Welcome to the AI meetup in Silicon Valley, in collaboration with JPMorgan Chase. Join us for deep dive tech talks on AI, GenAI, LLMs and machine learning, food/drink, networking with speakers and fellow developers.

Agenda:
- 5:30pm~6:00pm: Checkin, food and networking
- 6:00pm~6:10pm: Welcome, Community update
- 6:10pm~8:00pm: Tech talks and Q&A
- 8:00pm~8:30pm: Open discussion, Mixer and Closing.

keynote: Extending Capabilities of GenAI
Speaker: Jure Leskovec (Stanford University)
Abstract: The era of intelligent systems and applications is here and AI has been disrupting science and industry. The speed of change has been breathtaking. However, building AI-based solutions is hard, training AI models and putting them in production takes highly-skilled teams months if not years. There is a real need to drastically simplify AI workflow and bring AI closer to users, and make it accessible to a wide range of audiences. In this talk, I present our work on extending the capabilities of GenAI. I will discuss Relational Deep Learning, which allows for (1) automatic learning from the entire data spread across multiple relational tables; (2) no manual feature engineering as the system learns optimal embeddings; and (3) state-of-the-art predictive performance. Second, I will discuss how to build much more reliable retrieval augmented systems that prevent hallucinations and make much better use of private enterprise data.

Tech Talk: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
Speaker: Wanying Ding (JPMorgan Chase)
Abstract: Knowledge graphs have gained popularity for their ability to organize and analyze complex data effectively. When combined with graph embedding techniques, such as graph neural networks (GNNs), knowledge graphs become a potent tool in providing valuable insights. This work explores the application of graph embedding in identifying competitors from a financial knowledge graph, which are fraught with unique challenges, such as directed and undirected relationships, attributed nodes, and sparsely annotated competitor connections. To address these challenges, we propose a novel graph embedding model, JPEC (JPMorgan Proximity Embedding for Competitor Detection), for competitor retrieval. JPEC uses a graph neural network to learn both first-order and second-order node proximities that account for node features. In our experiments, JPEC has outperformed most existing models, showcasing its effectiveness in competitor retrieval.

Tech Talk: Turning the Machines Loose - Building GenAI with LaunchDarkly
Speaker: Cody De Arkland (LaunchDarkly)
Abstract: AI is ushering in the next generation of applications. More people are building software now than ever before. Ideas are being created on a Friday, and shipping version 1 of their application the following Monday. This new pace is putting new demands on the way applications are developed, and platforms like LaunchDarkly enable teams to keep shipping fast - while still maintaining control over what might go wrong. In this session, we'll unpack how LaunchDarkly is enabling the next generation of applications to ship AI faster, by using feature flags to control everything form models to prompts, target AI experiences at anyone or anything, and measure every AI change - recovering automatically when issues pop up.

Speakers/Topics:
Stay tuned as we are updating speakers and schedules. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics

Venue:
JPMC Tech Center, 3223 Hanover St, Palo Alto, CA 94304

Sponsors:
We are actively seeking sponsors to support AI developers community.  Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 30,000+ AI developers in San Francisco Bay Area or 350K+ worldwide.

Community on Slack/Discord
- Event chat: chat and connect with speakers and attendees
- Sharing blogs, events, job openings, projects collaborations
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