4 Elements That Elevate Crypto AI Projects Above the Crowd
Imagine stepping into a world where artificial intelligence isn't locked away in the vaults of tech giants or confined to elite research labs.
Instead, AI is flourishing in an open, vibrant ecosystem where models can learn, grow and communicate freely across a decentralized network.
In this imagined future, users can contribute the processing power needed to train AI models from their personal computers, similar to how one might lend a hand in a communal garden. In return, those users gain access to a plethora of open-source AI models.
These models, each a specialist in its own right, communicate and collaborate via smart contracts, offering solutions that no single AI model could achieve on its own.
Then, through decentralized applications, this collective intelligence is readily accessible to average users, offering unrivaled insights and capabilities.
While this may seem like a pipe dream, the foundation for an AI future built on the blockchain like this is already being laid.
And it represents a massive paradigm shift from traditional, centralized AI models to decentralized AI in which the blockchain isn't just a ledger for cryptocurrencies, but the backbone of a new cognitive economy.
Right now, a lot of big AI projects are centralized. In this type of setup, AI models reside on centralized servers, often controlled by a single entity. This not only creates bottlenecks but also raises concerns over privacy, data security and accessibility.
Decentralized AI, in contrast, distributes the computational load across numerous nodes in the blockchain network. Blockchain technology, at its core, is a distributed ledger that offers transparency, security and immutability.
When these features intertwine with AI, the benefits are profound. Indeed, they could herald in a new era of trustworthy and autonomous systems as it democratizes access to AI.
This would allow anyone to contribute to or benefit from AI models without the gatekeeping often seen in centralized systems.
Now, as I alluded to, this requires four components.
First up: blockchain technology.
The blockchain's immutable records ensure that every piece of data used to train AI models can be traced to its origin, enhancing the credibility of AI-generated insights. This transparency is crucial for verifying transaction data.
It is also crucial for understanding the decisions made by AI.
Furthermore, the decentralized nature of blockchain means that AI models can operate without the need for a central authority, mitigating risks associated with data monopolies and single points of failure.
But the convergence of AI and blockchain tech is not just about creating more robust and transparent AI models. It's about forging a collaborative network where each participant, whether human or machine, contributes to a collective intelligence greater than the sum of its parts.
And that’s where our second component comes in: the integration of a decentralized mixture of experts — or MoE — model.
MoE is an ensemble method that combines the strengths of multiple expert models to make decisions or predictions. Each model “expert” specializes in a niche, contributing unique insights to the collective intelligence.
When decentralized on a blockchain, MoE transcends traditional boundaries, enabling a dynamic and fluid exchange of expertise that's both scalable and resilient.
This approach harnesses the collective power of diverse AI models to improve overall model performance.
But to make it all work, we’ll need smart contracts, the third component.
They will be the architects of interaction within this decentralized AI network by automating the collaboration between different AI models.
How? By codifying the rules of engagement, as they do for every other action on supported blockchains. Smart contracts could then provide a robust framework that allows decentralized AI models to work together, unlocking new possibilities for complex problem-solving and innovation.
Imagine, for example, a scenario where an AI model specializing in language translation needs to validate a nuanced term's context. It can automatically engage another model with expertise in cultural nuances with the transaction, execution and compensation all governed seamlessly by smart contracts.
This combination of an MoE AI model and smart contracts working together automatically is the golden ticket. It not only streamlines the process but also ensures transparency and fairness in the AI ecosystem.
Finally, everything above is woven together into a slick, decentralized app. This fourth component brings the entire process full circle.
See, dApps will serve as gateways for users to dive into the world of decentralized AI, much as they do for the broad decentralized finance sector currently. These applications, built on blockchain networks, can offer a seamless and secure way for users to interact with AI models, participate in their training or utilize their capabilities for various services.
These applications can also empower users to contribute to the AI's training and evolution. Through dApps, users can submit data, participate in consensus mechanisms or even propose updates to the AI models, all within a transparent and secure blockchain framework.
To truly appreciate the depth and potential of this transformation, let's delve into two concrete examples.
Bittensor
Bittensor is a crypto network where AI models, referred to as "neurons," communicate and learn from each other to optimize their performance.
Its unique consensus mechanism — the proof of intelligence — ensures that contributions are measured by the value they bring to the network. And there’s an incentive to add more value. Participants are rewarded with cryptocurrency tokens for their computational offerings.
The architecture of Bittensor is designed to mimic the neural pathways of the human brain, with each neuron contributing to the network's collective intelligence. This approach not only fosters a more efficient and scalable AI network but also opens up new avenues for machine learning models to evolve in ways previously unimaginable.
Microsoft's SUM on Blockchain
Microsoft's initiative, Sharing Updatable Models (SUM) on Blockchain, illustrates another innovative approach to decentralized AI.
This framework aims to democratize AI by enabling a collaborative environment where machine learning models can be shared, updated and utilized by anyone on the blockchain. SUM focuses on transparency and accessibility, allowing for the continuous improvement of AI models through community contributions.
The architecture of SUM encourages users to submit data or improvements to models, rewarding them for their contributions and ensuring that the AI evolves in a direction that benefits all users. This model not only accelerates the pace of AI development but also creates a more equitable ecosystem where the value generated by AI is shared among its contributors.
While SUM does not explicitly use an MoE model, its framework and emphasis on transparency and accessibility reflect MoE's principles of leveraging collective expertise for enhanced AI development.
So, there you have it. These innovations not only enhance AI's capabilities through collective intelligence but also democratize access to advanced technologies.
As we progress, the focus will likely shift toward overcoming challenges such as scalability, privacy and interoperability to fully realize the potential of decentralized AI ecosystems.
Until then, I'll keep a watchful eye on these evolving landscapes, continually exploring and reporting on the breakthroughs and challenges they present.
According to PricewaterhouseCoopers, AI could generate a whopping $15.7 trillion in extra GDP by 2030. With an outlook like that, AI is a narrative that likely isn’t going anywhere any time soon.
And I believe the real AI opportunities are in crypto.
For you, this journey into decentralized AI and blockchain represents not just technological curiosity but also a glimpse into the future of how we interact, innovate and integrate these advancements into our daily lives.
Stay tuned as we navigate this exciting frontier together, uncovering the stories that will shape our digital tomorrow.
Best,
Jurica Dujmovic
