The problem Cognition solves

Cognitive AI is a machine learning tool designed for blockchain technology to protect copyrights. The motivation behind this project is to prevent unauthorized use and distribution of copyrighted materials in the digital world. By leveraging blockchain's immutable and decentralized nature, Cognitive AI aims to enforce copyright ownership and usage rights more effectively.

Challenges we ran into

When creating the application that utilizes vector database, FastAPI, Solidity, and TensorFlow, we encountered several challenges that required careful consideration and planning. One of the most significant challenges we faced was learning how to implement blockchain technology effectively. The decentralized and immutable nature of blockchain required us to rethink traditional data storage and retrieval mechanisms, which presented a steep learning curve.

Another key challenge we encountered was embedding base 64 strings that represented images into vector database. We had to find a way to convert these images into numerical representations that could be processed by machine learning algorithms. To overcome this challenge, we researched and experimented with different techniques to create embeddings for these images, such as using deep learning models and pre-trained embeddings.

Furthermore, we used IPFS (InterPlanetary File System) to create unique IDs for the images stored in vector database. We had to learn how to integrate the IPFS system into our application and ensure that it functioned seamlessly with our backend infrastructure. This involved researching IPFS best practices and experimenting with different configurations to achieve optimal performance and stability.

Lastly, we had to create a Solidity smart contract that would enforce copyright ownership and usage rights. We had to learn Solidity's syntax and semantics and design a contract that could effectively enforce copyright laws while also being efficient and scalable.

Overall, the challenges we faced when creating this application were complex and required careful attention to both perplexity and burstiness. By leveraging the power of TensorFlow, vector database, Solidity, and FastAPI, we were able to overcome these challenges and create a robust and scalable application that effectively addresses the challenge of copyright protection in the digital age