Since “2001: A Space Odyssey,” people have wondered: could machines like HAL 9000 eventually exist that can process information with human-like intelligence?
Copyright by msutoday.msu.edu
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 .
“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 . 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.
“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. […]