Why and how we should decentralize AI

”AI is digital knowledge, and knowledge might be the number one construct of the digital world that deserves to be decentralized.” - Jesus Rodriguez, IntoTheBlock

Decentralizing AI is of paramount importance in the contemporary technological landscape. Centralized control over artificial intelligence, whether it be in terms of computational resources, data ownership, or decision-making processes, poses significant risks. Decentralization fosters transparency, reducing the opacity surrounding AI model training, data usage, and evaluation. It mitigates the potential for undue influence and monopolistic control by a few major players, democratizing access to AI capabilities. By involving a diverse set of stakeholders and contributors, decentralization promotes innovation and ensures AI aligns better with a broader spectrum of human interests and values. It also enhances the robustness and resilience of AI systems by reducing their vulnerability to single points of failure. Ultimately, decentralizing AI empowers individuals, organizations, and communities, ushering in a more equitable and accountable era for artificial intelligence.

Decentralizing AI is a formidable challenge due to a multitude of technical, economic, and practical hurdles. First and foremost, the computational power required for AI training and inference often necessitates centralized data centers with immense resources, making it logistically complex to distribute this processing across decentralized networks efficiently. Data decentralization faces issues of privacy, security, and quality control, which are difficult to address without central oversight. The optimization and evaluation dimensions demand stringent quality control and coordination that can be challenging to achieve in a decentralized model. Furthermore, overcoming existing economic incentives and power structures favoring centralized AI ecosystems is no small feat. Achieving decentralization requires trust, cooperation, and novel governance frameworks among participants. In essence, while decentralizing AI offers a plethora of benefits, the complexities of realigning existing infrastructures, power dynamics, and technical architectures present significant barriers to its widespread adoption.

The challenge of decentralizing AI may be complex, but in theory, innovation from web3 and AI engineers can get us there. It’s best to look at the problem from it’s component parts, to better understand what decentralized AI would look like.

The ways AI can be decentralized can be broken down into several dimensions:

1. Compute Decentralization Dimension: This dimension involves decentralizing the computational resources used in AI processes. It is highly relevant during pre-training and fine-tuning phases, where significant GPU compute cycles are needed. By establishing a decentralized GPU compute network, various parties can contribute their computing power, reducing the control large cloud providers have over foundation model creation.
2. Data Decentralization Dimension: Data decentralization is crucial during pre-training and fine-tuning. The opacity surrounding the datasets used for these phases is a concern. A decentralized data network could incentivize multiple parties to provide datasets with clear disclosures and monitor their usage in model training.
3. Optimization Decentralization Dimension: Optimization decentralization involves the use of human intervention to validate and improve AI model outputs. This is particularly relevant during fine-tuning, where transparency is currently lacking. A network of human and AI validators, whose actions are traceable, can significantly enhance this aspect.
4. Evaluation Decentralization Dimension: The evaluation dimension pertains to determining the best language model for specific tasks. AI benchmarks and evaluations are often not transparent and require trust in the organizations conducting them. Decentralizing the evaluation of foundation models for different tasks can improve transparency, particularly during the inference phase.
5. Model Execution Decentralization Dimension: This dimension focuses on decentralizing the execution of AI models. Currently, using foundation models relies on centralized infrastructure, necessitating trust in a single party. Establishing a network for distributing inference workloads across different parties can enhance the adoption of foundation models and increase their accessibility.

These dimensions emphasize the importance of decentralization at various stages of AI development and deployment, from computation and data to optimization, evaluation, and model execution. Decentralization in these areas can improve transparency, reduce central control, and encourage broader participation in the AI ecosystem.

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