Michigan State University – September 20,
2019 -Since "2001: A Space Odyssey," people have wondered: could
machines like HAL 9000 eventually exist that can process information with
human-like intelligence?
Researchers at Michigan State University
say that true, human-level intelligence remains a long way off, but their new
paper published in The American Naturalist explores how computers could
begin to evolve learning in the same way as natural organisms did -- with
implications for many fields, including artificial intelligence.
"We know that all organisms are
capable of some form of learning, we just weren't sure how those abilities
first evolved. Now we can watch these major evolutionary events unfold before
us in a virtual world," said Anselmo Pontes, MSU computer science
researcher and lead author.
"Understanding how learning behavior evolved
helps us figure out how it works and provides insights to other fields such as
neuroscience, education, psychology, animal behavior, and even AI. It also
supplies clues to how our brains work and could even lead to robots that learn
from experiences as effectively as humans do."
According to Fred Dyer, MSU integrative
biology professor and co-author, these findings have the potential for huge
implications.
"We're untangling the story of how
our own cognition came to be and how that can shape the future," Dyer
said. "Understanding our own origins can lead us to developing robots that
can watch and learn rather than being programmed for each individual
task."
The results are the first demonstration
that shows the evolution of associative learning in an artificial organism
without a brain. Here is a video showing the process.
"Our inspiration was the way
animals learn landmarks and use them to navigate their environments,"
Pontes said. "For example, in laboratory experiments, honeybees learn to
associate certain colors or shapes with directions and navigate complex
mazes."
Since the evolution of learning cannot
be observed through fossils -- and would take more than a lifetime to watch in
nature -- the MSU interdisciplinary team composed of biologists and computer
scientists used a digital evolution program that allowed them to observe tens
of thousands of generations of evolution in just a few hours, a feat
unachievable with living systems.
In this case, organisms evolved to learn
and use environmental signals to help them navigate the environment and find
food.
"Learning is crucial to most
behaviors, but we couldn't directly observe how learning got started in the
first place from our purely instinctual ancestors," Dyer said. "We
built in various selection pressures that we thought might play a role and
watched what happened in the computer."
While the environment was simulated, the
evolution was real. The programs that controlled the digital organism were
subject to genetic variation from mutation, inheritance and competitive
selection. Organisms were tasked to follow a trail alongside signals that -- if
interpreted correctly -- pointed where the path went next.
In the beginning of the simulation,
organisms were "blank slates," incapable of sensing, moving or
learning. Every time an organism reproduced, its descendants could suffer
mutations that changed their behavior. Most mutations were lethal. Some did
nothing. But the rare traits that allowed an organism to better follow the
trail resulted in the organism collecting more resources, reproducing more
often and, thus, gaining share in the population.
Over the generations, organisms evolved
more and more complex behaviors. First came simple movements allowing them to
stumble into food. Next was the ability to sense and distinguish different
types of signals, followed by the reflexive ability to correct errors, such as
trying an incorrect path, backing up and trying another.
A few organisms evolved the ability to
learn by association. If one of these organisms made a wrong turn it would
correct the error, but it would also learn from that mistake and associate the
specific signal it saw with the direction it now knew it should have gone. From
then on, it would navigate the entire trail without any further mistakes. Some
organisms could even relearn when tricked by switching signals mid-trail.
"Evolution in nature might take too
long to study," Pontes said, "but evolution is just an algorithm, so
it can be replicated in a computer. We were not just able to see how certain
environments fostered the evolution of learning, but we saw populations evolve
through the same behavioral phases that previous scientists speculated should
happen but didn't have the technology to see."
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