One-Trial Correction of Legacy AI Systems and Stochastic Separation Theorems
This addresses the need for quick error correction in existing AI systems, particularly for applications like image detection, though it is incremental as it builds on existing methods.
The paper tackles the problem of efficiently tuning legacy AI systems without retraining by adding a cascade of perceptron nodes to modulate decisions, achieving improved performance as demonstrated in fine-tuning a pre-trained pedestrian detection network.
We consider the problem of efficient "on the fly" tuning of existing, or {\it legacy}, Artificial Intelligence (AI) systems. The legacy AI systems are allowed to be of arbitrary class, albeit the data they are using for computing interim or final decision responses should posses an underlying structure of a high-dimensional topological real vector space. The tuning method that we propose enables dealing with errors without the need to re-train the system. Instead of re-training a simple cascade of perceptron nodes is added to the legacy system. The added cascade modulates the AI legacy system's decisions. If applied repeatedly, the process results in a network of modulating rules "dressing up" and improving performance of existing AI systems. Mathematical rationale behind the method is based on the fundamental property of measure concentration in high dimensional spaces. The method is illustrated with an example of fine-tuning a deep convolutional network that has been pre-trained to detect pedestrians in images.