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Fiction is full of robots with feelings.
Like that emotional kid David, played by Haley Joel Osment, in the movie A.I. Or WALL•E, who obviously had feelings for EVE-uh. The robot in Lost in Space sounded pretty emotional whenever warning Will Robinson of danger. Not to mention all those emotional train-wreck, wackadoodle robots on Westworld.
But in real life robots have no more feelings than a rock submerged in novocaine.
There might be a way, though, to give robots
feelings, say neuroscientists Kingson Man and Antonio Damasio. Simply build the
robot with the ability to sense peril to its own existence. It would then have
to develop feelings to guide the behaviors needed to ensure its own survival.
“Today’s robots lack
feelings,” Man and Damasio write in a new paper (subscription
required) in Nature Machine Intelligence. “They are not designed to represent the internal
state of their operations in a way that would permit them to experience that
state in a mental space.”
So Man and Damasio propose a strategy for
imbuing machines (such as robots or humanlike androids) with the “artificial
equivalent of feeling.” At its core, this proposal calls for machines designed to
observe the biological principle of homeostasis. That’s the idea that life must
regulate itself to remain within a narrow range of suitable conditions — like keeping
temperature and chemical balances within the limits of viability. An
intelligent machine’s awareness of analogous features of its internal state
would amount to the robotic version of feelings.
Such feelings would not only motivate
self-preserving behavior, Man and Damasio believe, but also inspire artificial intelligence
to more closely emulate the real thing.
Typical “intelligent” machines are designed to
perform a specific task, like diagnosing diseases, driving a car, playing Go or
winning at Jeopardy! But intelligence in one arena isn’t the same as the
more general humanlike intelligence that can be deployed to cope with all sorts
of situations, even those never before encountered. Researchers have long
sought the secret recipe for making robots smart in a more general way.
In Man and Damasio’s view, feelings are the
missing ingredient.
Feelings arise from the need to survive. When
humans maintain a robot in a viable state (wires all connected, right amount of
electric current, comfy temperature), the robot has no need to worry about its
own self-preservation. So it has no need for feelings — signals that something
is in need of repair.
Feelings motivate
living things to seek optimum states for survival, helping to ensure that
behaviors maintain the necessary homeostatic balance. An intelligent machine
with a sense of its own vulnerability should similarly act in a way that would
minimize threats to its existence.
To perceive
such threats, though, a robot must be designed to understand its own internal
state.
Man and
Damasio, of the University of Southern California, say the prospects for
building machines with feelings have been enhanced by recent developments in
two key research fields: soft robotics and deep learning. Progress in soft
robotics could provide the raw materials for machines with feelings. Deep
learning methods could enable the sophisticated computation needed to translate
those feelings into existence-sustaining behaviors.
Deep learning
is a modern descendant of the old idea of artificial neural networks — sets of
connected computing elements that mimic the nerve cells at work in a living
brain. Inputs into the neural network modify the strengths of the links between
the artificial neurons, enabling the network to detect patterns in the inputs.
Deep
learning requires multiple neural network layers. Patterns in one layer exposed
to external input are passed on to the next layer and then on to the next,
enabling the machine to discern patterns in the patterns. Deep learning can classify
those patterns into categories, identifying objects (like cats) or determining
whether a CT scan reveals signs of cancer or some other malady.
An
intelligent robot, of course, would need to identify lots of features in its
environment, while also keeping track of its own internal condition. By representing
environmental states computationally, a deep learning machine could merge
different inputs into a coherent assessment of its situation. Such a smart
machine, Man and Damasio note, could “bridge
across sensory modalities” — learning, for instance, how lip movements (visual
modality) correspond to vocal sounds (auditory modality).
Similarly, that robot
could relate external situations to its internal conditions — its feelings, if
it had any. Linking external and internal conditions “provides a crucial piece
of the puzzle of how to intertwine a system’s internal homeostatic states with
its external perceptions and behavior,” Man and Damasio note.
Ability to sense
internal states wouldn’t matter much, though, unless the viability of those states
is vulnerable to assaults from the environment. Robots made of metal do not
worry about mosquito bites, paper cuts or indigestion. But if made from proper
soft materials embedded with electronic sensors, a robot could detect such
dangers — say, a cut through its “skin” threatening its innards — and engage a
program to repair the injury.
A robot
capable of perceiving existential risks might learn to devise novel methods for
its protection, instead of relying on preprogrammed solutions.
“Rather than having to hard-code a
robot for every eventuality or equip it with a limited set of behavioral
policies, a robot concerned with its own survival might creatively solve the
challenges that it encounters,” Man and Damasio suspect. “Basic goals and
values would be organically discovered, rather than being extrinsically
designed.”
Devising novel
self-protection capabilities might also lead to enhanced
thinking skills. Man and Damasio believe advanced human thought may have
developed in that way: Maintaining viable internal states (homeostasis)
required the evolution of better brain power. “We regard high-level
cognition as an outgrowth of resources that originated to solve the ancient biological
problem of homeostasis,” Man and Damasio write.
Protecting
its own existence might therefore be just the motivation a robot needs to
eventually emulate human general intelligence. That motivation is reminiscent
of Isaac Asimov’s famous laws of robotics: Robots must protect humans,
robots must obey humans, robots must protect themselves. In Asimov’s fiction,
self-protection was subordinate to the first two laws. In real-life future
robots, then, some precautions might be needed to protect people from
self-protecting robots.
“Stories about robots
often end poorly for their human creators,” Man and Damasio acknowledge. But
would a supersmart robot (with feelings) really pose Terminator-type dangers?
“We suggest not,” they say, “provided, for example, that in addition to having
access to its own feelings, it would be able to know about the feelings of
others — that is, if it would be endowed with empathy.”
And so Man and Damasio
suggest their own rules for robots: 1. Feel good. 2. Feel empathy.
“Assuming a robot
already capable of genuine feeling, an obligatory link between its feelings and
those of others would result in its ethical and sociable behavior,” the
neuroscientists contend.
That
might just seem a bit optimistic. But if it’s possible, maybe there’s hope for
a better future. If scientists do succeed in instilling empathy in robots,
maybe that would suggest a way for doing it in humans, too.
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