Episode description

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?

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