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Related Concept Videos

Frustration and Conflict: Approach-Approach, Approach-Avoidance01:20

Frustration and Conflict: Approach-Approach, Approach-Avoidance

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Frustration occurs when people are obstructed or prevented from achieving a desired goal or fulfilling a perceived need. For example, when someone's input is ignored in a discussion, it can lead to feelings of frustration. Conflict, however, arises from opposing interests, goals, or actions. Conflicts can take various forms based on the nature of these opposing desires or goals.
One common type of conflict is the Approach–Approach Conflict. In this case, a person faces two desirable...
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Ethical Dilemmas II01:30

Ethical Dilemmas II

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Resolving an ethical dilemma in healthcare involves a systematic approach that considers every aspect of the issue, respecting both the patient's needs and values and the healthcare professional's ethical obligations. Here are potential steps to resolve an ethical dilemma:
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Ethical Dilemmas I01:17

Ethical Dilemmas I

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Ethical dilemmas in nursing are of utmost importance, as they often arise from the tension between adhering to core ethical principles and the practical realities of healthcare delivery. These dilemmas require nurses to navigate complex situations where competing ethical considerations pull them in different directions.
Let us explore some examples to understand the potentially complex moral decisions nurses face.
Take the case of caring for minors, particularly in areas related to reproductive...
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Confirmation Biases01:31

Confirmation Biases

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Robbers Cave04:49

Robbers Cave

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During the 1950s, the landmark Robbers Cave experiment demonstrated that when groups must compete with one another, intergroup conflict, hostility, and even violence may result. At the Oklahoman summer camp, two troops of boys—termed the Rattlers and the Eagles—took part in a week-long tournament. During this time, their negativity culminated in derogatory name-calling, fistfights, and even vandalism and destruction of property. However, this work also revealed that such tension...
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Impression Management Techniques IV: Altercasting01:14

Impression Management Techniques IV: Altercasting

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Altercasting is a strategic communication technique in which an individual imposes a specific identity or social role onto another person to influence their behavior and shape the interaction. By presuming a role—such as “responsible leader” or “patient person”—altercasting encourages the target to conform to that identity, often aligning their behavior with the expectations associated with the role. The power of this tactic lies in its subtlety; once a role...
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Related Experiment Videos

Adversarial Examples: Opportunities and Challenges.

Jiliang Zhang, Chen Li

    IEEE Transactions on Neural Networks and Learning Systems
    |November 15, 2019
    PubMed
    Summary
    This summary is machine-generated.

    Deep neural networks (DNNs) are vulnerable to adversarial examples (AEs), which are imperceptible manipulations that fool AI systems. This review surveys AE generation methods and defenses, highlighting future challenges in AI security.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning Security

    Background:

    • Deep neural networks (DNNs) excel in tasks like image recognition and medical diagnosis.
    • DNNs are susceptible to adversarial examples (AEs), subtle inputs designed to cause misclassifications.
    • AEs pose significant security risks to safety-critical AI applications.

    Purpose of the Study:

    • To provide a comprehensive review of the latest research on adversarial examples (AEs).
    • To survey state-of-the-art methods for generating and defending against AEs.
    • To identify future research directions and challenges in AI security.

    Main Methods:

    • Introduction to the concept, causes, characteristics, and evaluation metrics of AEs.
    • Survey of current AE generation techniques, discussing their pros and cons.
    • Review of existing defense strategies and their limitations.

    Main Results:

    • DNNs' vulnerability to AEs is a critical concern in AI security.
    • A variety of AE generation methods exist, each with trade-offs.
    • Current defense mechanisms against AEs have notable limitations.

    Conclusions:

    • Adversarial examples represent a significant challenge to the reliability and security of deep learning models.
    • Further research is needed to develop robust defenses and understand AE vulnerabilities.
    • The field of AI security requires continuous innovation to address evolving threats from AEs.