How to Decentralize AI?

How to Decentralize AI?

The Intersection of Blockchain and Artificial Intelligence

The intersection of Web3 and artificial intelligence (AI) has become a widely discussed topic within the crypto community. Specifically, the integration of generative AI in Web3 has captured the attention of many blockchain enthusiasts. Generative AI is revolutionizing traditional software stacks, and Web3 aims to embrace this technology by exploring decentralized generative AI value propositions.

Does AI Deserve to be Decentralized?

When considering the integration of AI and blockchain technology, it is important to evaluate whether AI deserves to be decentralized. The argument for decentralization stems from the fact that AI is digital knowledge, and knowledge is a fundamental aspect of the digital world that deserves decentralization.

The concentration of control in the hands of major AI providers poses significant challenges. The pace of AI evolution follows a multi-exponential curve, and competing with centralized AI providers becomes increasingly difficult as they continue to improve their models. Decentralizing AI networks can foster collaboration among different parties, enabling the sharing of knowledge and democratic access to its benefits.

Transparency is another crucial element to consider. Foundation model architectures, such as GPT-4, involve complex neural networks that are difficult to understand using traditional monitoring practices. Decentralized AI networks can provide open testing benchmarks and guardrails, allowing visibility into the functioning of foundation models without relying on trust in a single provider.

Why Hasn’t Decentralized AI Worked Until Now?

While the case for decentralized AI seems clear, successful attempts in this area have been limited. The lack of success can be attributed to the questionable value proposition of decentralized AI approaches in the past. Before the emergence of large foundation models, the dominant AI architecture relied on supervised learning with curated and labeled datasets. These models were small enough to be easily interpretable and control was not a significant concern.

However, the prominence of large-scale generative AI and foundation models in a centralized manner has changed the landscape. This shift has created an opportunity for decentralized AI to thrive and overcome previous limitations.

The Dimensions of Decentralization in AI

When it comes to generative AI, decentralization should be considered across different stages of the foundation model’s lifecycle. These stages include pre-training, fine-tuning, and inference.

The Compute Decentralization Dimension

Decentralized computing is particularly relevant during the pre-training and fine-tuning phases of foundation models. Centralized data centers currently handle the significant GPU compute required for these operations. By creating a decentralized GPU compute network, different parties can contribute their compute resources, reducing the control exerted by large cloud providers.

The Data Decentralization Dimension

Data decentralization plays a crucial role in the pre-training and fine-tuning phases. The lack of transparency around the datasets used to train foundation models is a challenge. Introducing a decentralized data network incentivizes parties to contribute datasets with appropriate disclosures, fostering transparency and tracking the usage of data in model training.

The Optimization Decentralization Dimension

During the fine-tuning phase, validation through techniques like reinforcement learning with human feedback (RLHF) is essential. However, there is currently limited transparency in this area. A decentralized network of human and AI validators, where results are traceable, can improve the validation process and provide increased transparency.

The Evaluation Decentralization Dimension

AI benchmarks are flawed, lacking transparency and requiring trust in their creators. Decentralizing the evaluation of foundation models for different tasks is crucial to increase transparency. This dimension becomes particularly relevant during the inference phase.

The Model Execution Decentralization Dimension

The most obvious area for decentralization is the execution of foundation models. Currently, using these models requires trust in centralized infrastructures controlled by a single entity. Decentralizing the distribution of inference workloads across different parties can bring significant value to the adoption of foundation models.

The Right Way to Do AI

Foundation models have propelled AI into mainstream adoption, but they have also highlighted challenges that come with their rapid development. The case for decentralized AI has never been stronger. Digital knowledge, in all its dimensions, deserves to be decentralized. Centralized entities should not have excessive power over the future of intelligence.

While technical challenges exist, decentralizing AI is an achievable goal. It will require multiple technical breakthroughs, but in the era of foundation models, decentralized AI is the right approach to shaping the future of AI.

In conclusion, the intersection of blockchain and AI presents promising opportunities for the Web3 ecosystem. Decentralization can address the concentration of power in AI and enable democratic access to knowledge. By considering the various dimensions of decentralization in generative AI, we can pave the way for a more transparent and collaborative future.