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Related Concept Videos

Associative Learning01:27

Associative Learning

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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...
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Introduction to Learning01:18

Introduction to Learning

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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.
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Observational Learning01:12

Observational Learning

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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...
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Purposive Learning01:22

Purposive Learning

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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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Cognitive Learning01:21

Cognitive Learning

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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.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Lifelong Dual Generative Adversarial Nets Learning in Tandem.

Fei Ye, Adrian G Bors

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    |June 1, 2023
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    Summary
    This summary is machine-generated.

    Lifelong dual generative adversarial networks (LD-GANs) enable artificial intelligence to learn new concepts without forgetting previous knowledge. This novel approach uses a Teacher-Assistant model and lifelong self knowledge distillation for efficient, continuous learning.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Advanced deep learning networks often forget previously learned information when trained on new data.
    • Continuous learning, or lifelong learning (LLL), is crucial for developing more capable AI systems.

    Purpose of the Study:

    • To propose a novel framework, lifelong dual generative adversarial networks (LD-GANs), that addresses the issue of catastrophic forgetting in AI.
    • To develop an efficient training algorithm for continuous knowledge acquisition in AI.

    Main Methods:

    • Introduced lifelong dual generative adversarial networks (LD-GANs) with a Teacher and Assistant network structure.
    • Proposed a lifelong self knowledge distillation (LSKD) algorithm for training LD-GANs during task transitions.
    • Utilized a single discriminator for evaluating the realism of generated images from both GANs.

    Main Results:

    • LD-GANs demonstrated memory efficiency, avoiding parameter freezing after task learning.
    • The framework showed strong performance in unsupervised lifelong representation learning.
    • Extended LD-GANs to a Teacher-Student network for cross-domain data representation assimilation.

    Conclusions:

    • The proposed LD-GANs framework effectively enables continual learning without forgetting.
    • LSKD facilitates knowledge transfer between AI components during lifelong learning.
    • LD-GANs offer a promising solution for memory-efficient and continuous AI development.