AI offers a wide array of applications, from audio transcription and image description to writing assistance, proofreading, content translation, and diverse general prompting purposes.

For implementing these kinds of use cases, you broadly have three options:

• Cloud solutions – send your data to an AI model running on a powerful server in the cloud
• On-device solutions – download a (possibly large) model file to the client and run it locally
• Shared models – use models built into a device or browser (the focus of this talk)

On the Chrome team, we’re proposing a number of APIs that we and partners we’ve worked with think would be worthwhile to have as common Web platform APIs.

In this talk, I’ll:
- Give an overview of these APIs
- Briefly demo some implementations in Chrome
- Open the floor for general discussion and feedback

Thierno currently works as a Web Ecosystem Engineer @Google focusing on on integrating Gemini into Google Chrome and making Web AI accessible for everyone.