Session Tracks:

We are looking for speakers to present on the following tracks:

Technical Workshops: Hands-on sessions focused on Google AI tools, frameworks, and practical applications.

Speaker Sessions: Inspirational talks, case studies, and insights into Google AI trends, ethics, and innovations.

To be considered as a speaker, applicants must meet the following conditions:

Relevant Expertise:

Demonstrated experience in AI, machine learning, or related fields.

For technical workshops, hands-on experience with Google AI tools and frameworks is required.

Session Proposal:

Submit a clear and concise session title and description (max 300 words).

Specify whether the session is a Technical Workshop or Speaker Session.

Outline the key takeaways for the audience.

Session Format:

Technical Workshops: 30 - 50 minutes, interactive and hands-on.

Speaker Sessions: 20–30 minutes, including Q&A.

Audience Engagement:

Proposals should demonstrate how the session will engage and benefit the audience.

For technical workshops, include details on tools, datasets, or platforms participants will use.

Original Content:

Sessions must feature original content or case studies.

Proposals that include demos, live coding, or real-world examples will be prioritized.

Availability:

Speakers must be available to present on the event day - 3rd May, 2025. (We expect our speakers to deliver sessions in-person).

Review the below topics to get familiar with the products and features we are most excited about

Gemini 2.0

Explore Gemini 2.0’s capabilities through the following notebooks using Google Colab:

• Getting Started - A comprehensive overview using the GenAI SDK
• Multimodal Live API - Quickstart - Overview of the Multimodal Live API using the GenAI SDK
• Gemini 2.0 - Multimodal live API: Tool use - Overview of tool use in the Live API with the GenAI SDK
• Gemini 2.0 - Multi-tool with the Multimodal Live API - Demo showing search, code and function calling with the Live API
• Gemini 2.0 - Search as a tool - Quick start using the Search tool with the unary and Live APIs in the GenAI SDK
• Search tool with Gemini 2.0 - Demo showing generation of a research report using the search tool with the unary API in the GenAI SDK
• 2D spatial understanding with Gemini 2.0 - Comprehensive overview of 2D spatial understanding capabilities with the GenAI SDK
• Pointing and 3D Spatial Understanding with Gemini 2.0 (Experimental) - Comprehensive overview of 3D spatial understanding capabilities with the GenAI SDK
• Video understanding with Gemini 2.0 - Comprehensive overview Gemini 2.0 video understanding capabilities with the GenAI SDK
• Introduction to Gemini 2.0 Flash Thinking - Introduction to the experimental Gemini 2.0 Flash Thinking model with the GenAI SDK

Gemma family of models

Explore Gemma family of models with following workshops:

• Gemma common_use_cases - Illustrate some common use cases for Gemma, CodeGemma and PaliGemma.
• Keras_Gemma_2_Quickstart - Gemma 2 pre-trained 9B model quickstart tutorial with Keras.
• Keras_Gemma_2_Quickstart_Chat - Gemma 2 instruction-tuned 9B model quickstart tutorial with Keras. Referenced in this blog.
• Synthetic_data_generation_with_Gemma_2 - Synthetic data generation with Gemma 2
• Minimal_RAG - Minimal example of building a RAG system with Gemma using Google UniSim and Hugging Face.
• Gemma_with_Firebase_Genkit_and_Ollama - Example to demonstrate using Gemma with Firebase Genkit and Ollama
• Keras_PaliGemma_2_Quickstart - PaliGemma 2 3B DOCCI model quickstart tutorial with Keras
• Finetune_PaliGemma_2_with_Keras - Finetune PaliGemma 2 with Keras.
• Finetune_PaliGemma_2_with_JAX - Finetune PaliGemma 2 with JAX.
• Run_with_Transformersjs - Run PaliGemma 2 with Transformers.js.
• CodeGemma_finetuned_on_SQL_with_HF - Demonstrate how to load, fine-tune and deploy CodeGemma model on SQL by utilising Hugging Face.
• Aligning_DPO_Gemma_2b_it - Demonstrate how to align a Gemma model using DPO (Direct Preference Optimization) with Hugging Face TRL.

On Device AI

• LLM Inference guide for Android - LLM Inference guide for Anrdoid
• LLM Inference guide for iOS - LLM Inference guide for iOS
• Gemma on Android - Android app to deploy fine-tuned Gemma-2B-it model using MediaPipe LLM Inference API.
• PaliGemma on Android – Inference PaliGemma on Android using Hugging Face and Gradio Client API for tasks like zero-shot object detection, image captioning, and visual question-answering.
• LLM Inference guide for Web - LLM Inference guide for Web
• Run_with_Transformersjs.ipynb – Run PaliGemma 2 with Transformers.js.

Google Cloud & Vertex AI

This list contains notebooks, code samples, sample apps, and other resources that demonstrate how to use, develop and manage generative AI workflows using Generative AI on Google Cloud, powered by Vertex AI:

• Intro to Gemini 2.0 Flash
• Intro to Multimodal Live API with Gen AI SDK
• Intro to Gemini 2.0 Thinking Mode
• Intro to Code Execution
• Multimodal Live API Demo App
• Intro to Google Gen AI SDK
• Real-Time RAG with Multimodal Live API
• Creating Marketing Assets using Gemini 2.0
• Vertex AI Gemini Research Multi Agent Demo Research Agent for EV Industry
• Create a Multi-Speaker Podcast with Gemini 2.0 & Text-to-Speech
• Intro to Gemini 2.0 Flash REST API
• Incorporating generative AI into your game development process with Gemini and Gemma AI