2 Platforms That Unite Blockchain and AI Tech to Push Both Higher
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| By Jurica Dujmovic |
Blockchain technology is rapidly maturing, pushing beyond its cryptocurrency origins to become a foundational technology for innovative applications across various sectors.
And, as I alluded to last week, one of the most notable intersections is with artificial intelligence, where blockchain tech can provide a new framework for developing and deploying AI systems.
But before we go further, let’s first discuss the difference between AI implementations. Specifically, decentralized vs. centralized AI solutions.
Let’s start with centralized AI systems.
They offer enhanced scalability and efficiency due to their access to powerful, high-performance computing resources in data centers, enabling them to efficiently process large volumes of data. They also benefit from more straightforward regulatory compliance frameworks and generally incur lower operational costs by avoiding blockchain transaction fees.
However, centralized AI lacks the inherent security and decentralization advantages of blockchain-based systems. While they may implement robust security measures, they do not offer the distributed data structure of blockchain, which enhances resistance to cyberattacks and ensures no single entity controls the entire network.
Furthermore, commercially available centralized AI (apart from open-source platforms) might not match the flexibility and innovative potential of decentralized systems, which often encourage open and collaborative development through community-driven governance.
Decentralized AI systems, on the other hand, present a novel solution to many of the limitations found in centralized models. However, they are not without their own set of challenges. Questions around scalability, computational efficiency and operational complexities are intrinsic to the nascent nature of these systems.
Despite these growing pains, there are innovative platforms actively demonstrating the value and potential of decentralized AI.
One of those platforms is the Render Network (RNDR, Not Yet Rated).
Render is a blockchain-based GPU rendering network that leverages the power of distributed computing to democratize the process of rendering 3D graphics, as well as providing computational power for the AI.
This network is significant for several reasons:
- Decentralized Rendering: RNDR utilizes the idle GPU power available across its network of users to perform complex rendering tasks. This approach not only maximizes resource utilization but also offers a more cost-effective solution compared to traditional, centralized rendering farms. Decentralization also makes it incredibly scalable, where centralized rendering farms are very limited in terms of growth.
- Accessibility and Efficiency: By enabling users to tap into a global network of GPU power, RNDR makes high-quality rendering accessible to a broader range of creators, including those who might not have access to high-end rendering hardware.
- Integration with AI: RNDR's potential extends beyond rendering. The network's vast GPU resources can be utilized for AI-driven tasks, such as training machine learning models or performing complex computations necessary for advanced AI applications.
- Economic and Ecological Impact: RNDR offers a new economic model for both creators and GPU providers. Users who contribute their GPU power to the network can earn RNDR tokens, creating a unique marketplace for rendering services. This not only incentivizes participation but also promotes an eco-friendlier approach by using existing resources more efficiently.
- Innovation in Crypto and AI: The Render Network symbolizes the innovative intersection of blockchain, AI and digital content creation. By leveraging blockchain technology, RNDR stands at the forefront of a new wave of decentralized applications that could reshape industries like entertainment, architecture and virtual reality.
By enabling the decentralized execution of resource-intensive AI tasks, RNDR addresses a key limitation in blockchain technology: the lack of computational power for complex operations. This approach not only democratizes access to high-end rendering and AI computing resources but also promotes a more efficient use of existing hardware.
Render’s ability to handle AI-driven tasks like training machine learning models or processing large data sets on a decentralized platform is a significant stride towards overcoming the computational challenges often faced in blockchain environments, paving the way for more advanced and diverse AI applications in the blockchain space.
Petals offers another prime example of the potential for AI integration with blockchain technology. Similar to Render Network, Petals leverages a decentralized approach, using the idle computing resources from consumer devices to power an AI model fine tuning and generation on a distributed computing platform.
Some key aspects that make Petals notable include:
- Distributed Computing for AI: By utilizing spare computing capacity on consumer devices, Petals facilitates affordable and scalable distributed computing for training and running AI models. This provides an alternative to reliance on large, centralized data centers.
- Data Privacy and Security: Through cryptography and blockchain-based access controls, Petals aims to provide enhanced security, privacy and control over sensitive data used to develop and power AI models. This addresses key ethical concerns with centralized AI.
- Community-Governed Platform: Petals utilizes a community governance model and decentralized autonomous organization structure to manage the platform.
With innovations like decentralized model marketplaces and distributed computing to power AI, Petals signifies how blockchain technology can unlock new paradigms for AI development and deployment aligned with ideals like data privacy, user empowerment and democratization of access.
And in the case of Render, you can invest in this intersection of AI and blockchain tech by buying its native token, RNDR, which was recently listed on centralized exchanges.
But both Render and Petals rely on the broader blockchain infrastructure. That’s what makes these innovations possible.
Choosing the optimal blockchain platform for the implementation of AI is a crucial consideration for developers and organizations looking to leverage AI capabilities. Key factors impacting this decision include scalability to handle vast transactions, composability for building complex applications, computational power for AI processing and robust tooling for seamless development.
And for investors, that means there’s a pick-and-shovel opportunity in betting on the blockchain that best supports AI.
Picking may prove difficult at this stage, however.
Ethereum’s (ETH, “B+”) current position as a leading smart contract blockchain offers an established environment for AI-related endeavors.
But like everything developed on Ethereum, that stability comes at the cost of fast speeds.
In contrast, alternative protocols such as Cardano (ADA, “B”), Tezos (XTZ, “C+”) and Algorand (ALGO, “C”) are quickly evolving, presenting advanced protocol capabilities that could redefine throughput and interoperability within the AI-blockchain domain.
Meanwhile, the inclusion of another major narrative in crypto, interoperability, shifts my perspective even more. As a reminder, interoperability is the ability for different platforms built on different blockchains to communicate and work together.
As of right now, that functionality is limited as most blockchains are a world onto themselves. But there are notable projects looking to break through that barrier.
Polkadot's (DOT, “C+”) heterogenous multi-chain framework aims to enable a fully interoperable "internet of blockchains." In other words, it’s an environment better suited for complex AI systems that need to interact across customized side chains optimized for functions like data curation, model training, prediction marketplaces, decentralized identity and more.
Cosmos (ATOM, “C+”) offers similar interchain capabilities and additional tools that developers may find appealing.
Ultimately, specialization across blockchain platforms for supporting different elements of adaptive intelligence architectures while retaining composability may emerge as the winning strategy. One chain may excel at data quality while others handle distributed learning or verifiable computation.
But in that case, the path forward will likely involve architectural optimization, not a one-size-fits-all blockchain. Particularly in the realm of AI, where learning models and datasets can be vastly different, the challenge of ensuring that these systems can effectively interoperate is pronounced.
Interoperability can empower AI solutions to leverage the unique strengths of different blockchain networks, such as Ethereum's rich development ecosystem, Tezos's formal verification for secure smart contracts or Polkadot's cross-chain composability.
To realize the full potential of blockchain-enabled AI, developers must create systems that are not only interoperable within the blockchain ecosystem but also with existing non-blockchain infrastructures.
For investors, this could mean more opportunities and ways to get leveraged to this one trend that represents a convergence of three narratives that I see playing a big role in the coming bull market.
Best,
Jurica
