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相关概念视频

Observational Learning01:12

Observational Learning

263
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
263
Cognitive Learning01:21

Cognitive Learning

476
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
476
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

233
In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
233
Introduction to Learning01:18

Introduction to Learning

506
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
506
Associative Learning01:27

Associative Learning

507
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
507
Purposive Learning01:22

Purposive Learning

182
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
182

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In Vivo Optical Calcium Imaging of Learning-Induced Synaptic Plasticity in Drosophila melanogaster
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人工智能的 dendrocentric学习

Kwabena Boahen1,2,3,4,5,6

  • 1Department of Bioengineering, Stanford University, Stanford, CA, USA. boahen@stanford.edu.

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|November 30, 2022
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概括
此摘要是机器生成的。

人工智能 (AI) 由于计算需求的增加而面临硬件限制. 这项研究提出了树突中心学习,模仿神经元树突,用于智能手机上的节能AI.

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科学领域:

  • 计算神经科学
  • 人工智能硬件
  • 半导体行业的发展趋势

背景情况:

  • 人工智能 (AI) 的快速发展需要计算能力的指数增长,特别是浮点乘法.
  • 目前半导体行业在芯片乘数密度上的进步 (每两年翻一番) 无法与人工智能的计算加速相匹配 (每两个月翻一番).
  • 在3D架构中,密集的乘法器的物理限制,包括信号传输距离和热散射,阻碍了进一步的性能增长.

研究的目的:

  • 为人工智能 (AI) 提出一种超越当前硬件的热和物理限制的新方法.
  • 在AI中引入树突中心学习作为传统突触学习的替代方案.
  • 在移动设备上展示节能AI计算的可行性.

主要方法:

  • 一个模拟基于树的学习过程的计算模型的开发.
  • 设计以模仿拟的树模型的铁电装置的概念化.
  • 拟议的树中心学习人工智能 (合成智能) 模型的能源效率评估.

主要成果:

  • 树突中心式学习为超越3D芯片架构的热制约提供了一个潜在的解决方案.
  • 这种方法从传统的突触权重转向沿着树突的有序输入,称为树突中心学习.
  • 拟议的合成智能模型显示了显著降低功耗的潜力,运行在瓦特级别.

结论:

  • 由生物神经元结构所启发的 dendrocentric 学习为人工智能硬件提供了一个范式转变.
  • 这种方法可以实现高能效的人工智能计算,有可能在智能手机上运行,而不是需要高功率的云基础设施.
  • 拟议的人工智能为克服目前半导体的局限性和实现可持续的人工智能进步提供了途径.