Challenges of Artificial Intelligence

Ownership:

Centralized AI systems have ownership issues regarding the use and control of resources (data, algorithms, and computing power). These systems often accumulatevast resources for AI training and inference without explicit consent or adequatecompensation to the resource owners.

Transparency:

Transparency in AI is crucial for building trust, ensuring fairness, accountability, andstreamlining decision-making. However, the complexity of deep learning algorithms makes it difficult for even their creators to understand the decision-making process. This "black box" issue is particularly severe in centralized systems, which typicallylack the incentive to disclose the details of their AI models. The lack of transparencymakes it hard for users to understand decisions affecting them and complicates auditing and verification processes, impacting the fairness and ethical use of AI.

Permissionlessness:

Centralized AI systems usually offer limited choices and control to users. Designedand controlled by a single entity, they use a "one-size-fits-all" approach that cannot cater to all users' diverse needs. Additionally, these systems may restrict access toresources or algorithms, preventing users from customizing services to their specificrequirements.

Today, AI is controlled, governed, and owned by a few giant monopolies, includingOpenAI, Microsoft, Google, Meta, and a handful of closed-source AI labs. Wouldyoutrust your life or your family's lives in the hands of Sam Altman? The man whopromises to bring the world safe AI yet struggled to govern his own company, as evidenced by the recent board crisis. It's a stark reality that no one wants to admit: thefate of 8 billion people rests in the hands of fewer than 50 individuals who control AI through these companies.

This raises an important question: Is it possible to create a network where ordinarypeople can participate in the AI revolution in a decentralized and trust-minimized way, and benefit from its development?