| Jun 06, 2024 |
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(Nanowerk Information) A analysis workforce led by the College of Engineering of the Hong Kong College of Science and Know-how (HKUST) has developed a liquid metal-based digital logic machine that mimics the clever prey-capture mechanism of Venus flytraps. Exhibiting reminiscence and counting properties, the machine can intelligently reply to numerous stimulus sequences with out the necessity for extra digital parts.
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The clever methods and logic mechanisms within the machine present a recent perspective on understanding “intelligence” in nature and supply inspiration for the event of “embodied intelligence”.
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The findings have been printed in Nature Communications (“A liquid metal-based module emulating the intelligent preying logic of flytrap”).
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| Venus flytrap-inspired liquid metal-based logic module. (Picture: HKUST) (click on on picture to enlarge)
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The distinctive prey-capture mechanism of Venus flytraps has all the time been an intriguing analysis focus within the realm of organic intelligence. This mechanism permits them to successfully distinguish between varied exterior stimuli comparable to single and double touches, thereby distinguishing between environmental disturbances comparable to raindrops (single contact) and bugs (double touches), guaranteeing profitable prey seize.
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This performance is primarily attributed to the sensory hairs on the carnivorous vegetation, which exhibit options akin to reminiscence and counting, enabling them to understand stimuli, generate motion potentials (a change {of electrical} indicators in cells in response to stimulus), and bear in mind the stimuli for a brief length.
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Impressed by the inner electrical sign accumulation/decay mannequin of Venus flytraps, Prof. SHEN Yajing, Affiliate Professor of the Division of Digital and Laptop Engineering (ECE) at HKUST, who led the analysis, joined fingers along with his former PhD scholar at Metropolis College of Hong Kong, Dr. YANG Yuanyuan, now Affiliate Professor at Xiamen College, proposed a liquid metal-based logic module (LLM) based mostly on the extension/contraction deformation of liquid steel wires.
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The machine employs liquid steel wires in sodium hydroxide resolution because the conductive medium, controlling the size of the liquid steel wires based mostly on electrochemical results, thereby regulating cathode output based on the stimuli utilized to the anode and gate. Analysis outcomes exhibit that the LLM itself can memorize the length and interval {of electrical} stimuli, calculate the buildup of indicators from a number of stimuli, and exhibit important logical capabilities just like these of Venus flytraps.
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To exhibit, Prof. Shen and Dr. Yang constructed a synthetic Venus flytrap system comprising the LLM clever decision-making machine, switch-based sensory hair, and smooth electrical actuator-based petal, replicating the predation technique of Venus flytraps.
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Moreover, they showcased the potential functions of LLM in purposeful circuit integration, filtering, synthetic neural networks, and extra. Their work not solely supplies insights into simulating clever behaviors in vegetation, but in addition serves as a dependable reference for the event of subsequent organic sign simulator units and biologically impressed clever programs.
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“When people mention ‘artificial intelligence’, they generally think of intelligence that mimics animal nervous systems. However, in nature, many plants can also demonstrate intelligence through specific material and structural combinations. Research in this direction provides a new perspective and approach for us to understand ‘intelligence’ in nature and construct ‘life-like intelligence’,” stated Prof. Shen.
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“Several years ago, when Dr. Yang was still pursuing her PhD in my research group, we discussed the idea of constructing intelligent entities inspired by plants together. It is gratifying that after several years of effort, we have achieved the conceptual verification and simulation of Venus flytrap intelligence. However, it is worth noting that this work is still relatively preliminary, and there is much work to be done in the future, such as designing more efficient structures, reducing the size of devices, and improving system responsiveness,” added Prof. Shen.
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