• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

制造“面包”:当下及未来人工智能的仿生策略

Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future.

作者信息

Krichmar Jeffrey L, Severa William, Khan Muhammad S, Olds James L

机构信息

Departments of Cognitive Sciences and Computer Science, University of California, Irvine, Irvine, CA, United States.

Sandia National Laboratories, Data-Driven and Neural Computing, Albuquerque, NM, United States.

出版信息

Front Neurosci. 2019 Jun 27;13:666. doi: 10.3389/fnins.2019.00666. eCollection 2019.

DOI:10.3389/fnins.2019.00666
PMID:31316340
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6610536/
Abstract

The Artificial Intelligence (AI) revolution foretold of during the 1960s is well underway in the second decade of the twenty first century. Its period of phenomenal growth likely lies ahead. AI-operated machines and technologies will extend the reach of Homo sapiens far beyond the biological constraints imposed by evolution: outwards further into deep space, as well as inwards into the nano-world of DNA sequences and relevant medical applications. And yet, we believe, there are crucial lessons that biology can offer that will enable a prosperous future for AI. For machines in general, and for AI's especially, operating over extended periods or in extreme environments will require energy usage orders of magnitudes more efficient than exists today. In many operational environments, energy sources will be constrained. The AI's design and function may be dependent upon the type of energy source, as well as its availability and accessibility. Any plans for AI devices operating in a challenging environment must begin with the question of how they are powered, where fuel is located, how energy is stored and made available to the machine, and how long the machine can operate on specific energy units. While one of the key advantages of AI use is to reduce the dimensionality of a complex problem, the fact remains that some energy is required for functionality. Hence, the materials and technologies that provide the needed energy represent a critical challenge toward future use scenarios of AI and should be integrated into their design. Here we look to the brain and other aspects of biology as inspiration for Biomimetic Research for Energy-efficient AI Designs (BREAD).

摘要

20世纪60年代就已预言的人工智能(AI)革命,在21世纪的第二个十年正全面展开。其显著增长的时期可能还在前方。人工智能操控的机器和技术将把智人的活动范围扩展到远远超出进化所施加的生物限制:向外深入到深空,以及向内进入DNA序列的纳米世界和相关医学应用领域。然而,我们相信,生物学能提供关键的经验教训,从而为人工智能带来繁荣的未来。一般而言,对于机器,尤其是人工智能来说,长时间运行或在极端环境中运行将需要比目前高效几个数量级的能源使用效率。在许多运行环境中,能源将受到限制。人工智能的设计和功能可能取决于能源的类型及其可用性和可获取性。任何在具有挑战性的环境中运行人工智能设备的计划都必须从它们如何供电、燃料位于何处、能量如何存储并提供给机器,以及机器在特定能量单位下能运行多长时间这些问题开始。虽然使用人工智能的一个关键优势是降低复杂问题的维度,但事实仍然是,功能实现需要一些能量。因此,提供所需能量的材料和技术是人工智能未来应用场景面临的一项关键挑战,应将其纳入人工智能的设计中。在此,我们将大脑和生物学的其他方面视为仿生研究以实现节能人工智能设计(BREAD)的灵感来源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/9285c8280107/fnins-13-00666-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/b2deb87e1df6/fnins-13-00666-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/cee2c9db9d54/fnins-13-00666-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/b64a9284504b/fnins-13-00666-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/9285c8280107/fnins-13-00666-g0004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/b2deb87e1df6/fnins-13-00666-g0001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/cee2c9db9d54/fnins-13-00666-g0002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/b64a9284504b/fnins-13-00666-g0003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b66d/6610536/9285c8280107/fnins-13-00666-g0004.jpg

相似文献

1
Making BREAD: Biomimetic Strategies for Artificial Intelligence Now and in the Future.制造“面包”:当下及未来人工智能的仿生策略
Front Neurosci. 2019 Jun 27;13:666. doi: 10.3389/fnins.2019.00666. eCollection 2019.
2
The future of Cochrane Neonatal.考克兰新生儿协作网的未来。
Early Hum Dev. 2020 Nov;150:105191. doi: 10.1016/j.earlhumdev.2020.105191. Epub 2020 Sep 12.
3
From Nanobots to Neural Networks: Multifaceted Revolution of Artificial Intelligence in Surgical Medicine and Therapeutics.从纳米机器人到神经网络:外科学与治疗学中人工智能的多面革命
Cureus. 2023 Nov 19;15(11):e49082. doi: 10.7759/cureus.49082. eCollection 2023 Nov.
4
A Survey on Green Enablers: A Study on the Energy Efficiency of AI-Based 5G Networks.绿色推动因素调查:基于人工智能的5G网络能源效率研究
Sensors (Basel). 2024 Jul 16;24(14):4609. doi: 10.3390/s24144609.
5
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare.人工智能的前景:人工智能在医疗保健领域的机遇与挑战综述。
Br Med Bull. 2021 Sep 10;139(1):4-15. doi: 10.1093/bmb/ldab016.
6
Humanitarian health computing using artificial intelligence and social media: A narrative literature review.利用人工智能和社交媒体进行人道主义健康计算:叙事文献综述。
Int J Med Inform. 2018 Jun;114:136-142. doi: 10.1016/j.ijmedinf.2018.01.015. Epub 2018 Jan 31.
7
The Emergence of Artificial Intelligence within Radiation Oncology Treatment Planning.人工智能在放射肿瘤治疗计划中的出现。
Oncology. 2021;99(2):124-134. doi: 10.1159/000512172. Epub 2020 Dec 22.
8
A Hybrid Stacked CNN and Residual Feedback GMDH-LSTM Deep Learning Model for Stroke Prediction Applied on Mobile AI Smart Hospital Platform.基于移动 AI 智能医院平台的应用,采用混合堆叠 CNN 和残差反馈 GMDH-LSTM 深度学习模型进行中风预测。
Sensors (Basel). 2023 Mar 27;23(7):3500. doi: 10.3390/s23073500.
9
Artificial intelligence technologies and compassion in healthcare: A systematic scoping review.医疗保健中的人工智能技术与人文关怀:一项系统综述。
Front Psychol. 2023 Jan 17;13:971044. doi: 10.3389/fpsyg.2022.971044. eCollection 2022.
10
Unraveling the Ethical Enigma: Artificial Intelligence in Healthcare.解开伦理谜团:医疗保健领域的人工智能
Cureus. 2023 Aug 10;15(8):e43262. doi: 10.7759/cureus.43262. eCollection 2023 Aug.

