Google Gemma and Open Source AI Models - Clement Farabet
#1about 6 minutes
Tracing a 20-year journey through AI's evolution
Clement Farabet recounts his career from early neural net experiments and building custom hardware to the inflection point of AlexNet and joining Google DeepMind.
#2about 2 minutes
Understanding the roles of Gemini and Gemma models
Gemini is a powerful, cloud-based API model for complex reasoning, while Gemma is an open model designed for efficient, customizable on-device deployment.
#3about 4 minutes
How Gemma models evolve and support customization
Gemma's capabilities are often distilled from more advanced Gemini models, and the community creates specialized variants like Med-Gemma for specific domains.
#4about 3 minutes
Understanding the open and permissive license of Gemma
Gemma's license allows for broad commercial use and modification, providing developers with the model weights and inference code without a subscription model.
#5about 6 minutes
Building web apps and live experiences with AI Studio
AI Studio enables developers to generate entire web applications from prompts, which Gemini 1.5 Pro can then debug and deploy to Cloud Run.
#6about 7 minutes
Envisioning the future of AI-driven contextual operating systems
On-device models like Gemma are paving the way for contextual operating systems where traditional apps and file systems are replaced by natural language interactions.
#7about 10 minutes
Defining AI agents and navigating the path to AGI
True AI agents possess autonomous action capabilities, which are best developed within constrained environments to solve complex tasks safely and avoid open-ended risks.
#8about 6 minutes
How developers can get started and contribute to AI
Developers should start with open models on platforms like Hugging Face and focus on creating novel user interfaces or fine-tuning models for specific domains.
#9about 2 minutes
A call for developer feedback on AI Studio and APIs
Providing feedback on AI Studio and the Gemini API is crucial for helping Google steer the platform's development to better meet developer needs.
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Introducing generative AI in the browser with Chrome AI
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Using open source Gemma for local AI processing
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The history and merger of Google's AI teams
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Understanding Google's open weights Gemma models
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