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Struggling to follow a rambling professor? Too tired to pay attention to an afternoon Zoom call? Frustrated that an educational video won't get to the point? Well, this prompt is for you!
This prompt saves you the headache of understanding messy transcripts by organizing important information into easy-to-follow Cornell notes. It's perfect for transcripts from:
🎓 Online courses
🖋️ Remote business meetings
📖 Edcuational videos
🌟And any other resource with a transcript!
...more
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Updated: Over 1 month ago
Prompt Details
Model
Chat - GPT-4 (gpt-4)
Token size
254 ($0.00760 / call)
Example input
Objective: Analyze a transcript of a lecture from an AI Ethics course on the subject of Accountability and Responsibility. Organize vital information to grasp Accountability and Responsibility according to the transcript.
Measure of Success:
A well-structured and informative table categorizing concepts, explanations, keywords contained within the transcript’s context.
An insightful summary encapsulating key aspects on the topic of Accountability and Responsibility according to the transcript.
A comprehensive glossary as a reference.
Example output
Your output will be a Notes Table, Summary, and Glossary of terms interpreted from the transcript. This is a sample of what ChatGPT provides when given the transcript of a lecture on Accountability and Responsibility from an AI Ethics course.
A Notes Table organized by Concept, Notes, and Keywords. Since I can't paste an actual table, below is an example of one of many rows it would include:
||CONCEPT: Importance and Protection of Accountability | NOTES: The principle of accountability is crucial, with 70% of ethical frameworks discussing it. However, there's no general agreement on who should be held accountable. Some suggest auditability, transparency in AI operations, as a method to protect accountability and reliability. | KEYWORDS: Accountability, reliability, auditability, transparency ||
An informative, one paragraph summary of the transcript. Here is a partial excerpt for the given example:
The lecture transcript highlights the ambiguity and complexity surrounding accountability and responsibility in AI ethics. It emphasizes the lack of a clear definition for "responsible AI" and explores the critical concerns of the accountability gap, including causality, justice, and reparations. Various frameworks have different views on whether humans or AI should be held accountable. The issue is cited in over 70% of existing frameworks, indicating..."
Glossary of words and concepts introduced within the transcript's context. Here is a partial list given the example:
Responsible AI: A term often used without a clear definition, associated with acting with integrity or clarifying attribution of responsibility and legal liability.
Accountability Gap: The ambiguity in determining who is responsible when AI fails, involving issues of causality, justice, and reparations.
Causality: Identifying the greatest cause for the harm if AI messes up.
Justice: Determining who to punish or seek retribution from if AI fails.
Reparations: Deciding who pays if AI causes harm.
Auditability: The ability to verify that AI is performing as it should, used as a means to ensure accountability and reliability.