OG Labs, the earliest modular AI chain for dApps, has recently started an exclusive partnership. The company revealed that it has collaborated with Pond (an artificial intelligence-driven neural network) to provide exclusive features to the community members. In this respect, the platform discussed the Graphic Neural Network ecosystem of Pond.
OG Labs and AI-Driven Pond Collaborate to Offer Data Storage and AI-Model Compatibility
On its official account on X, the firm disclosed that Pond’s GNN ecosystem needs a hyper-scalable data availability solution. As per it, the solution also needs to offer data storage along with complete interoperability with other AI models. In addition to this, the company noted that the networks and the dApps have interconnected and complicated relationships with users.
The respective relationships are similar to the structures of neural networks. To deal with this, the GNNs play a significant role. These lie within the category of an unconventional AI model type of AI models. It reportedly comprises nodes (e.g. a wallet/user) and edges (denoting relationships). Artificial intelligence permits the analysis of the respective complicated relationships.
The Move Offers Many Exclusive Possibilities
This development paves the way for node classification, graph generation, graph classification, and link prediction. Though Pond possesses its separate models, others can reportedly develop their own. This can result in a merger for more optimization. This provides several possibilities such as SocialFi & Gaming, On-Chain Marketing, Security, Financial AI, and Price Predictions.
Some other things are the in-game action examination, recommendation engines, strange behavior detection, liquidity optimization, and sentiment analysis. Nonetheless, to turn this into reality, there is a requirement for a highly scalable infrastructure for data storage. At this point, OG jumps in and permits the storage and rapid retrieval of huge data amounts. OG operates for each Web2 data type, providing data with a very quick retrieval.
