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The rise of intelligent matter
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Artificial intelligence (AI) is accelerating the development of unconventional computing paradigms inspired by the abilities and energy efficiency of the brain. The human brain excels especially in computationally intensive cognitive tasks, such as pattern recognition and classification. A long-term goal is de-centralized neuromorphic computing, relying on a network of distributed cores to mimic the massive parallelism of the brain, thus rigorously following a nature-inspired approach for information processing. Through the gradual transformation of interconnected computing blocks into continuous computing tissue, the development of advanced forms of matter exhibiting basic features of intelligence can be envisioned, able to learn and process information in a delocalized manner. Such intelligent matter would interact with the environment by receiving and responding to external stimuli, while internally adapting its structure to enable the distribution and storage (as memory) of information. We review progress towards implementations of intelligent matter using molecular systems, soft materials or solid-state materials, with respect to applications in soft robotics, the development of adaptive artificial skins and distributed neuromorphic computing.
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“Therefore, there is a rising interest in energy-efficient hardware primarily optimized for artificial intelligence [29].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [29]
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Plants sum and subtract stimuli over different timescales
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“Examples of memory formation at the tissue level are widespread (18, 21, 22, 36, 37), even in nonbiological matter (38, 39).”
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“A closed loop is necessary in the artificial world to obtain autonomy and interaction capabilities in a dynamic environment.(49) Nevertheless, obtaining highly integrated and autonomous bio-inspired robots is still enormously challenging,(50) and radical innovations are needed to design systems that can efficiently de…”
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“R ESISTIVE random access memory (RRAM) has been extensively studied as a promising candidate for neuromorphic computing [1], [2].”
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Kinetic Theory of Decentralized Learning for Smart Active Matter.
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“4d, for a 3 ×3 addressarray, to visit the Desired addresses (1, 4, 5, 8, 9), the external magnetic fi eld stimuli applied at designated positions (magnetic instruction) generate the state sequence States (11, 10, 11, 01, 11, 10, 11), successfully traversing the speci fi ed addresses.”
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Physical neural networks with self-learning capabilities
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Urban Sensing of Virtual Internet of Things for Metaverse
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Hopping-Transport Mechanism for Reconfigurable Logic in Disordered Dopant Networks
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Soft Robots Powered by Sustainable Energy
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Direct-Write Printing of Multifunctional Iontronic Composites That Sense, Rectify, and Actuate
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Universal Mechanical Polycomputation in Granular Matter
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Artificial Intelligence without Digital Computers: Programming Matter at a Molecular Scale
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Architected Materials for Soft Robotics
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Optimized user-guided motion control of modular robots
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Multi‐Instably Mechanical Systems for Computing‐Storing Functions
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Self-organizing memristive networks as physical learning systems
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“Physical learning systems can be broadly described as systems comprised of physical substrates whose intrinsic nonlinear dynamics can be exploited for learning 1,2 .”
InfluentialA light-fueled self-oscillator that senses force
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Transients and multiperiodic responses: a hierarchy of material bits
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Active matter flocking via predictive alignment.
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“Similar feedback mechanisms are also indispensable in the control of autonomous robotic systems [4].”
Harnessing Liquid Crystal Elastomers for Locomotion and Mechanical Intelligence in a Soft Robot
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“9 Such intelligent functionality enables increasingly autonomous soft robotic systems capable of adapting and making decisions without direct human control.”
An introduction to reservoir computing
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“Nevertheless, according to Kaspar et al. [26], reservoir computing systems in the form described here do not constitute “intelligent matter” in the technical sense: • The systems possess fading memory, whereas intelligent matter needs to have long-term memory.”
The rise of intelligent adaptive metasurfaces
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Dynamically adaptive materials
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BackgroundBiodegradable Humidity Actuators for Sustainable Soft Robotics using Deliquescent Hydrogels
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Protocell Computing on Aragonite Substrates
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Stable magnetic soft structures
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Template-Free Ultrafast Directed Self-Assembly Using Biaxial Toggled Magnetic Fields
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Electric Field Driven Soft Morphing Matter
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Optimization of Core–Shell Ternary Electrodes for High‐Performance Ionic Actuator in Soft Gripper
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Chemomechanical Self‐Oscillatory Microgel Motility in Stratified Chemical Media
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Informational Memory Shapes Collective Behavior in Intelligent Swarms.
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“This system exemplifies how sensing, actuation, and memory together generate adaptive collective behavior [22].”
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Photoresponsive Block Copolymer Nanostructures through Implementation of Arylazopyrazoles.
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“Today, the adaptive material characteristics are attracting more attention [48,49] as part of the paradigm of intelligent materials [50–52].”
BackgroundTowards a Microfluidic Microcontroller Circuit Library for Soft Robots
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Perovskite Plasticity: Exploiting Instability for Self‐Optimized Performance
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Graphene/Ferroelectric (Ge-Doped HfO2) Adaptable Transistors Acting as Reconfigurable Logic Gates
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“However, we consider that the analogy between a transistor and a neuron is in its infancy, since the neuron has thousands of synapses connected to it and thus the advancement of solid-state intelligent matter [27] is at the beginning.”
BackgroundTranscriptional Regulation of Synthetic Polymer NetworksPreprint
Graham, A. J., Dundas, C. M., Partipilo, G., Mahfoud, I. E. M., FitzSimons, T. M., Rinehart, R., Chiu, D., Tyndall, A. E., Rosales, A. M., & Keitz, B. K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.17.464678 · Cited by 6
Metadata
- Authors
- Kaspar, C., Ravoo, B. J., van der Wiel, W. G., Wegner, S. V., & Pernice, W. H. P.
- Affiliations
- University of Münster (DE)
- Corresponding author
- Pernice, W. H. P.
- Venue
- Nature
- Published
- 2021-06-16
- Volume
- 594
- Issue
- 7863
- Pages
- 345-355
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W3169707058
- Cited by
- 526
- References
- 0
- Citations by year
- 2026: 70 · 2025: 136 · 2024: 112 · 2023: 106 · 2022: 75 · 2021: 27
- FWCI
- 5.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- University of Twente Research Information (publishedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- University of Twente Research Information (submittedVersion, other-oa)
- Funders
- European Commission (101017237, HORIZON2020)
- Deutsche Forschungsgemeinschaft (SFB 1459, 433682494)
- Volkswagen Foundation (A126874)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Mechanical Engineering › Advanced Materials and Mechanics
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Reservoir Computing
- Keywords
- neuromorphic computing, decentralized computing, molecular systems, soft materials, solid-state materials, soft robotics, distributed memory, pattern recognition
- MeSH terms
- Artificial Intelligence, Colloids, Environment, Enzymes, Equipment Design, Homeostasis, Humans, Physical Stimulation, Robotics, Skin, Artificial, Biomimetics, Biomimetic Materials
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