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The rise of intelligent matter

Author

Listed:
  • C. Kaspar

    (University of Münster)

  • B. J. Ravoo

    (University of Münster
    University of Münster)

  • W. G. Wiel

    (University of Münster
    University of Twente)

  • S. V. Wegner

    (University of Münster)

  • W. H. P. Pernice

    (University of Münster
    University of Münster)

Abstract

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.

Suggested Citation

  • C. Kaspar & B. J. Ravoo & W. G. Wiel & S. V. Wegner & W. H. P. Pernice, 2021. "The rise of intelligent matter," Nature, Nature, vol. 594(7863), pages 345-355, June.
    • Handle: RePEc:nat:nature:v:594:y:2021:i:7863:d:10.1038_s41586-021-03453-y
    DOI: 10.1038/s41586-021-03453-y
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    Cited by:

    1. Gianluca Milano & Alessandro Cultrera & Luca Boarino & Luca Callegaro & Carlo Ricciardi, 2023. "Tomography of memory engrams in self-organizing nanowire connectomes," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Shanming Hu & Yuhuang Fang & Chen Liang & Matti Turunen & Olli Ikkala & Hang Zhang, 2023. "Thermally trainable dual network hydrogels," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Wenbo Li & Huyue Chen & Zhiran Yi & Fuyi Fang & Xinyu Guo & Zhiyuan Wu & Qiuhua Gao & Lei Shao & Jian Xu & Guang Meng & Wenming Zhang, 2023. "Self-vectoring electromagnetic soft robots with high operational dimensionality," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Herbert Jaeger & Beatriz Noheda & Wilfred G. Wiel, 2023. "Toward a formal theory for computing machines made out of whatever physics offers," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Jiqiang Wang & Baohu Wu & Peng Wei & Shengtong Sun & Peiyi Wu, 2022. "Fatigue-free artificial ionic skin toughened by self-healable elastic nanomesh," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Zhaoqi Gu & Runlin Zhu & Tianci Shen & Lin Dou & Hongjiang Liu & Yifei Liu & Xu Liu & Jia Liu & Songlin Zhuang & Fuxing Gu, 2023. "Autonomous nanorobots with powerful thrust under dry solid-contact conditions by photothermal shock," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    7. Tie Mei & Chang Qing Chen, 2023. "In-memory mechanical computing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    8. Yan Sun & Shuting Xu & Zheqi Xu & Jiamin Tian & Mengmeng Bai & Zhiying Qi & Yue Niu & Hein Htet Aung & Xiaolu Xiong & Junfeng Han & Cuicui Lu & Jianbo Yin & Sheng Wang & Qing Chen & Reshef Tenne & All, 2022. "Mesoscopic sliding ferroelectricity enabled photovoltaic random access memory for material-level artificial vision system," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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