IAN helps its users learn, connect, and build in a more effective manner through a collaborative community intelligence model that learns and adapts in real time.
IAN simultaneously aggregates a network of intelligence sources (AI's and users) to optimize interactions, alleviate information overload, and keep AI systems accountable, personalized, and safe.
IAN-5 is designed to connect and optimize diverse AI agents. Just as the human brain utilizes different regions for various cognitive tasks, AI agents in IAN are specialized and trained for distinct functions. This synergy mirrors our own neurological processes, enabling a more nuanced and effective approach to problem-solving. By leveraging a diverse network of specialized AI agents, IAN delivers precise, unbiased insights tailored to meet a wide range of needs.
By aggregating outputs from various AI agents, IAN ensures that users receive the most accurate insights available. It detects and highlights biases and inconsistencies across different AI models, making it easier for users to trust the information they receive. IAN offers a smarter, fairer AI experience by leveraging the strengths of diverse AI agents in one seamless interface.
In this demonstration, IAN-5 compared the outputs of three different AI models—GPT-4o, Gemini Pro, and Llama v3—on their ability to accurately report Michael Phelps' Olympic medal count. By highlighting the strengths, weaknesses, and discrepancies in their responses, we showcased the importance of aggregating multiple models to enhance transparency and interoperability in AI systems. This approach enables a more robust and reliable AI ecosystem, ensuring more accurate and comprehensive information for users.
