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Related Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Machines: Problem Solving I01:22

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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...
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Automatic processing refers to the cognitive operations that occur without conscious intent or awareness, playing a fundamental role in shaping social cognition and behavior. These processes enable individuals to navigate complex social environments efficiently by relying on mental shortcuts and pre-existing knowledge structures known as schemas. One of the most influential mechanisms underlying automatic processing is priming, which subtly activates mental representations through exposure to...
Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
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Related Experiment Videos

Machine understanding.

Huili Chen1, Stephen R Grimm2, Olga Russakovsky3

  • 1Program in Cognitive Science, Princeton University, Princeton, NJ, USA; Faculty of Information, University of Toronto, Toronto, ON, Canada.

Trends in Cognitive Sciences
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a framework to precisely assess artificial intelligence (AI) understanding. It offers conceptual tools for evaluating machine comprehension in AI systems.

Keywords:
AI evaluationUnderstandingbenchmarkinginterpretabilityworld models

Related Experiment Videos

Area of Science:

  • Philosophy of AI
  • Cognitive Science
  • Artificial Intelligence

Background:

  • Assessing artificial intelligence (AI) systems' "understanding" is crucial for evaluating intelligence.
  • This evaluation is vital for the safe and responsible deployment of AI technologies.
  • Current AI practices lack precise frameworks for discussing machine understanding.

Purpose of the Study:

  • To develop a conceptual framework for precisely discussing and evaluating machine understanding in AI.
  • To provide tools for researchers and developers to ask more specific questions about AI comprehension.
  • To offer a structured approach for making more precise claims regarding AI understanding.

Main Methods:

  • Drawing on scholarship from philosophy and cognitive science.
  • Analyzing current practices within the field of artificial intelligence.
  • Conceptualizing understanding as a relation between a system (S) and a target of understanding (T).
  • Discussing methods to specify the relation, the system, and the target of understanding.

Main Results:

  • A framework for conceptualizing machine understanding as a relational construct (S-T).
  • Identification of options for specifying the components of the understanding relation.
  • Development of precise questions and claims regarding AI comprehension.

Conclusions:

  • The proposed framework offers conceptual tools, not a definitive theory, for machine understanding.
  • Enhances the ability to assess and advance AI comprehension.
  • Facilitates more rigorous evaluation practices for AI systems.