Challenges and future directions in deploying NLP in commercial environments
Who is this presentation for?
- Data scientists, business analysts, and those who work with applied NLP or machine learning (ML)
NLP has made a major leap in the past decade from its theoretical capabilities and its integration into broadly deployed industry solutions. The field seems poised to continue its precipitous advance in mastering the distinctly human capability of using and understanding natural language. However, there are a couple of material challenges when deploying NLP for industry that need to be addressed—improving focused learning from a small set of examples and scaling the solution across different domains.
Moshe Wasserblat presents recent technology innovations and practices that show great promise in materially improving NLP results, scalability, and robustness. You’ll learn how it imposes a shift in how business organizations consume computing resources and deploy NLP applications.
What you'll learn
- Learn what holds back implementation of NLP applications in a real environment
- Understand state-of-the-art models and the latest solutions and resources for dealing with applied NLP challenges
- Discover the impact on hardware design and computing resources
Moshe Wasserblat is the Natural Language Processing and Deep Learning Research Group manager for Intel’s Artificial Intelligence Products Group. Previously, he was with NICE Systems for more than 17 years, where he founded and led the speech and text analytics research team. His interests are in the field of speech processing and natural language processing. He was the cofounder and coordinator of the EXCITEMENT FP7 ICT program and served as organizer and manager of several initiatives, including many Israeli chief scientist programs. He has filed more than 60 patents in the field of language technology and has several publications in international conferences and journals. His areas of expertise include speech recognition, conversational natural language processing, emotion detection, speaker separation, speaker recognition, deep learning, and machine learning.
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