Intuition 백서 전문

ntent. Furthermore, this approach would incentivize users to share their preferences. Users could be rewarded for providing data that helps reﬁne the accuracy of recommendations and advertisements. This creates a mutually beneﬁcial relationship where users receive content that genuinely interests them, and platforms can better serve their audiences. 9.3 AI Implications Artiﬁcial Intelligence (AI) systems are becoming increasingly integral to various sectors, signiﬁcantly inﬂu- encing decision-making processes, automating tasks, and driving innovation. However, the eﬃcacy of AI is fundamentally dependent on the quality and integrity of the data it ingests. Inaccurate, unveriﬁable, or biased data can lead to ﬂawed AI outputs, potentially causing signiﬁcant negative impacts. Intuition oﬀers a robust framework for ensuring that AI systems have access to high-quality, veriﬁable, and attributable data, thus enhancing their reliability and eﬀectiveness. The Intuition protocol incentivizes users to label data in a semantic format, thereby creating a global, per- missionless network of actors contributing to a global AI dataset. This decentralized approach aims to serve as the intuition module of the collective consciousness, encouraging individuals to share their information, knowledge, wisdom, and intuition. By signing contributed data with self-sovereign private keys, Intuition ensures that the data is veriﬁable and attributable, thereby maintaining its integrity. Veriﬁable attributes provided by Intuition enable AI systems to also selectively ingest data from trusted sources. Users are motivated to build their reputations to a level where AI companies seek to incorporate their data, potentially rewarding them for their contributions. This system also disincentivizes actions that 84 could harm reputations, promoting a culture of accuracy and reliability. AI companies may pay for data reads, creating a ﬁnancial incentive for users to provide high-quality data. The original data creators are thus rewarded, fostering an ecosystem of continuous improvement and trust. Furthermore, Intuition facilitates the discoverability of private data. AI companies can issue requests to users to run their models over local encrypted datasets, compensating them for selectively disclosing pieces of data or for allowing their models to run using homomorphic encryption/decryption techniques. This approach not only protects user privacy but also expands the dataset available to AI systems, enhancing their learning and predictive capabilities. 10 Conclusion As we have explored throughout this whitepaper, the evolving landscape of the web is rooted in a fundamen- tal shift from subjective, human-reliant trust mechanisms to more objective, system-driven trust frameworks. This migration is largely being led by decentralized/decentralizing technologies such as those under devel- opment in the crypto ecosystem. Despite the remarkable advancements in the development of ‘trustless’ systems, the broader paradigm of trust remains complex, nuanced, and multifaceted. While trustless sys- tems eliminate certain trust assumptions from the equation of our interactions, even these ‘trustless’ systems are not fully trustless and are still largely predicated on social consensus and trust in humans. Furthermore, many layers of the decentralized web cannot yet be made ‘trustless’, and must be made more ‘trustful’. Intuition is envisioned as a solution to help build a more trustful interaction layer of the internet. To accomplish this, Intuition reimagines how data is expressed, structured, reﬁned, and monetized, an innovation only recently made possible through the advancements in the crypto ecosystem. Intuition breaks data down into discrete units called ‘Atoms’, and encourages the expression of higher-order statements through semantic compositions of these ‘Atoms’ called Triples. With discrete, referenceable identiﬁers for all things, value can move through the system programmatically, rewarding users for contributing useful data. Intuition draws inspiration from the game theoretic mechanics that have successfully secured layer 1 blockchains for over a decade, and applies them to the process of achieving consensus around standardized data and identiﬁers for all things. The result is an open, permissionless, decentralized social and knowledge graph, built upon the veriﬁable collective wisdom of the crowds. This can be envisioned as a universal, permissionless, semantic data lake, akin to Tim Berners Lee’s original vision of the Semantic Web. The Intuition system serves as a technologically-mediated form of intuition, designed to enhance everyday interactions by providing us all with data we need, when we need it, to better decision-make as we navigate our exponential world. 85