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.
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.
Key components of the decentralized AI paradigm include:
Distributed AI Supercomputer: A decentralized AI supercomputer serves as the foundation for pre-training large generative models. As AI capabilities advance, such supercomputers demand significant computing power. A decentralized approach can facilitate the development and improvement of these models while distributing the computational load.
Incentive Mechanism: To maintain a distributed AI supercomputer at scale, an incentive mechanism is essential. Token architectures and blockchain technology can help incentivize decentralized computers to provide computational resources, ensuring efficient and reliable operation.
Open-Source Model and Community: Once the generative AI model is released to the public, it transitions to an open-source status. It is managed by a core group of developers and a broader community, similar to how open-source projects are maintained. This collaborative effort enables ongoing development and improvements.
Decentralized Ledger for Identity Verification: Addressing identity verification is crucial in this paradigm. A decentralized ledger allows users to securely access real-time AI engines by plugging in and out their data. This approach ensures that personalized experiences are delivered based on user preferences while maintaining data privacy.
Chip Architecture Optimized for ML: Hardware manufacturers, such as Apple and others, optimize devices for machine learning (ML) models. These devices become the decentralized engines for serving hyper-personalized content in real-time. The device itself acts as a server where personal data is stored.
Real-Time Engines on User Devices: The workflow involves pre-training AI models on decentralized computers. Once the open-source model is available, it’s customized for various applications. Users can interact with the model through a decentralized ledger for identity verification. Optimized hardware devices then serve as real-time engines, providing hyper-personalized content based on user data stored on the device.
Edge artificial intelligence (edge AI) combines artificial intelligence and edge computing to create AI workflows that span from centralized data centers to the network’s edge. The Edge Artificial Intelligence framework is exciting in the context of AI, where large generative models can learn the context of the user on the fly, as they’re able to access the data of the user through the hardware without the data ever flowing back or moving from it. Thus, it makes AI decentralized and privacy-oriented.
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results.
Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual.
Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.
OpenAI is an artificial intelligence research laboratory that transitioned into a for-profit organization in 2019. The corporate structure is organized around two entities: OpenAI, Inc., which is a single-member Delaware LLC controlled by OpenAI non-profit, And OpenAI LP, which is a capped, for-profit organization. The OpenAI LP is governed by the board of OpenAI, Inc (the foundation), which acts as a General Partner. At the same time, Limited Partners comprise employees of the LP, some of the board members, and other investors like Reid Hoffman’s charitable foundation, Khosla Ventures, and Microsoft, the leading investor in the LP.
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemiummodel. Microsoft also commercializes opener products through its commercial partnership.
OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
