A crucial topic in the world of AI for the last few months has been Decentralized AI. Many believe that AI systems need to keep evolving, and one of the first steps towards evolution is to decentralization.
Recently, Emad Mostaque, Stability AI founder and chief executive, stepped down from his role to pursue decentralized AI. He even continued to state in several posts on X that one can’t beat “centralized AI” with more “centralized AI,”.
Not going to beat centralized AI with more centralized AI.
— Emad acc/acc (@EMostaque) March 23, 2024
All in on #DecentralizedAI
Lots more 🔜 https://t.co/SbEF5zoo05
A similar action was taken by Elon Musk, who just a month ago, filed a lawsuit against OpenAI, as he believes the company has shifted from its goal to being decentralized for development and more towards profit purposes.
In this article, we are going to explore what are decentralized AI systems and how they can play a crucial role in shaping the future of the tech industry. So, let’s find out in-depth!
What is Decentralized AI?
An artificial intelligence (AI) solution that employs blockchain technology to distribute, analyze, and store data across a network of nodes is called a decentralized artificial intelligence (DAI) system.
Fundamentally, decentralization represents a move away from large, isolated computer systems and towards a more dispersed, cooperative strategy. It involves using open-source models and utilizing the combined power of GPUs that are dispersed throughout the world.
With the help of this paradigm, AI development and application can become more accessible and less dependent on the established centres of technological might.
In simple words, users can profit from AI-generated insights without giving up control of their data to a centralized authority by using pre-trained AI models on their local devices thanks to decentralized AI systems.
The user can process data that is kept on their smartphone using a prebuilt AI model as part of a decentralized approach, and they can communicate the results to a third party without disclosing any of their personal information.
Decentralized AI represents a philosophical as well as a technological revolution. It puts into question the current state of AI development, which has been controlled by a few powerful companies that can afford to invest in enormous data centres and processing capacity.
Conversely, it is based on the concept of a communal, cooperative network in which resources are shared and available to everybody with an internet connection. This strategy might level the playing field by enabling smaller organizations and people to contribute to AI research and gain from its developments.
Although the decentralized ethos is based on open-source models, these models frequently use synthetic data generated by their commercial counterparts, like GPT.
Furthermore, GPUs supplied by a few centralized IT behemoths are usually used by the decentralized AI infrastructure. To make the technology accessible to the wider public, a centralized organization must also provide an easy-to-use access layer. This paradox of centralization inside decentralization is both fascinating and intricate.
Why AI Should be Decentralized?
Considering the recent trends in AI technologies and the demand for technical evolution, here are a few reasons why modern AI must be decentralized:
Centralized Models
A small number of powerful companies own centralized black boxes that house a large portion of today’s AI. This concentration of power gives a small number of unchecked entities enormous influence over society, finance, and creativity, undermining AI’s otherwise democratizing promise.
Decentralizing the development and uses of AI systems is becoming increasingly important as they progress. AI that is permissionless and trustless can spur innovation in a variety of sectors.
Various AI models such as OpenAI and Stability AI haven’t decentralized their platforms, and this has led to their former CEOs resigning from the company. Emad Mostaque left Stability AI and has joined The Render Network, looking forward to paving the way for next-generation AI models with the help of decentralized GPU computing.
We are thrilled to welcome @EMostaque Founder of @StabilityAI as an advisor to the @rendernetwork to collaborate on next generation AI models, IP rights systems, and open standards powered by decentralized GPU computing.
— The Render Network (@rendernetwork) March 18, 2024
https://t.co/udZjhaMAFJ
Open-Source Models
On the other hand, there are several open-source models available on the internet which developers can access. More than 450,000 open-source AI models, including extensive language models, image generators, and more, are available on platforms like Hugging Face.
Although open-source models represent a significant improvement, they are frequently developed in isolation rather than in concert.
To decentralize AI efficiently, machine learning models that are capable of mutual learning must be constructed by open-source developers working together. To create AI systems that can compete with centralized alternatives, decentralized teams must collaborate.
Distributed Development
Instead of consolidating its power, decentralized AI spreads control over the most advanced technology ever created by humans, reducing the possibility of any one entity exerting a dominant impact.
AI can advance in a way that better aligns with individual wants rather than imposing uniform outcomes if development and governance are divided across entities with different motives and interests. This fosters a variety of applications as opposed to having a few dominant models control the culture.
Limiting Mass Surveillance and Manipulations
Decentralized AI additionally offers safeguards against government or corporate mass spying and manipulation. Massively deployed advanced AI against citizen interests is made possible by centralized control. However, these means of tyranny are curtailed by decentralized AI.
Decentralized artificial intelligence, in general, prevents any one organization from imposing a single set of incentives, limitations, or goals, which is essential for such a vital technology.
Components of a Decentralized AI
Decentralized AI systems integrate decentralized intelligence from local agents and across decentralized ecosystems with their services, and environments for higher-level intelligence and intelligent problem-solving. They also store, update, share, and exchange intelligence between decentralized agents, nodes, or devices.
Decentralized AI systems are constructed using several fundamental elements, including blockchain-distributed ledgers, smart contracts, federated learning, homomorphic encryption technologies, and AI platforms or decentralized apps, to accomplish this at a high level.
Distributed ledgers on blockchains allow AI developers to push pre-made machine learning (ML) models to users’ devices. Then, either alone or as a component of a coordinated connected network, these devices can function as autonomous agents doing local AI inference tasks.
A perfect example of Blockchain’s potential in Decentralized AI was suggested by Mustafa Saidalavi Mohamed, A Digital Transformation Specialist & Genomics AI/ML Trailblazer.
He suggested that Elon Musk employs Blockchain-based technologies in his open-source alternative to OpenAI, Grok-1. Mustafa believes that it will allow for greater development and participation of stakeholders and AI developers.
How AI Can Be Decentralised?
We need to reconsider the core components of the AI stack if we are to decentralize AI. This covers elements like data, processing power, training, fine-tuning, and inference of the model. It won’t be sufficient to just use open-source models if other components of the stack, like the entities that provide computing for inference or training, stay centralized.
Active coordination between all AI stack tiers is necessary for true decentralization. We must remember that a network is only as decentralized as its least decentralized element.
As a result, by breaking down the AI stack into its most fundamental modular components and developing marketplaces around them, decentralized AI networks can rival their centralized siblings.
Render-like networks are a prime illustration of this. It pays users for lending their extra computer power to networked artists who want image rendering but lack the computational capacity to complete their tasks.
Blockchain technology enables these kinds of solutions, which use financial incentives to arrange personnel across the AI stack. This provides a clearer picture as to why AI leaders such as Emad Mostaque stepped down from his position in Stability AI and joined The Render Network.
Conclusion
The concept of AI decentralization is still a dilemma for many AI enthusiasts out there. Some have considered the potential for collaborative research and development that comes with decentralizing AI but still, a fair majority have refused to overlook the cutting-edge technology benefits that come with centralized AI models.
