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1. • • Intelligent matter is able to interact with its environment, learns from the input it receives and self-regulates its action. All four key functional elements—sensor, actuator, network and long-term memory—need to be incorporated. The arm of an octopus, with its embedded sensors, actuators and nervous system, represents intelligent matter.
     www.nature.com/articles/s41586-021-03453-y

     The rise of intelligent matter | Nature

2. People also ask

   What makes systems with natural intelligence possible?

   • In addition to structural, functional, and adaptive depth, the other major attribute that makes systems with natural intelligence possible is that they are inherently integrated at every level of the deep, multi-scale adaptive process described above.

   Deep Intelligence: What AI Should Learn from Nature’s Imagination

   link.springer.com/article/10.1007/s12559-023-10124-9

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   How do biological intelligent systems achieve their (collective) goals?

   • Biological intelligent systems demonstrate increased ability to achieve their (collective) goals despite obstacles by integrating the individual competencies of their components (which can perform tasks in their own space without any inkling of the large-scale goals to which they contribute) (c).

   Collective intelligence: A unifying concept for integrating biology

   www.nature.com/articles/s42003-024-06037-4

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   How does intelligent matter interact with its environment?

   • Intelligent matter is able to interact with its environment, learns from the input it receives and self-regulates its action. All four key functional elements—sensor, actuator, network and long-term memory—need to be incorporated. The arm of an octopus, with its embedded sensors, actuators and nervous system, represents intelligent matter.

   The rise of intelligent matter - Nature

   www.nature.com/articles/s41586-021-03453-y

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   Should natural intelligence be at the core of AI systems?

   • In particular, it argues that integrated embodiment, modularity, synergy, developmental learning, and evolution are key enablers of natural intelligence and should be at the core of AI systems as well.

   Deep Intelligence: What AI Should Learn from Nature’s Imagination

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   Can Intelligent Systems make autonomous decisions based on input?

   • Scientific Reports 13, Article number: 7586 (2023) Cite this article Intelligent systems that are capable of making autonomous decisions based on input from their environment have great potential to do good, but they also raise significant social and ethical concerns.

   Embedding responsibility in intelligent systems: from AI ethics to respo…

   www.nature.com/articles/s41598-023-34622-w

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   How does Intelligence interact with the world?

   • This means that intelligence always interacts with the world as a single entity, and, except in pathological cases, never faces the problem of integrating parts of its immediate perception, memory, and emotion post facto to make inferences across all aspects of experience.

   Deep Intelligence: What AI Should Learn from Nature’s Imagination

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3. www.nature.com › articles › s41586/021/03453-yThe rise of intelligent matter | Nature

   www.nature.com › articles › s41586/021/03453-y

   Jun 16, 2021 · 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...

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4. www.nature.com › articles › s42003/024/06037-4Collective intelligence: A unifying concept for ... - Nature

   www.nature.com › articles › s42003/024/06037-4

   Mar 28, 2024 · Biological intelligent systems demonstrate increased ability to achieve their (collective) goals despite obstacles by integrating the individual competencies of their components (which can...

5. www.nature.com › articles › s41598/023/34622-wEmbedding responsibility in intelligent systems: from AI ...

   www.nature.com › articles › s41598/023/34622-w

   May 18, 2023 · Intelligent systems that are capable of making autonomous decisions based on input from their environment have great potential to do good, but they also raise significant social and ethical...

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7. academic.oup.com › aob › articlePlants are intelligent, here’s how | Annals of Botany ...

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   Oct 20, 2019 · INTRODUCTION. The idea that plants are intelligent has been controversial since it was first described (Trewavas, 2003).

   • Author: Paco Calvo, Monica Gagliano, Gustavo M Souza, Anthony Trewavas

   • Publish Year: 2020

8. www.unep.org › news-and-stories › storyHow artificial intelligence is helping tackle environmental ...

   www.unep.org › news-and-stories › story

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   Nov 7, 2022 · Monitoring methane emissions. Image: UNEP. One of the UNEP-led initiatives inside the WESR digital ecosystem is the International Methane Emissions Observatory (IMEO), which leverages AI to revolutionize the approach to monitoring and mitigating methane emissions.

9. knowledge.wharton.upenn.edu › article › how-can-aiHow Can AI and the Human Brain Work Together?

   knowledge.wharton.upenn.edu › article › how-can-ai

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   Nov 9, 2023 · How Can AI and the Human Brain Work Together? November 9, 2023 • 22 min read Neuroscientists are using AI to unlock new insights into the human brain. Wharton experts weigh in on the latest ...

10. link.springer.com › article › 10Deep Intelligence: What AI Should Learn from Nature’s ...

    link.springer.com › article › 10

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    Mar 6, 2023 · This position paper brings together ideas from neuroscience, evolutionary and developmental biology, and complex systems to analyze why such natural intelligence is possible in animals and suggests that AI should exploit the same strategies to move in a different direction.