We’ve developed a sophisticated system for handling temporal and semantic information in our Kruel.AI, leveraging several techniques to ensure the most relevant and predictive data is pulled.
Our approach involves:
- Cosine Similarity Over Multiple Data Points:
- We use cosine similarity extensively to measure the semantic similarity between different data points. This helps in identifying and ranking the most contextually relevant memories or information pieces based on their vector representations allowing for a more dimensional graph.
- Temporal Encodings:
- Temporal aspects are encoded alongside semantic embeddings, ensuring that the recency of information is taken into account. This is akin to combining cosine similarity with a temporal decay function to prioritize more recent data without discarding valuable older information.
- Clustering Techniques:
- Clustering methods are employed to group similar data points together. This helps in reducing noise and improving the efficiency of data retrieval. By clustering, we can manage and retrieve large volumes of information more effectively.
- Neural Network Relationship System:
- Our system uses a neural network-based relationship mechanism to understand and map the relationships between different pieces of information. This network helps in dynamically adjusting the relevance scores based on the evolving context and data patterns.
- Hybrid Scoring System:
- We implement a hybrid scoring system that combines the cosine similarity score with a temporal scoring function. This ensures that both the semantic relevance and the temporal proximity are considered in a balanced manner.
These methods along with some others collectively allow our system to retrieve the most contextually appropriate and temporally relevant data, making it highly effective in dynamic and real-time applications.
I found several of the ideas discussed above here very intriguing and believe they could further enhance our system. In particular the concepts around more sophisticated temporal encodings and adaptive nearest neighbor algorithms, in particular, offer promising avenues for expanding our current capabilities. It’s always exciting to see innovative approaches. Does make me cry a little though haha every new idea means more coding… I think AI is a life project haha.