. Podcast with Etienne Dilocker (SeMI Technologies Co-Founder & CTO): www.youtube.com/watch?v=6lkanzOqhDs

3. SOC2: linfordco.com/blog/soc-1-vs-soc-2-audit-reports/

4. Dmitry's post on 7 Vector Databases: towardsdatascience.com/milvus-pineco…-9c65a3bd0696

5. Billion-Scale ANN Challenge: big-ann-benchmarks.com/index.html

6. 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-7964657ec911

8. Bob's Twitter: twitter.com/bobvanluijt

9. Dmitry's Twitter: twitter.com/DmitryKan

10. 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 [email protected]

3. Billion-Scale Approximate Nearest Neighbour Search Challenge: https://big-ann-benchmarks.com/index....

4. ANNOY: https://github.com/spotify/annoy

5. 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/DmitryKan

Watch on YouTube: https://www.youtube.com/watch?v=jT3i7NLwJ8w