Decentralized AI is a paradigm where a generative model can be pre-training on a large distributed AI supercomputer through an incentive mechanism. While an open-source community maintains the AI model. And a decentralized ledger for identity verification. Hardware devices available on the market with chips optimized for ML can work as distributed real-time engines for users, where hyper-personalized content can be delivered without ever leaving the users’ device.
Distributed AI Supercomputer
A distributed AI supercomputer can work as the foundation to enable the pre-training of the large generative model.
The more we move forward to additional capabilities of these AI supercomputers, the more we can be sure that those AI supercomputers whill require a massive amount of computing power to work (unless we change paradigm).
In that circumstance, a decentraized AI supercomputer would be used as the foundation to enable the development and improvement of these large generative models.
Incentive mechanism for computers in the network
Of course, in order for the supercomputer to work at scale, it might need an incentive mechanism for these decentralized computers to keep delivering this computing power.
In this respect, a token architecture might help shape that up.
Thus, mechanisms like the blockchain might help.
Open-source AI model and community
Once the model has been released to the public, it becomes open-source, and it’s developed by a community of core developers.
And a loosely held community of additional developers that, from time to time, will help this development.
Just like other open-source projects like Linux, Mozilla, WordPress, Wikipedia, and so forth, there might be a corporation managing the core development team and a foundation owning the open-source and leading the corporation, which can monetize this open-source effort via enterprise services built on top of the open-source software.
Identity verification on a decentralized ledger
A key problem to solve here is identity verification and a decentralized ledger able to enable users to plug in and out their data in order to get real-time AI engines available from time to time.
Chip architecture optimized for ML models
From that respect, hardware manufacturers like Apple (but over time, everyone will do it), which optimize their devices for ML models, become the decentralized devices able to serve content, contextualized and on the fly.
The device itself becomes the server where the personal data is stored.
Real-time engines served on top of users’ devices
To recap the workflow:
- AI models are pre-trained on top of decentralized computers.
- Once the generative AI model has been released as open-source, it gets maintained by a community of developers, while it can get downloaded by anyone.
- The open-source model is customized to accomplish many applications.
- These applications, through a decentralized ledger for identity verification, enable users to plug data n and out of these models based on consumption.
- The hardware device (like the iPhone) optimized for ML delivery become a real-time engine able to provide hyper-personalized content on the fly.
- The user can enjoy hyper-personalized experiences without having to give away the data, which is stored on top of the device, which works as database.
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