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Related Concept Videos

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Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
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Participant Modeling
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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Drug clearance is a critical pharmacokinetic process involving the irreversible removal of drugs from the body through various organs over a specified time period. Physiological models are indispensable in determining organ-specific clearance, defined by the proportion of the drug eliminated per unit of time from the organ's blood volume.
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Clearance Models: Compartment Models01:25

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Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
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Related Experiment Video

Updated: Aug 1, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

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Published on: April 11, 2018

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Modeling needs user modeling.

Mustafa Mert Çelikok1, Pierre-Alexandre Murena1, Samuel Kaski1,2

  • 1Department of Computer Science, Aalto University, Espoo, Finland.

Frontiers in Artificial Intelligence
|April 24, 2023
PubMed
Summary
This summary is machine-generated.

Integrating humans back into modeling workflows, through human-AI collaboration, enables tackling complex problems. This approach enhances machine learning pipelines for broader applications.

Keywords:
AI assistancehuman-centric artificial intelligencehuman–AI collaborationhuman–AI interactionmachine learningprobabilistic modelinguser modeling

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Human-Computer Interaction

Background:

  • Current modeling and machine learning pipelines often exclude human input for objectivity and automation.
  • This exclusion has limited these workflows to only well-specified problems, hindering broader applicability.

Purpose of the Study:

  • To advocate for the reintegration of humans into modeling processes.
  • To explore how human-AI collaboration can expand the scope of solvable problems.
  • To identify necessary advancements in user models and problem scoping for interactive AI assistance.

Main Methods:

  • This perspective article discusses the conceptual framework for human-centric machine learning.
  • It analyzes the challenges and requirements for developing interactive modeling workflows.

Main Results:

  • Reintroducing humans into the loop allows for iterative modeling processes.
  • AI can provide collaborative assistance, enabling the modeling of a wider range of problems.
  • New interactive modeling workflows and human-compatible machine learning pipelines are envisioned.

Conclusions:

  • Human-AI collaborative modeling is essential for addressing complex, ill-specified problems.
  • Advancements in user models and problem scoping are crucial for realizing this vision.
  • This paradigm shift promises more versatile and human-centric AI applications.