The world is rushing to embrace Generative AI and Large Language models (LLMs), seeing how these tools could drive a massive productivity boost in the workplace. However, to unlock this potential, your enterprise must ensure that your LLMs are grounded in accuracy and explainability. If you don’t, your results could be biased or riddled with hard-to-uncover hallucinations.

To learn how your organization can confidently adopt Generative AI, join this on-demand fireside chat with Jesús Barrasa, Neo4j’s Head of Solutions Architecture; Nick Johnson, Neo4j’s Senior Product Marketing Manager; along with our special guests, Dr. Ali Arsanjani, Google Cloud; and Geraldene Munsamy, Basecamp Research.

We will explore:
  • The distinction between Generative AI and LLMs
  • The opportunities and challenges enterprises face in their LLM adoption
  • How a Neo4j knowledge graph can help your organization adopt Generative AI with confidence
  • The difference between grounding an LLM with a vector database and a knowledge graph

DR. ALI ARSANJANI
Director of AI /ML Partner Engineering, Google Cloud

Dr. Ali Arsanjani is the Director of Cloud Partner Engineering at Google Cloud, where he cultivates key partnerships specializing in AI/ML and Data/Analytics. He aligns current market needs with future trends to steer Google's AI and Analytics strategy.Previously, he headed Machine Learning at AWS and led their largest customer segment, developing AI/ML strategies and solutions. Ali also managed a research team focused on advanced AI technologies like Neuro Machine Interaction, NLP, and Graph Neural Networks.

Geraldene Munsamy
Senior Deep Learning Scientist, Basecamp Research

Geraldene Munsamy is a senior deep learning scientist at Basecamp Research, a biotech startup focused on bridging the gap between natural biodiversity and cutting-edge biotechnological applications. Geraldene holds a Ph.D. in Computational Chemistry, where her research was centered on the utilization of computational methods and machine learning for bespoke drug design. Leveraging her research expertise in understanding the mechanistics of protein dynamics Geraldene's current role specializes in applying machine learning to power protein design and function annotation as well as serving as a strategic team player in defining and resourcing the AI roadmaps across the company's protein product area.

Dr. Jesús Barrasa
Head of Solutions Architecture, Neo4j

Dr. Jesús Barrasa is the Head of Solutions Architecture at Neo4j and the company's resident expert in semantic technologies. He combines over 20 years of professional experience in the data management space split between industry and research and academia. Prior to joining Neo4j, Jesús worked for data integration companies like Denodo and Ontology Systems (now EXFO) where he got first-hand experience with many successful enterprise-wide data integration deployments and large graph technology projects enhancing the operations and analytics of major companies all over the world. Jesús' doctoral work in Artificial Intelligence and Knowledge Representation focused on the automatic repurposing of legacy data as knowledge graphs. He's an active thought leader in the graph and semantics communities and co-hosts the popular monthly webcast on knowledge graphs "Going Meta" (http://goingmeta.live/).

Nick Johnson
Senior Product Marketing Manager, Neo4j

Nick Johnson is a Senior Product Marketing Manager for Graph Data Science at Neo4j, where he works to bring Graph Data Science to life with highly targeted messaging, positioning, case studies, content, and more. Before Neo4j, Nick worked in Product Marketing across several organizations including Microsoft, SAS, and Orbit. Nick holds a Master’s degree in International Development from Sciences Po and Bachelor degrees in International Studies and Swedish from the University of Washington. 

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