Innovation of AI Stack: The Vision and Practice of Sahara AI

Sahara AI is committed to creating a more open, fair, and collaborative artificial intelligence economic environment. By leveraging blockchain technology, Sahara ensures that all participants, including data contributors, annotators, and model developers, receive fair compensation while safeguarding the sovereignty of data and models, as well as the security and tradability of artificial intelligence assets.

Current AI Stack Overview

The current AI stack mainly consists of the following layers:

1. Data Collection and Annotation
2. Model Training and Services
3. Creation and Deployment of AI Agents
4. Computing Resources

There is intense competition and diverse practices at every level. For example, data collection often utilizes large public datasets for foundational training, followed by fine-tuning with specialized datasets. Model training requires dedicated hardware support, while the development of AI agents should facilitate the use of ready-made resources to attract more developers to participate.

However, traditional Web2 companies face many limitations in this regard. Their centralized design leads to isolation between different parts of the stack, resulting in inconsistencies in security standards, database design, backend integration, and profit strategies. This design is difficult to adapt to the rapid changes in the artificial intelligence economic model.

Sahara's AI Collaborative Economy Vision

The Sahara platform aims to provide one-stop services for the entire AI lifecycle, covering all aspects from data collection, annotation, model training, and services, to the creation and deployment of AI entities, as well as multi-agent communication, AI asset trading, and resource crowdsourcing. By lowering the entry barrier, Sahara AI offers individuals, enterprises, and communities equal opportunities to participate in the construction of the AI future.

The target user group of Sahara includes:

• Experienced AI developers: Can interact with the blockchain and AI stack using the Sahara SDK and API.
• AI Development Beginners: Create AI assets using no-code/low-code environments with intuitive interfaces and preset templates.
• AI Training Participants: Complete AI training tasks and earn token rewards.
• Regular AI Users: Use the AI agent through a simple user interface, with flexible options to purchase usage rights or trade shares of AI assets.
• Enterprise users: Create a dedicated AI intelligent entity, train it using their own data, and enjoy lower-cost decentralized AI services.

Technological Innovation

The Sahara team has made numerous technical innovations on the backend while simplifying the user experience:

• Sahara Blockchain: Minimizes gas fees, compatible with EVM, enabling secure cross-chain communication.
• AI-Native Precompiles: Precompiled smart contracts that optimize AI task performance.
• Sahara Blockchain Protocol: Manage AI assets, track contributions, and distribute rewards.
• Data Management: On-chain storage of metadata and proofs, off-chain storage of important datasets and models.
• Collaborative Execution Protocol: Supports the joint development and deployment of AI models.

Team Background

Sahara is led by USC professor Sean Ren and UC Berkeley alumnus Tyler Z, with team members from renowned institutions and companies such as Stanford, UC Berkeley, AI2, Toloka, Stability AI, Microsoft, Google, and Chainlink. Additionally, there are advisors from companies like Motherson Group, Together AI, and Midjourney providing professional guidance.

Currently, Sahara AI has been adopted by over 35 leading technology innovation projects and research institutions, including Microsoft, Amazon, MIT, etc., for services such as data collection, annotation, and personalized domain agents.

At a time when generative AI is still in its infancy, Sahara AI uniquely addresses the bottleneck issues of AI stack integration through its modular design and the application of blockchain technology. Sahara AI is moving towards making AI technology more accessible and fairer, paving the way for the popularization of AI.