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Abstract: Today, with the ever more long documents and multimedia data, finding the right information is more important and challenging than ever. The rise of deep learning has ushered in a new era of "neural search". However, building a neural search system is non-trivial work for most engineers. The main challenges are: (1) long dev cycle due to the complex tech stack (2) poor scalability due to the glued architecture (3) strong requirements on the domain knowledge to fine-tune the results. With Jina (https://github.com/jina-ai/jina), engineers can quickly build up a search engine powered by state-of-the-art AI in just minutes. In this talk, I will introduce the design philosophy and the key features of Jina; and showcase how Jina bootstraps a QA semantic search system and a short-video search system in just lines of code.
- Speaker: Han Xiao
Dr. Han Xiao is the Founder & CEO of Jina AI, a neural search company. Han received a Ph.D. (2014) and M.Sc. (2009) in computer science from the Technical University of Munich in Germany. In[masked], Han led a team on neural information retrieval at Tencent AI. He served in the Tencent Technical Advisory Council and Opensource Program Office, fostering the open-source and DevOps culture in the company. In[masked] Han worked at Zalando Research as a Senior Research Scientist. He is also the creator of Fashion-MNIST (5.5K+ Google Scholar uses, ~10K Github stars) and BERT-as-service (~10K Github stars, ~1.6K forks).
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