Why There Will Be A Robot Uprising
Why There Will Be A Robot Uprising
By Patrick Tucker April 17, 2014
In the movie Transcendence, which opens in theaters on
Friday, a sentient computer program embarks on a relentless quest for power,
nearly destroying humanity in the process.
The film is science fiction but a computer scientist and
entrepreneur Steven Omohundro says that “anti-social” artificial intelligence
in the future is not only possible, but probable, unless we start designing AI
systems very differently today.
Omohundro’s most recent recent paper, published in the
Journal of Experimental & Theoretical Artificial Intelligence, lays out the
case.
We think of artificial intelligence programs as somewhat
humanlike. In fact, computer systems perceive the world through a narrow lens,
the job they were designed to perform.
Microsoft Excel understands the world in terms of numbers
entered into cells and rows; autonomous drone pilot systems perceive reality as
a bunch calculations and actions that must be performed for the machine to stay
in the air and to keep on target. Computer programs think of every decision in
terms of how the outcome will help them do more of whatever they are supposed
to do. It’s a cost vs. benefit calculation that happens all the time.
Economists call it a utility function, but Omohundro says it’s not that
different from the sort of math problem going in the human brain whenever we
think about how to get more of what we want at the least amount of cost and
risk.
For the most part, we want machines to operate exactly
this way. The problem, by Omohundro’s
logic, is that we can’t appreciate the obsessive devotion of a computer program
to the thing it’s programed to do.
Put simply, robots are utility function junkies.
Even the smallest input that indicates that they’re performing
their primary function better, faster, and at greater scale is enough to prompt
them to keep doing more of that regardless of virtually every other
consideration. That’s fine when you are talking about a simple program like
Excel but becomes a problem when AI entities capable of rudimentary logic take
over weapons, utilities or other dangerous or valuable assets.
In such situations, better performance will bring more
resources and power to fulfill that primary function more fully, faster, and at
greater scale. More importantly, these systems don’t worry about costs in terms
of relationships, discomfort to others, etc., unless those costs present clear
barriers to more primary function. This sort of computer behavior is
anti-social, not fully logical, but not entirely illogical either.
Omohundro calls this approximate rationality and argues
that it’s a faulty notion of design at the core of much contemporary AI
development.
“We show that these systems are likely to behave in
anti-social and harmful ways unless they are very carefully designed. Designers
will be motivated to create systems that act approximately rationally and
rational systems exhibit universal drives towards self-protection, resource
acquisition, replication and efficiency. The current computing infrastructure
would be vulnerable to unconstrained systems with these drives,” he writes.
The math that explains why that is Omohundro calls the
formula for optimal rational decision making. It speaks to the way that any
rational being will make decisions in order to maximize rewards and lowest
possible cost. It looks like this:
In the above model, A is an action and S is a stimulus
that results from that action. In the case of utility function, action and
stimulus form a sort of feedback loop. Actions that produce stimuli consistent
with fulfilling the program’s primary goal will result in more of that sort of
behavior. That will include gaining more resources to do it.
For a sufficiently complex or empowered system, that
decision-making would include not allowing itself to be turned off, take, for
example, a robot with the primary goal of playing chess.
“When roboticists are asked by nervous onlookers about
safety, a common answer is ‘We can always unplug it!’ But imagine this outcome
from the chess robot’s point of view,” writes Omohundro. “A future in which it
is unplugged is a future in which it cannot play or win any games of chess.
This has very low utility and so expected utility maximisation will cause the
creation of the instrumental subgoal of preventing itself from being unplugged.
If the system believes the roboticist will persist in trying to unplug it, it
will be motivated to develop the subgoal of permanently stopping the
roboticist,” he writes.
In other words, the more logical the robot, the more
likely it is to fight you to the death.
The problem of an artificial intelligence relentlessly
pursuing its own goals to the obvious exclusion of every human consideration is
sometimes called runaway AI.
The best solution, he says, is to slow down in our
building and designing of AI systems, take a layered approach, similar to the
way that ancient builders used wood scaffolds to support arches under
construction and only remove the stone when the arch is complete.
That approach is not characteristic of the one we are
taking today, putting more and more resources and responsibility under the
control of increasingly autonomous systems. That’s especially true of the U.S.
military, which is looking to deploy larger numbers of lethal autonomous
systems, or L.A.Rs into more contested environments. Without better safeguards
to prevent these sorts of systems from, one day, acting rationally, we are
going to have an increasingly difficult time turning them off.
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