引用本文的文献

1
AI-Driven Control Strategies for Biomimetic Robotics: Trends, Challenges, and Future Directions.用于仿生机器人的人工智能驱动控制策略:趋势、挑战与未来方向
Biomimetics (Basel). 2025 Jul 14;10(7):460. doi: 10.3390/biomimetics10070460.
2
"Live" Nanomaterials Process Biomimetic Recognition and Assembly In Vivo.“活性”纳米材料在体内进行仿生识别与组装。
Small Sci. 2023 Oct 10;3(11):2300032. doi: 10.1002/smsc.202300032. eCollection 2023 Nov.
3
When neuro-robots go wrong: A review.当神经机器人出现故障时:综述

本文引用的文献

1
Neural correlates of sparse coding and dimensionality reduction.神经稀疏编码和降维的关联。
PLoS Comput Biol. 2019 Jun 27;15(6):e1006908. doi: 10.1371/journal.pcbi.1006908. eCollection 2019 Jun.
2
Data and Power Efficient Intelligence with Neuromorphic Learning Machines.基于神经形态学习机器的数据与功率高效智能
iScience. 2018 Jul 27;5:52-68. doi: 10.1016/j.isci.2018.06.010. Epub 2018 Jul 3.
3
Glider soaring via reinforcement learning in the field.通过强化学习在野外滑翔。
Front Neurorobot. 2023 Feb 3;17:1112839. doi: 10.3389/fnbot.2023.1112839. eCollection 2023.
4
Explainable AI: A Neurally-Inspired Decision Stack Framework.可解释人工智能:一种受神经启发的决策堆栈框架。
Biomimetics (Basel). 2022 Sep 9;7(3):127. doi: 10.3390/biomimetics7030127.
5
Cortical Motion Perception Emerges from Dimensionality Reduction with Evolved Spike-Timing-Dependent Plasticity Rules.皮层运动知觉源自具有进化的尖峰时间依赖可塑性规则的降维处理。
J Neurosci. 2022 Jul 27;42(30):5882-5898. doi: 10.1523/JNEUROSCI.0384-22.2022. Epub 2022 Jun 22.
6
A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning.一种通过无地标学习校准的啮齿动物头部方向的脉冲神经网络模型。
Front Neurorobot. 2022 May 26;16:867019. doi: 10.3389/fnbot.2022.867019. eCollection 2022.
7
Design Principles for Neurorobotics.神经机器人学的设计原则
Front Neurorobot. 2022 May 25;16:882518. doi: 10.3389/fnbot.2022.882518. eCollection 2022.
8
Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding.基于深度赫布预测编码的多模态表示学习用于地点识别
Front Robot AI. 2021 Dec 13;8:732023. doi: 10.3389/frobt.2021.732023. eCollection 2021.
Nature. 2018 Oct;562(7726):236-239. doi: 10.1038/s41586-018-0533-0. Epub 2018 Sep 19.
4
How to stop data centres from gobbling up the world's electricity.如何阻止数据中心消耗全球电力。
Nature. 2018 Sep;561(7722):163-166. doi: 10.1038/d41586-018-06610-y.
5
Spiking Optical Flow for Event-Based Sensors Using IBM's TrueNorth Neurosynaptic System.基于 IBM TrueNorth 神经突触系统的用于事件型传感器的尖峰光流。
IEEE Trans Biomed Circuits Syst. 2018 Aug;12(4):860-870. doi: 10.1109/TBCAS.2018.2834558. Epub 2018 Jun 19.
6
Efficient Coding and Energy Efficiency Are Promoted by Balanced Excitatory and Inhibitory Synaptic Currents in Neuronal Network.神经网络中平衡的兴奋性和抑制性突触电流促进高效编码和能量效率。
Front Cell Neurosci. 2018 May 3;12:123. doi: 10.3389/fncel.2018.00123. eCollection 2018.
7
Optimal dynamic soaring consists of successive shallow arcs.最佳动力翱翔由连续的浅层弧线组成。
J R Soc Interface. 2017 Oct;14(135). doi: 10.1098/rsif.2017.0496.
8
3D Visual Response Properties of MSTd Emerge from an Efficient, Sparse Population Code.MSTd的3D视觉反应特性源自一种高效、稀疏的群体编码。
J Neurosci. 2016 Aug 10;36(32):8399-415. doi: 10.1523/JNEUROSCI.0396-16.2016.
9
Frigate birds track atmospheric conditions over months-long transoceanic flights.军舰鸟在跨洋长途飞行中能追踪数月的大气状况。
Science. 2016 Jul 1;353(6294):74-8. doi: 10.1126/science.aaf4374.
10
Solving Constraint Satisfaction Problems with Networks of Spiking Neurons.利用脉冲神经元网络解决约束满足问题。
Front Neurosci. 2016 Mar 30;10:118. doi: 10.3389/fnins.2016.00118. eCollection 2016.