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Knowledge Transfer Between Artificial Intelligence Systems.

Ivan Y Tyukin^1,2, Alexander N Gorban^1,2, Konstantin I Sofeykov^1,3

¹Department of Mathematics, University of Leicestger, Leicester, United Kingdom.

Frontiers in Neurorobotics

|August 29, 2018

Summary

This summary is machine-generated.

Artificial Intelligence (AI) knowledge transfer enables a student AI to learn from a teacher AI or human expert without retraining. This efficient method uses linear functionals for AI systems with high-dimensional internal structures.

Keywords:

concentration of measure error correction knowledge transfer in artificial intelligence systems neural networks stochastic separation theorems supervised learning

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Area of Science:

Artificial Intelligence
Machine Learning
Computational Neuroscience

Background:

Legacy AI systems often lack efficient knowledge transfer mechanisms.
Retraining AI models is computationally expensive and time-consuming.
Mimicking responses between AI systems or human experts is a key challenge.

Purpose of the Study:

To investigate efficient Artificial Intelligence (AI) knowledge transfer between systems.
To enable learning without retraining or significant computational resources.
To explore AI learning through response mimicry.

Main Methods:

Utilizing the structure of n-dimensional topological vector spaces for AI internal variables.
Implementing knowledge transfer via cascades of linear functionals.
Demonstrating the concept with pre-trained convolutional neural networks.

Main Results:

Sufficiently high dimensionality (n) allows AI knowledge transfer with high probability.
Learning new examples or correcting mistakes is non-iterative and efficient.
The process requires minimal computational cost, specifically two additional inner products.

Conclusions:

Efficient AI knowledge transfer is achievable in high-dimensional systems.
Linear functionals offer a pathway for rapid, low-resource AI learning.
This approach facilitates seamless integration of knowledge between AI models.