Author:
Frédéric Alexandre
Affiliation:
INRIA Bordeaux Sud-Ouest, LaBRI and Institut des Maladies Neurodégénératives, France
Keyword(s):
Machine Learning, Computational Neuroscience, Autonomous Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Computational Neuroscience
;
Health Engineering and Technology Applications
;
Higher Level Artificial Neural Network Based Intelligent Systems
;
Human-Computer Interaction
;
Learning Paradigms and Algorithms
;
Methodologies and Methods
;
Neural Multi-Agent Intelligent Systems and Applications
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
Abstract:
Recently, Machine Learning has achieved impressive results, surpassing human performances, but these powerful
algorithms are still unable to define their goals by themselves or to adapt when the task changes. In
short, they are not autonomous. In this paper, we explain why autonomy is an important criterion for really
powerful learning algorithms. We propose a number of characteristics that make humans more autonomous
than machines when they learn. Humans have a system of memories where one memory can compensate
or train another memory if needed. They are able to detect uncertainties and adapt accordingly. They are
able to define their goals by themselves, from internal and external cues and are capable of self-evaluation to
adapt their learning behavior. We also suggest that introducing these characteristics in the domain of Machine
Learning is a critical challenge for future intelligent systems.