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Aggregates Classification01:29

Aggregates Classification

346
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
346
Classification of Systems-I01:26

Classification of Systems-I

215
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
215
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

620
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
620
Classification of Systems-II01:31

Classification of Systems-II

177
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
177
Types of Aggregate Grading01:15

Types of Aggregate Grading

598
Aggregate grading is crucial in economically obtaining a concrete mix with adequate strength, reasonable workability, and minimal segregation. There are four types of aggregate gradation: well-graded, uniformly (or one-sized) graded, gap-graded, and open-graded.
Well-graded aggregates include a complete range of necessary size fractions that fit together to create a dense matrix with minimal voids, represented by a smooth, continuous gradation curve. This type of grading ensures good...
598
Natural and Artificial Concepts01:24

Natural and Artificial Concepts

195
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...
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Related Experiment Video

Updated: Jul 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Toward Generalized Artificial Intelligence by Assessment Aggregation With Applications to Standard and Extreme

Abdourrahmane M Atto

    IEEE Transactions on Neural Networks and Learning Systems
    |August 1, 2023
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a plural learning framework to create generalized artificial intelligence (GAI) from specialized convolutional neural networks (CNNs). The approach uses distinct specialization and generalization training stages for improved AI performance.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Deep Learning

    Background:

    • Specialized Convolutional Neural Networks (CNNs) often lack generalizability.
    • Developing Artificial Intelligence (AI) that can generalize across diverse tasks remains a significant challenge.

    Purpose of the Study:

    • To propose a novel plural learning framework for deriving Generalized Artificial Intelligence (GAI).
    • To enhance AI capabilities by integrating specialized CNNs through a two-stage training process.

    Main Methods:

    • A two-stage training framework: specialization and generalization.
    • Specialization stage: individual CNNs learn to predict independently.
    • Generalization stage: an integration network learns from specialized CNN outputs (softmax probabilities).

    Main Results:

    • Demonstrated generalization through multimodel, multimodal, and distributed schemes.
    • Multimodel: CNNs on the same data modality cooperate.
    • Multimodal: CNNs specialize in different input types.
    • Distributed: CNNs exchange assessments for joint decision-making.

    Conclusions:

    • The proposed framework significantly improves AI performance in both standard and extreme classification tasks.
    • The integration network effectively learns from diverse assessment measures provided by specialized CNNs.
    • This approach offers a robust method for building more generalized AI systems from specialized components.