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Related Concept Videos

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...
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Dendrocentric learning for synthetic intelligence.

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

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

Nature
|November 30, 2022
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Summary
This summary is machine-generated.

Artificial intelligence (AI) faces hardware limitations due to increasing computational demands. This study proposes dendrocentric learning, mimicking neuronal dendrites, for energy-efficient AI on smartphones.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence Hardware
  • Semiconductor Industry Trends

Background:

  • Rapid advancements in artificial intelligence (AI) necessitate exponential growth in computational power, specifically floating-point multiplications.
  • Current semiconductor industry progress in chip multiplier density (doubling every two years) cannot match AI's computational acceleration (doubling every two months).
  • Physical limitations of densely tiled multipliers on chips, including signal travel distance and heat dissipation in 3D architectures, hinder further performance gains.

Purpose of the Study:

  • To propose a novel approach for artificial intelligence (AI) that overcomes the thermal and physical constraints of current hardware.
  • To introduce dendrocentric learning as an alternative to traditional synaptic learning in AI.
  • To demonstrate the feasibility of energy-efficient AI computation on mobile devices.

Main Methods:

  • Development of a computational model simulating dendrite-based learning processes.
  • Conceptualization of a ferroelectric device designed to emulate the proposed dendrite model.
  • Evaluation of the energy efficiency of the proposed dendrocentric learning artificial intelligence (synthetic intelligence) model.

Main Results:

  • Dendrocentric learning offers a potential solution to transcend the thermal constraints of 3D chip architectures.
  • This approach moves from traditional synaptic weighting to ordered input along dendrites, termed dendrocentric learning.
  • The proposed synthetic intelligence model shows potential for significantly reduced power consumption, operating at watt levels.

Conclusions:

  • Dendrocentric learning, inspired by biological neuronal structures, presents a paradigm shift for artificial intelligence hardware.
  • This method enables highly energy-efficient AI computation, potentially running on smartphones rather than requiring high-power cloud infrastructure.
  • The proposed synthetic intelligence offers a pathway to overcome current semiconductor limitations and achieve sustainable AI advancements.