# One-Trial Correction of Legacy AI Systems and Stochastic Separation Theorems

arXiv: https://arxiv.org/abs/1610.00494 · HTML: https://mlsanity.com/paper/1610.00494/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2016.

## Built on by (most cited first, 2 of 2)

- [Weakly Supervised Learners for Correction of AI Errors with Provable Performance Guarantees](https://mlsanity.com/paper/2402.00899/) (2024)
- [MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images](https://mlsanity.com/paper/2212.13715/) (2022)

## Abstract (arXiv)

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.