John Snow Labs Announces Fifth Annual NLP Summit, the World’s Largest Gathering for the Applied NLP, LLM, and Generative AI Community

Newly Released Program Focuses on Value Creation from Real-World Projects in Finance, Healthcare, Recruiting, Marketing, Life Science, and Education

John Snow Labs, the AI for healthcare company, today announced the program for the fifth annual NLP Summit, , taking place virtually September 24-26. The free conference, which attracted over 10,000 attendees last year, is the world’s largest gathering of the applied Generative AI and natural language processing (NLP) community.

Despite being a landmark year for Generative AI, few are capitalizing on its promised business value. This year’s agenda includes more than 50 technical sessions, covering cutting-edge applications that make a real impact. The program covers solution architectures, lessons learned, and trending open-source libraries, including the following keynote sessions:

  • The Age of Synthetic Data and Small LLMs – Loubha Ben Alla, Hugging Face
  • Multi-Agent Based Agentic Workflow in Healthcare – Sanjay Basu, Oracle
  • Deconstructing Graph RAG: An Overview of Concepts and Methodologies – Prashanth Rao, Kuzo
  • Applying Healthcare-Specific LLMs to Build Oncology Patient Timelines and Recommend Clinical Guidelines – Vishakha Sharma, Roche
  • Identifying and Mitigating Bias in AI Models for Recruiting – Katie Bakewell, NLPLogic and Jason Safley, Opptly
  • Quantizing Large Language Models – Supriya Raman, JPMorgan Chase
  • Automating Systematic Reviews of Academic Research – Dia Trambitas, John Snow Labs
  • Quantizing Large Language Models – Supriya Raman, JPMorgan Chase
  • Automating Systematic Reviews of Academic Research – Dia Trambitas, John Snow Labs
  • Pedagogical and Strategic Utility of Large Language Models – Michelle Banawan, Asian Institute of Management
  • Enhancing Marketing Campaigns with NLP: Data-Driven Decision Making in Consumer Analytics – Saurabh Kumar, The Kraft Heinz Company

Reliable and verified information completed by our editorial staff and experts Editing policy

How useful was this post?

Average rating 0 / 5. 0