I will start a short paper review series here in The Information Age. I have got some difficulty in reviewing papers lately, something that needs further improvement regarding organizational issues. In my defense I would just say that I do not want to do this in a way that isn’t proper and rigorous, with the proper analysis and conclusion. Also I’ve chosen not to have a structured criteria for the papers to review in this Blog, and that puts a pressure on the choosing task, as the sheer immense number of possible candidates is ming bending, to say the least. Anyway the topics revolve around computational and information theory and sciences papers, the readers my have already noticed, with the addition of some business, economics and finance topics.
Having said this, I decided to do a series of short paper reviews, where I try to provide the overall idea of the paper, the abstract and some comments, the very starting basic points in the paper, skipping the technical details, but always recommending all interested to further read (if the paper is fully available), or purchase it with the publisher.
The first installment in this series is a topic around artificial intelligence and the more specific but deeply interesting, if not very important, topic of robotic self-consciousness. This topic is interesting from an overall intelligence perspective; in our age of advanced scientific and technological capacities, we still lack an appropriate theoretical and consensual conceptual apparatus to explain satisfactorily human consciousness. But we nevertheless have done efforts in that direction, with some prominent thinkers, and with the help of the hard sciences like neuroscience and physics in the fray, empirically trying to test and validate hypothesis.
The question of the possibility of artificial consciousness is another matter. Whether inorganic matter ever is conscious or not is an interesting philosophical issue by its own right. But the paper I present here today proposes a line of inquiry that could in the end lead us to better answers to philosophical conundrums, in the process provide insight not only to the artificial consciousness topic, but also to the human consciousness topic as well. Using the knowledge we already have about the human brain we could indeed be in the right direction to achieve such a high stakes goal.
This paper appears to be a contribution in that direction as it is a brain-inspired robot mirror neuron model applied to mirror self-recognition. Self-recognition is one of central tenets of intelligence and high level consciousness, so any hope tha we one day will fully understand consciousness – artificial or human – must grapple with the details of this subject.
The paper appears in the publication Advances in Brain Inspired Cognitive Systems by Springer, which is actually a collection of papers gathered as a volume book that is accessible in the Google Book collection, that I share here as a link below after the abstract from Springer Link:
Towards Robot Self-consciousness (I): Brain-Inspired Robot Mirror Neuron System Model and Its Application in Mirror Self-recognition
Mirror Self-Recognition is a well accepted test to identify whether an animal is with self-consciousness. Mirror neuron system is believed to be one of the most important biological foundation for Mirror Self-Recognition. Inspired by the biological mirror neuron system of the mammalian brain, we propose a Brain-inspired Robot Mirror Neuron System Model (Robot-MNS-Model) and we apply it to humanoid robots for mirror self-recognition. This model evaluates the similarity between the actual movements of robots and their visual perceptions. The association for self-recognition is supported by STDP learning which connects the correlated visual perception and motor control. The model is evaluated on self-recognition mirror test for 3 humanoid robots. Each robot has to decide which one is itself after a series of random movements facing a mirror. The results show that with the proposed model, multiple robots can pass the self-recognition mirror test at the same time, which is a step forward towards robot self-consciousness.
STDP learning stands for Spike-timing-dependent-plasticity and is a neuroscientific topic that seems to be important to machine intelligence and self-consciousness in particular also, even if we acknowledge that self-consciousness and consciousness in general may need a broader set of assumptions and concepts to be fully understood.
It is noteworthy that the model proposed in this paper provide results that indicate its use as a step forward towards robot self-consciousness. Further confirmations of this will definitely be needed. But results such as this one can only give assurances as what the right direction will be in future research in this area.
featured image: Advances in Brain Inspired Cognitive Systems