A new paper from researchers at the University of Southern California’s Brain and Creativity Institute considers a novel path toward creating robots with ‘feelings.’
The key, according to researchers Kinson Man and Antonio Damasio, is homestasis, a self-preservation principle by which living creatures seek to maintain internal biological equilibrium by avoiding certain environments or kinds of stimuli.
Were robots to be programmed with a homeostatic sense of self-preservation, would that put them on a path toward developing true feelings?
According to a Science News report on the paper, Man and Damasio consider the most promising lead for feeling robots to come through the combination of soft robotics and deep learning, which when combined might approximate a homeostatic reaction to negative environmental stimuli.
Man and Domasio point to a 1954 experiment by W. Ross Ashby that demonstrated how homeostatic sensing might be translated into robotics.
Ross designed a system of electric and magnetic sensors, which when disrupted would cycle through a random number of different electromagnetic parameters to try and regain equilibrium.
They believe that using a similar approach to soft robotics, a sub-discipline of robotics that embeds electric sensors and other devices into flexible or soft synthetic material, could lay the foundation for a robot geared toward self-preservation.
Tying such a system to a deep learning program–a branch of artificial intelligence that allows an AI to make independent inferences—could produce a robot able to ‘feel’ differences in how it was being touched and respond accordingly.
Yet, Man and Damasio acknowledge, would such a system actually lead to feeling or simply mechanize it as an elaborate simulacrum? ‘Can all mental phenomena be reduced to information processing, implementable on any arbitrary computing medium?’ they ask in the paper’s conclusion.
‘A computer simulation of a hurricane won’t get us wet,’ they argue, pointing the idea there may be a fundamental divide between human experience and robot experience.
Whether real or simulated, they conclude, developing robots with the capacity for homeostatic behavior might lead to ‘better-behaved autonomy,’ and thus make it easier for humans and robots to negotiate their differences instead of trying to erase them entirely.
HOW DOES ARTIFICIAL INTELLIGENCE LEARN?
AI systems rely on artificial neural networks (ANNs), which try to simulate the way the brain works in order to learn.
ANNs can be trained to recognise patterns in information – including speech, text data, or visual images – and are the basis for a large number of the developments in AI over recent years.
Conventional AI uses input to ‘teach’ an algorithm about a particular subject by feeding it massive amounts of information.
Practical applications include Google’s language translation services, Facebook’s facial recognition software and Snapchat’s image altering live filters.
The process of inputting this data can be extremely time consuming, and is limited to one type of knowledge.
A new breed of ANNs called Adversarial Neural Networks pits the wits of two AI bots against each other, which allows them to learn from each other.
This approach is designed to speed up the process of learning, as well as refining the output created by AI systems.
Source: Dailymail
