Google I/O Extended 2024

Name: Google I/O Extended 2024
Start: 2024-07-30T18:00:00+01:00
End: 2024-07-30T21:00:00+01:00
Location: MongoDB Limited

MongoDB Limited, Shelbourne Road, Dublin, D04 Y3X9

GDG Dublin

Google I/O Extended is back, and free for everyone! After I/O our Google Developer Group Dublin will host an I/O Extended event to discuss the new technologies announced.

Jul 30, 5:00 – 8:00 PM

164 RSVP'd

Key Themes

AIAccessibilityAndroidCloudGeminiGoogle I/O ExtendedMobileVertex AIWeb

About this event

Topics will be around, Mobile, Web, AI and Cloud.
Do you want to speak at this event? if you do you can submit your talk here: https://sessionize.com/io-extended-2024-gdg-dublin
More details to come soon! 😄

When

Tuesday, July 30, 2024
5:00 PM – 8:00 PM UTC

Where

MongoDB Limited
Shelbourne Road
Dublin, D04 Y3X9

Agenda

Doors open, Pizza and networking
Harshit Meta - nsurance Policy Chatbot with Google Cloud Gemini and MongoDB Atlas Vector Search	In this talk, we will demo an insurance policy chatbot. The chatbot will use Retrieval Augmented Generation (RAG) to ground LLM responses. The tech stack includes Gemini (LLM), Google Cloud’s embedding model (vector generation), and MongoDB Atlas (vector search).
Tony O'Halloran - Cad é an Ghaeilge ar 'Transformer'? An Introduction to the Transformer Architecture for Translation	In this session, we’ll use Python to build our own model capable of (really poorly) translating from English to Irish. We’ll start with the basics of deep learning and build up an understanding of how the transformer architecture works for sequence-to-sequence tasks.
Urja Pawar - Exploring AI Decisions: A Deep Dive into Google’s What-If Tool	Understanding and trusting AI models is crucial for their effective deployment. Google’s What-If Tool offers an interactive and intuitive way to visualize and analyse machine learning models’ behaviour. This session will provide a comprehensive overview of the What-If Tool, demonstrating how it can be used to inspect model performance, explore counterfactuals, and ensure fairness across different datasets. Participants will learn to utilise this tool for debugging, auditing, and improving their models, making AI systems more transparent and reliable.