AN AI Powerhouse
Last updated
Last updated
Addressing Challenges:
User Data Sovereignty: Protecting user's data ownership rights.
Social Authenticity: Measurable social trust and authentic interactions.
Trusted Users: Auditable trust profiles and provable social authenticity
User Data Privacy: Safeguarding end user rights to data privacy.
ML Pipelines: Continuous data quality improvement and measures of user authenticity.
Combating Misinformation: Tackle fake, inappropriate, and low-quality content.
Preventing Manipulation: Safeguarding users against fake accounts and manipulative practices.
Counter Weaponization: Addressing rogue influences.
Anti-Authoritarianism: Keeping social media unbiased and authentic.
Restoring Faith: Rebuilding trust in online social media interactions.
Detection and Prevention:
We have developed groundbreaking AI/ML pipelines with unique concepts.
Existing Social Media platforms have been slow to adapt to this rise in misinformation partly perhaps due to the challenges of integrating these new AI techniques into long-established traditional recommender pipelines.
NN/ML/LLM Powerhouse:
Our comprehensive architecture combines Neural Networks (NN), Machine Learning (ML), Large Language Models (LLM) components, seamlessly integrated within a diverse range of features.
This sophisticated architecture aims to analyze data garnered from the mobile app, encompassing profile details, posts, reviews, comments, and the follower graph. Through distinctive ML pipelines, the system aims to deliver two key outcomes:
Categorized trust rankings play a crucial role in evaluating the reliability and authenticity of users, services, or content within a system.
Tailored recommendations spanning various facets of the mobile app, including the timeline and suggested connections. This intricate framework represents a cutting-edge approach to harnessing data for enhanced user experience and engagement.
In the sections below we provide a very high-level description of the elements of this architecture diagram, split into 3 sections:
Data: Collected from the mobile app and external sources.
Features: Generated variables from data.
Recommendations: Aggregated ML analysis for post recommendations and suggested connections.
ML Pipeline: Recommenders Stage This stage of the ML pipeline takes features generated from the previous stage and combines them and uses them as input to the recommender algorithms along with user graph data/ This produces specific recommendations of content for various sections of the mobile application. This stage has sub-modules tuned specifically for the type of recommendations required.
