- Parallelized Execution
Unlike many other models in current research, this model has been mathematically restructured to be fully parallelizable. It can take complete advantage of modern hardware parallelization like CUDA to train and retrieve memories in close to real time. This allows for the model to be used in real-world applications like robotics and data analysis, where speed is of the essence. - Neural Structure Modeling
This algorithm closely mimics the human brain's ability to abstractify and store knowledge by using a structure similar to the brain's hippocampus. This structure helps create local and global neural stimulation that contribute to the formation of knowledge-rich memories. The scalability of this memory model also allows for storage of multitudes more memories than the current state-of-the-art memory models. - Higher Retrieval Accuracy
The novel architecture of this algorithm drastically reduces the training and retreival loss of the memory model, vastly outcompeting the existing models published in research. This improves the performance of the model in real-world applications, allowing for more accurate and efficient data retrieval. Furthermore, the per-memory average loss of the model decreases with the size of the model, proving the importance of the algorithmic optimizations in the model. - Parallelized ExecutionUnlike many other models in current research, this model has been mathematically restructured to be fully parallelizable. It can take complete advantage of modern hardware parallelization like CUDA to train and retrieve memories in close to real time. This allows for the model to be used in real-world applications like robotics and data analysis, where speed is of the essence.
- Neural Structure ModelingThis algorithm closely mimics the human brain's ability to abstractify and store knowledge by using a structure similar to the brain's hippocampus. This structure helps create local and global neural stimulation that contribute to the formation of knowledge-rich memories. The scalability of this memory model also allows for storage of multitudes more memories than the current state-of-the-art memory models.
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Development
Due to the cost and complexities with the project in addition to the legalities of intellectual property, this project is currently closed-source until the necessary legal protections are in place. Being nearly in a completed state, this algorithm is ready to be fused with other research projects at DeepShift Labs to achieve our vision statement.
Features
Once published, this algorithm will boast Python and C++ bindings making it available for easy integration and high performance applications. It currently supports on-compute graph caching and GPU integration to optimize the execution time. It also has memory generation capabilities allowing for dynamic memory generation molded by the nature of the data. Finally, it has mathematical and software guards inplace to prevent undefined behaviour and enhancing stability during prolonged operation.