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The sea slug study points to better AI hardware



In order for artificial intelligence to become any smarter, it must first become as intelligent as one of the most basic creatures on the planet: the sea slug.


A new study has discovered that a material can be used to mimic the most important intelligence features of the sea slug. The discovery represents a significant step forward in the development of hardware that could aid in the improvement of the efficiency and reliability of artificial intelligence in a variety of applications ranging from self-driving cars and surgical robots to social media algorithms.


An international team of researchers from Purdue University, Rutgers University, the University of Georgia, and Argonne National Laboratory collaborated on the study, which was published this week in the Proceedings of the National Academy of Sciences.


Shriram Ramanathan, a Purdue professor of materials engineering, said that by studying sea slugs, neuroscientists discovered the characteristics of intelligence that are essential to the survival of any organism. The use of animals' mature intelligence to accelerate the development of artificial intelligence is something that we want to take advantage of.


The two most important signs of intelligence that neuroscientists have discovered in sea slugs are habituation and sensitization, according to their research. Habituation is the process of gradually becoming accustomed to a stimulus over time, such as becoming accustomed to driving the same route to work every day. It is the polar opposite of desensitization – it is the act of reacting strongly to a new stimulus, such as avoiding bad food from a restaurant.


Artificial intelligence has a difficult time learning and storing new information without overwriting information it has already learned and stored, a problem known as the "stability-plasticity dilemma" by researchers studying brain-inspired computing. Habituation would allow AI to "forget" unneeded information (thereby achieving greater stability), whereas sensitization would aid in the retention of new and important information.



A method was discovered by the researchers in this study that allowed them to demonstrate both habituation and sensitization in nickel oxide, a quantum material. The material is referred to as "quantum" because the properties of the material cannot be explained by classical physics alone.


If a quantum material can reliably mimic these types of learning, it may be possible to incorporate artificial intelligence directly into hardware. Furthermore, if artificial intelligence (AI) could operate both in hardware and software, it might be able to perform more complex tasks while using less energy.


According to Ramanathan, the team "basically emulated experiments done on sea slugs in quantum materials in order to understand how these materials can be of interest for artificial intelligence."


According to neuroscientific studies, the sea slug demonstrates habituation when it ceases to withdraw its gill as much in response to being tapped on the siphon when tapped repeatedly. An electric shock to the tail, on the other hand, causes the gill to withdraw much more dramatically, indicating that it has become sensitive.


A greater change in electrical resistance is observed when nickel oxide is subjected to the equivalent of a "gill withdrawal." The researchers discovered that repeatedly exposing the material to hydrogen gas causes the change in electrical resistance of nickel oxide to decrease over time, but that introducing a new stimulus such as ozone causes the change in electrical resistance to increase significantly.


Using this information as inspiration, a research group led by Kaushik Roy, the Edward G. Tiedemann Jr. Distinguished Professor of Electrical and Computer Engineering at Purdue University, developed a model of nickel oxide's behavior and developed an algorithm that successfully used these habituation and sensitization strategies to successfully group and categorize data points into clusters.


“The stability-plasticity conundrum has not been resolved in any way. However, we have demonstrated a method of dealing with it based on behavior we have observed in a quantum material,” Roy explained. “If we can develop a material that learns in this way and incorporate it into hardware in the future, artificial intelligence will be able to perform tasks much more efficiently.”


Researchers will need to figure out how to apply habituation and sensitization in large-scale systems before they can use quantum materials as artificial intelligence hardware in the real world. A material's response to stimuli while being integrated into a computer chip would also have to be determined by the researchers.


According to the researchers, this study serves as a starting point for guiding those subsequent steps. In addition to the experiments conducted at Purdue, a team at Rutgers University conducted detailed theory calculations to understand what was happening within nickel oxide at the microscopic level in order to mimic the intelligence features of the sea slug. The properties of the nickel oxide sample were characterized by Argonne National Laboratory, and the conductivity of the sample was measured by the University of Georgia in order to further understand the material's behavior.

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