Electricity Helps Find Materials That Can “Learn”

A team of scientists at Argonne National Laboratory were able to observe the behavior of non-living material mimics associated with learning, which they say could lead to better artificial intelligence (AI) systems.

A paper describing the study was published Advanced Intelligent Systems.

The group aims to develop the next generation of supercomputers and is looking to the human brain for inspiration.

Learning Non-Biological Materials, Like Behaviors

Researchers looking for brain-inspired computers often turn to non-biological materials that suggest they can adopt similar learning behaviors. These materials were used to build hardware that could be combined with new software algorithms, resulting in more efficient AI.

A new study was conducted by scientists from Purdue University. They exposed oxygen-deficient nickel oxide to short electrical pulses and elicited two electrical responses similar to learning. According to Rutgers University professor Shriram Ramanathan, who was a professor at Purdue University at the time of the work, they came up with an electrically-driven system that demonstrated learning behavior.

The research team is supported by the Advanced Photon Source (APS), a US Department of Energy (DOE) Science Faculty at DOE’s Argonne National Laboratory.

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Behavior and Sensitization

The first response that occurs is habituation, which occurs when the habituated material is slightly zapped. Although the resistance of the material increases after the first salber, the researchers noticed that it was used with electrical impulses.

Fanny Rodolakis is a scientist and beamline scientist at APS.

“It’s like a habit that happens when you live near an airport,” Rodolakis says. “You move the day, what do you think of the net,” but eventually you hardly notice anymore.

The second answer shows that the material is sensitization, which occurs when a larger dose of electricity is administered.

“With greater stimulus, the material’s response increases instead of decreasing over time,” says Rodolakis. “It’s akin to watching a scary movie, and then having someone say ‘boo!’ after the corner – you see it really jump.”

“Pretty much all living organisms exhibit these two characteristics,” Ramanathan continues. “There are really fundamental aspects of intelligence.”

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The two behaviors are governed by the quantum interactions that occur between the electrons. These interactions cannot be described by classical physics, and they play a fundamental role in shaping time transitions in matter.

“An example of a phase transition is a liquid-solid,” says Rodolakis. “The matter that we observe at the boundary is straight and competitive interactions that take place at the electronic level are easily set one way or another by small stimuli.”

According to Ramanathan, it is necessary to have a system that can be completely controlled by electrical signals.

“Being able to handle materials in this way will allow the hardware to take some responsibility for intelligence,” he said. “Using quantum properties as intelligence in hardware represents a key step toward energy efficient computing.”

Overcoming the Stability-Plasticity Dilemma

Scientists can use the difference between habituation and sensitization to overcome the stability-plasticity dilemma, which is a major challenge in AI development. Algorithms often struggle to adapt to new information, and when they do, they often forget some of their previous experiences or what they’ve learned. If scientists can create habituating material, they can teach it to ignore or forget unnecessary information and achieve stability. On the other hand, sensitization could train the system to remember and incorporate new information, which gives it plasticity.

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“AI often has a hard time learning and storing new information without rewriting what’s already been stored,” says Rodolakis. “Too much stability prevents AI from learning, but too much plasticity can lead to disastrous forgetting.”

According to the team, one of the great advantages of the new study involves the small size of the nickel oxide device.

“This type of study has not been done before in the current generation of electronics without a large number of transistors,” explains Rodolakis. “The single connection system is the least modern system to demonstrate these properties, which has important implications for the possible development of neuromorphic circuitry.”


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