通过神经形态原理实现高效可靠的人工智能
Bipin Rajendran1, Osvaldo Simeone1, Bashir Al-Hashimi2
1Northeastern University London , London, UK.
概括
人工智能 (AI) 需要超越大型神经网络的新原则来提高效率和可靠性. 采用由大脑启发的神经形态工程概念可以导致可持续的AI发展.
科学领域:
- 人工智能的人工智能
- 神经形态工程的神经形态工程
- 可持续的计算 可持续的计算
背景情况:
- 目前的人工智能依赖于在GPU上训练的大型神经网络,导致高成本和能源使用.
- 这种以硬件为中心的方法有风险偏爱适合当前硬件的算法,而不是内在优越的算法.
- 现有的AI模型往往缺乏可靠性,无法量化不确定性并产生自信的不正确输出.
研究的目的:
- 建议从当前的人工智能范式转向更高效,更可靠的系统.
- 概述未来人工智能设计的关键神经形态工程原理.
- 探索大脑启发的计算如何解决当前AI的局限性.
主要方法:
- 讨论六个核心的神经形态原理:状态的反复模型,极端的动态稀疏性,无反向传播的学习,概率决策,内存计算和硬件软件联合设计.
- 对每个主要领域的相关先前研究进行调查.
- 确定未来的研究方向.
主要成果:
- 确定适用于AI算法,架构和硬件的六个关键神经形态原则.
- 这些原则有潜力指导开发更高效,可靠和可持续的AI系统.
- 强调神经形态工程与人工智能进步之间的协同作用.
结论:
- 实现高效可靠的人工智能需要采用神经形态原则.
- 由大脑启发的计算为克服当前人工智能扩展的局限性提供了一条道路.
- 未来的人工智能开发应该整合由神经科学为基础的算法,架构和硬件共同设计.
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