Malte Pietsch - CTO, Deepset - Passion in NLP and bridging the academia-industry gap with Haystack

YouTube: https://www.youtube.com/watch?v=N5Brb7Rzc2cTopics: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- 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/