Currently, most AIs are centralized AI, and there are actually four issues:
First, data misuse and unclear ownership: Large companies train models by scraping public data (such as blogs and papers) without permission or compensation to data providers, leading to privacy and ethical controversies.
Second, the black box problem of models: The training process and data sources of large language models (like GPT-4) are not transparent, resulting in unreliable outputs ("hallucinations") and a crisis of trust.
Third, lack of incentives for contributors: Creators of data, models, and agents cannot benefit from the commercialization of the AI ecosystem, hindering collaborative innovation.
Fourth, limitations of general models: General large models lack precision in specific fields (such as finance and law), and the demand for specialized models (SLM) is increasing.
In simple terms, it means data is misused, algorithms are black boxes, ordinary people have no opportunity to participate, and general models are insufficient in niche areas.
Therefore, in response to these issues, AI combined with Web3 can have many new features, and this is the direction many projects are trying, one of which is worth noting is OpenLedger. @OpenledgerHQ
OpenLedger is an AI blockchain that can monetize data, models, and agents. This chain is built on the OP Stack-based Layer 2 network, using EigenDA for data availability storage.
OpenLedger proposes a key concept: "Data Intelligence Layer."
The "Data Intelligence Layer" is a key component of the OpenLedger architecture, positioned as a decentralized middleware that connects data providers, model developers, AI agents, and the Web3 ecosystem.
The "Data Intelligence Layer" can be likened to the "operating system" of decentralized AI, similar to the role of AWS in cloud computing.
It not only manages data and models but also empowers ecosystem participants (individuals, businesses, communities) with collaboration and monetization capabilities through the transparency of blockchain and token economics.
This design of the "layer" transforms AI from centralized silos into an open collaborative network, unleashing the liquidity of data and intelligence.
There are four key technologies at the core:
1. Proof of Attribution
Unlike traditional Proof of Work or Proof of Stake, Proof of Attribution is a consensus mechanism specifically designed for the AI ecosystem, emphasizing "value attribution" rather than computational power competition.
This not only reduces energy consumption but also highly aligns with the data-driven characteristics of AI.
2. Datanets
The success of Datanets relies on community participation and data quality. OpenLedger is introducing dynamic pricing and quality assessment mechanisms to incentivize high-value data contributions and filter low-quality data.
3. Model Factory
The GUI design of the Model Factory disrupts existing complex fine-tuning frameworks (such as command-line tools), similar to "Canva for AI."
This can attract small and medium-sized enterprises and independent developers, expanding the diversity of the ecosystem.
4. OpenLoRA
The technical implementation of OpenLoRA involves model compression or distributed computing optimization, seamlessly integrating with existing blockchain infrastructure (such as EigenDA).
In summary, OpenLedger constructs a unique decentralized AI ecosystem through Proof of Attribution, Datanets, Model Factory, and OpenLoRA, with advantages in specialized models, transparency, and Web3 integration, giving it competitive potential in high-precision fields such as finance and law.
OpenLedger has just completed its first visit to China in Hong Kong, with upcoming arrangements in Hangzhou, Shenzhen, Chengdu, and Shanghai. Interested friends can communicate with the project team on-site, and there is also a chance to obtain an airdrop whitelist offline!