Decentralized AI is crucial as it democratizes access to AI, fosters diversity and innovation, ensures data privacy, and provides a transparent and secure environment for AI development and usage.
While a centralized AI infrastructure exhibits effectiveness in various contexts, its inherent limitations pave the way for a more decentralized approach. One primary concern associated with centralized AI stems from potential biases embedded within the AI models. These biases could be attributed to a lack of diversity in the training data or in the development teams creating the models. Within a centralized system, control and decision-making power are concentrated in the hands of a limited group, which can inadvertently foster AI models reflecting their unconscious biases. Conversely, decentralized AI (DE-AI) advocates for participation from a diverse community, helping to mitigate these biases and foster more representative and unbiased AI models.
Moreover, a centralized AI infrastructure can incur significant expenses. The financial burden of establishing and maintaining the requisite hardware and software, along with the energy costs linked to operating high-performance AI models, can be substantial. Such steep cost barriers may restrict access to AI technology, making it exclusive to large corporations or affluent individuals. DE-AI responds to this issue by distributing computational tasks across a network of community nodes, effectively spreading the costs amongst numerous participants. This approach not only curtails the cost of AI services but also democratizes access to AI technology, making it more affordable and accessible to a broader user base.
DE-AI is needed to counter the constraints of centralized AI infrastructure, such as potential biases and substantial costs. By harnessing the power of decentralization, DE-AI aims to establish a more equitable and inclusive AI landscape.