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BlindChat
🐱 BlindChat

Type to start searching
mithril-security/blind_chat
 * 71
 * 11

BlindChat
mithril-security/blind_chat
 * 71
 * 11

 * 🏠 Home
   🏠 Home
    * 🐱 BlindChat 🐱 BlindChat
      Table of contents
       * 📜 What is BlindChat?
          * Introduction
      
       * 💬 BlindChat Local: Demo
          * 👩‍💻 You can try out BlindChat here!
      
       * 🌎 Who is BlindChat for?
       * 🎯 Roadmap
       * 📇 Get in touch
       * References
   
    * 🧑‍🎨 Design
    * 🛠️ Set-up

Table of contents
 * 📜 What is BlindChat?
    * Introduction

 * 💬 BlindChat Local: Demo
    * 👩‍💻 You can try out BlindChat here!

 * 🌎 Who is BlindChat for?
 * 🎯 Roadmap
 * 📇 Get in touch
 * References


👋 WELCOME TO BLINDCHAT!

--------------------------------------------------------------------------------

Making Conversational AI Confidential & Transparent


📜 WHAT IS BLINDCHAT?

--------------------------------------------------------------------------------


INTRODUCTION

🐱 BlindChat is an open-source project to develop the first fully in-browser and
private Conversational AI.

Most conversational AI solutions today require users to send their data to AI
providers who serve AI models as a Service. This poses privacy issues for users
who lose control over their data.

⚠️ Because data is a key asset to improve LLMs, many solutions more or less
implicitly fine-tune users’ data to improve their model.

This creates privacy risks for users as LLMs might learn their data by heart.
Carlini et al.[1] showed that LLMs such as GPT-J could learn at least 1% of
their training set by heart.

🔐 BlindChat solves this issue as users have guarantees that their data remains
private at all times and have full control over it, either by doing local
inference or using secure isolated environments called secure enclaves.


💬 BLINDCHAT LOCAL: DEMO

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👩‍💻 YOU CAN TRY OUT BLINDCHAT HERE!

We enable users to interact with a Flan-T5 model locally through their browser:
the model is pulled and used for local inference using transformers.js.


🌎 WHO IS BLINDCHAT FOR?

--------------------------------------------------------------------------------

BlindChat aims to serve two users:

 * End users: We want to provide privacy-by-design alternatives to change the
   current status quo. Most users today are forced to give up their data to
   leverage AI services, and opaque or inexistent privacy controls are the norm.

 * Developers: We want to help developers easily serve privacy-by-design
   Conversational AI, which is why we are focused on making BlindChat easy to
   customize and deploy.

(back to top)


🎯 ROADMAP

--------------------------------------------------------------------------------

You can check out our progress in more detail on our official roadmap. We
highlight feature on which we would love help from contributors in our help
wanted section.

Roadmap quick summary:

 * Revamping of Hugging Face Chat UI to make it entirely client-side (removal of
   telemetry, data sharing, server-side history of conversations, server-side
   inference, etc.)
 * Integration of privacy-by-design inference with local model
 * Local caching of conversations
 * Integration of more advanced local models (e.g. phi-1.5) and more advanced
   inference (e.g. Web LLM)
 * Integration of privacy-by-design inference with remote enclaves using
   BlindLlama for powerful models such as Llama 2 70b & Falcon 180b
 * Integration with LlamaIndex TS for local Retrieval Augmented Generation (RAG)
 * Internet search
 * Connectors to pull data from different sources


📇 GET IN TOUCH

--------------------------------------------------------------------------------

We would love to hear your feedback or suggestions, here are the ways you can
reach us:

 * Found a bug? Open an issue!
 * Got a suggestion? Join our Discord community and let us know!
 * Set up a one-on-one meeting with a member of our team

Want to hear more about our work on privacy in the field AI?

 * Check out our blog
 * Subscribe to our newsletter here

Thank you for your support!


REFERENCES

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[1] Carlini, N., Ippolito, D., Jagielski, M., Lee, K., Tramer, F., & Zhang, C.
(2022). Quantifying Memorization Across Neural Language Models. ArXiv.
/abs/2202.07646

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