On educating machines
arXiv:1909.06017
Abstract
Machine education is an emerging research field that focuses on the problem which is inverse to machine learning. To date, the literature on educating machines is still in its infancy. A fairly low number of methodology and method papers are scattered throughout various formal and informal publication avenues, mainly because the field is not yet well coalesced (with no well established discussion forums or investigation pathways), but also due to the breadth of its potential ramifications and research directions. In this study we bring together the existing literature and organise the discussion into a small number of research directions (out of many) which are to date sufficiently explored to form a minimal critical mass that can push the machine education concept further towards a standalone research field status.
References in corpus (10)
- Explaining and Harnessing Adversarial Examples
- Intriguing properties of neural networks
- FitNets: Hints for Thin Deep Nets
- Towards Deep Neural Network Architectures Robust to Adversarial Examples
- Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
- Deep Generative Stochastic Networks Trainable by Backprop
- Learning to Execute
- An Overview of Machine Teaching
- A Review of Theoretical and Practical Challenges of Trusted Autonomy in Big Data
- A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents