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Artificial intelligence
Unit 1
How AI Works
Introduction to AI
Machine Learning and AI
Natural Language Processing (NLP)
Computer Vision and AI
AI Ethics and Bias
Unit 2
What is NLP
Introduction to NLP
NLP Techniques
NLP in AI Applications
Unit 3
Basic AI projects for Resume
Introduction to Basic AI Projects for Resume
Implementing a Simple AI Project
Showcasing AI Projects on a Resume
Unit 4
When is AGI coming?
Current State of AGI Research
Predictions and Timelines for AGI
Technological Milestones and AGI Development
Challenges and Risks of AGI
Pathways to AGI
Unit 1 • Chapter 5
AI Ethics and Bias
Summary
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Concept Check
What is a common ethical concern regarding AI bias?
Hardware upgrades for AI.
Algorithm efficiency improvements.
Data privacy violations.
Discrimination based on race or gender.
How can AI bias be mitigated?
Diverse training data and algorithm transparency.
Hiding the decision-making process.
Increasing overall bias in the system.
Ignoring potential bias issues.
What is the impact of AI bias on society?
Reinforces existing inequalities and discrimination.
Reduces the need for diversity in AI development.
Creates a fair and just society automatically.
Improves access to opportunities for all.
Why is it important to address AI bias in technology?
To maximize profitability for AI companies.
To speed up AI deployment without concern for bias.
To prioritize efficiency over ethical considerations.
To prevent harm and ensure fairness in decision-making processes.
What role does transparency play in mitigating AI bias?
Helps to uncover bias in algorithms and decision-making processes.
Increases complexity and confusion in AI systems.
Limits the potential advancements in AI technology.
Ensures complete secrecy in AI development.
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Computer Vision and AI