Humanization In AI
A decentralized identity and AI training solution designed to ensure security, privacy, and trust in the digital world.
Last updated
A decentralized identity and AI training solution designed to ensure security, privacy, and trust in the digital world.
Last updated
InterLink AI is a decentralized identity and artificial intelligence (AI) training platform designed to uphold security, privacy, and trust in digital environments. By leveraging Unique Data Training and Federated Learning, it facilitates the development of high-quality AI models while ensuring that user data remains securely stored on local devices rather than centralized servers.
The platform integrates non-identifiable biometric data from publicly available datasets, strategic partnerships, and governmental sources, enabling both real-time and batch AI processing with a high degree of accuracy. Security is reinforced through hashed biometric pools, zero-knowledge mapping, and trust scoring mechanisms, ensuring robust identity verification while preventing unauthorized access.
InterLink AI enables on-device AI model fine-tuning, preserving data privacy while allowing continuous learning and adaptation. These models are deployed through scalable, serverless architectures to support AI-driven automation and intelligent interactions. Built on high-performance infrastructure, including NVIDIA H100 GPUs and advanced decentralized storage solutions, InterLink AI ensures efficient, scalable processing while adhering to industry standards established by the National Institute of Standards and Technology (NIST).
AI model accuracy is often compromised by data contamination, where excessive duplicate and bot-generated inputs degrade learning quality. Conventional AI training methods rely on large-scale, centralized data collection, making them vulnerable to biases, redundancies, and security risks. Unique Data Training addresses these challenges by ensuring that AI models learn exclusively from real, verified human contributors.
Key Mechanisms of Unique Data Training
Human-Verified Data Collection: To prevent bot interference and duplicate data submissions, a proof-of-personhood mechanism is implemented. Each participant undergoes identity verification before contributing data, guaranteeing dataset authenticity and uniqueness. This validation process mitigates the risk of synthetic or fraudulent inputs that could otherwise distort model learning.
Decentralized Data Framework: Unlike traditional centralized storage methods, which pose privacy and security concerns, Unique Data Training operates within a decentralized infrastructure. This approach enables secure, private, and high-quality data acquisition while preserving individual data sovereignty. AI models train on locally stored datasets without direct exposure to centralized repositories, reducing risks associated with data breaches.
Peer-to-Peer Model Refinement: To further enhance learning efficiency, verified contributors can engage in peer-to-peer data sharing, allowing AI models to adapt to diverse datasets without relying on a single controlling authority. This fosters a more resilient and unbiased AI ecosystem, improving model robustness across different use cases.
Improved Decision Accuracy and Ethical AI Development: By ensuring AI models are trained on unique and bot-free data, the resulting algorithms achieve higher decision accuracy and greater reliability. This structured approach eliminates biases, enhances transparency, and promotes ethical AI development, fostering a sustainable data-sharing ecosystem where contributors are incentivized through structured rewards.
Decentralized Federated Learning AI represents a transformative approach to machine learning, enabling AI models to be trained directly on user devices without transmitting raw data to centralized servers. This decentralized methodology enhances privacy, security, and efficiency by ensuring that data remains localized while still contributing to collective model improvements. Each device independently trains the AI model using its unique dataset, generating model updates rather than exposing personal information.
Local Training on Devices: Each participating device, including smartphones, tablets, and laptops, independently trains an AI model using locally stored data. This approach ensures that sensitive user information remains on-device, eliminating the need for centralized data collection while still enabling model improvements across the broader AI ecosystem.
Model Distribution: A baseline AI model is initially deployed from a central or distributed node to participating devices, establishing a standardized foundation for localized learning. As users interact with their devices, the model undergoes continuous refinement using real-time, device-specific data. This process preserves data privacy while simultaneously enhancing model performance and personalization.
Peer-to-Peer Model Sharing: To enhance the efficiency and robustness of the training process, devices can exchange model updates directly with nearby nodes in a peer-to-peer manner. This decentralized sharing mechanism reduces dependence on a central authority, fosters collaborative learning, and improves adaptation to diverse data distributions.
Model Updated Distribution: Instead of transmitting raw data, devices generate encrypted model updates, such as weight adjustments, which are securely aggregated. The improved global model is then redistributed to participating devices, ensuring continuous AI advancement while maintaining strict data sovereignty and compliance with privacy standards.
On the AI development side, AI model organizations and developers initiate the training process by sending training requests. These requests are distributed within the Human Network, where verified users complete designated training tasks. This Unique Data Training guarantees that the data used for AI model improvement originates exclusively from real humans, enhancing both accuracy and fairness in AI development.
The training itself is conducted directly on human devices, leveraging decentralized computation. Federated Learning AI enables devices within the network to share computational resources for advanced training. This approach allows AI models to be refined locally on user devices without raw data being transmitted to central servers. Instead, only aggregated updates from the trained models are returned to the AI model organizations and developers. This decentralized and privacy-preserving framework not only strengthens data security but also ensures that AI models are trained on diverse, high-quality human-generated data, fostering ethical and transparent AI development.
The process begins when a user enters the system and undergoes Generation in . During this stage, the user scans their face, and the system employs AI deepfake checking, biometric hashing, and proof of personhood techniques to verify their identity. This ensures that each individual is assigned a singular, non-duplicable InterLink ID, guaranteeing both uniqueness and humaness. As a result, every verified user becomes a Unique Human, collectively forming the Human Network. This decentralized network is designed to eliminate duplicate data and prevent bot infiltration, ensuring that AI training data remains authentic and reliable.
