I will upload the article . Please find the at lest three main point from the contents of article ,and explain these main point use own words. please wirte down about at least 180 words.if you can’t see the file You can search the title which is”How can humans keep the upper hand on artificial intelligence?
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One machine-learning method used in AI is
reinforcement learning, where agents are rewarded
for performing certain actions — a technique
borrowed from behavioral psychology. Applying this
technique to AI, engineers use a points system
where machines earn points by carrying out the
right actions. For instance, a robot may earn one
point for correctly stacking a set of boxes and
another point for retrieving a box from outside. But
if, on a rainy day for example, a human operator
interrupts the robot as it heads outside to collect a
box, the robot will learn that it is better off staying
indoors, stacking boxes and earning as many points
as possible. “The challenge isn’t to stop the robot,
but rather to program it so that the interruption
doesn’t change its learning process — and doesn’t
induce it to optimize its behavior in such a way as
to avoid being stopped,” says Guerraoui.
From a single machine to an entire AI network
In 2016, researchers from Google DeepMind and
the Future of Humanity Institute at Oxford
University developed a learning protocol that
prevents machines from learning from
interruptions and thereby becoming uncontrollable.
For instance, in the example above, the robot’s
reward — the number of points it earns — would
be weighted by the chance of rain, giving the robot
a greater incentive to retrieve boxes outside. “Here
the solution is fairly simple because we are dealing
with just one robot,” says Guerraoui.
However, AI is increasingly being used in
applications involving dozens of machines, such as
self-driving cars on the road or drones in the air.
“That makes things a lot more complicated, because
the machines start learning from each other -especially in the case of interruptions. They learn
not only from how they are interrupted
individually, but also from how the others are
interrupted,” says Alexandre Maurer, one of the
study’s authors. Hadrien Hendrikx, another
researcher involved in the study, gives the example
of two self-driving cars following each other on a
narrow road where they can’t pass each other.
They must reach their destination as quickly as
possible — without breaking any traffic laws -and humans in the cars can take over control at
any time. If the human in the first car brakes often,
the second car will adapt its behavior each time
and eventually get confused as to when to brake,
possibly staying too close to the first car or driving
Giving humans the last word
This complexity is what the EPFL researchers aim
to resolve through “safe interruptibility.” Their
breakthrough method lets humans interrupt AI
learning processes when necessary — while making
sure that the interruptions don’t change the way
the machines learn. “Simply put, we add
‘forgetting’ mechanisms to the learning algorithms
that essentially delete bits of a machine’s memory.
It’s kind of like the flash device in Men in Black,”
says El Mahdi El Mhamdi, another author of the
study. In other words, the researchers altered the
machines’ learning and reward system so that it’s
not affected by interruptions. It’s like if a parent
punishes one child, that doesn’t affect the learning
processes of the other children in the family.
“We worked on existing algorithms and showed
that safe interruptibility can work no matter how
complicated the AI system is, the number of robots
involved, or the type of interruption. We could use
it with the Terminator and still have the same
results,” says Maurer.
Today, autonomous machines that use
reinforcement learning are not common. “This
system works really well when the consequences of
making mistakes are minor,” says El Mhamdi. “In
full autonomy and without human supervision, it
couldn’t be used in the self-driving shuttle buses in
Sion, for instance, for safety reasons. However, we
could simulate the shuttle buses and the city of Sion
and run an AI algorithm that awards and subtracts
points as the shuttle-bus system learns. That’s the
kind of simulation that’s being done at Tesla, for
example. Once the system has undergone enough of
this learning, we could install the pre-trained
algorithm in a self-driving car with a low
exploration rate, as this would allow for more
widespread use.” And, of course, while making sure
humans still have the last word.
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