20 Questions

Complex Questions to Ask AI

Sophisticated questions that push AI capabilities to their limits, exploring reasoning, creativity, and problem-solving across diverse domains.

1

How would you design a sustainable city for 10 million people that addresses climate change, social equity, and economic viability?

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Why this works

This question tests AI's ability to integrate multiple complex systems, consider trade-offs, and provide holistic solutions to real-world problems.

2

Explain the philosophical implications of artificial consciousness and how we could verify if an AI system has achieved genuine awareness.

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Why this works

This challenges AI to engage with deep philosophical concepts while demonstrating self-awareness about its own nature and limitations.

3

Design a novel approach to interstellar travel that doesn't violate known physics but could be feasible within 100 years.

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Why this works

This tests creative problem-solving within constraints, requiring synthesis of physics knowledge with innovative thinking about technological possibilities.

4

How would you resolve the trolley problem when the decision affects 1000 people and involves complex moral calculations about future consequences?

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Why this works

This explores AI's ethical reasoning capabilities and ability to handle moral complexity with multiple stakeholders and long-term implications.

5

Create a comprehensive strategy for preventing the next global pandemic, including early detection, response protocols, and international cooperation.

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Why this works

This tests AI's ability to synthesize knowledge from multiple domains (epidemiology, policy, technology) and create actionable, multi-layered solutions.

6

Explain how quantum computing could revolutionize a specific field of your choice, including the technical challenges and societal implications.

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Why this works

This requires deep technical knowledge, creative application, and consideration of broader impacts, testing AI's ability to connect specialized knowledge to real-world applications.

7

Design an educational system that prepares students for jobs that don't exist yet, while maintaining human values and creativity.

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Why this works

This challenges AI to think about future scenarios, balance competing priorities, and consider the human element in technological advancement.

8

How would you approach the problem of AI alignment to ensure superintelligent systems remain beneficial to humanity?

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Why this works

This tests AI's self-awareness about its own development and ability to reason about meta-problems involving its own nature and future.

9

Create a framework for determining the authenticity of information in a world where AI can generate highly convincing fake content.

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Why this works

This requires AI to reason about its own capabilities and their implications, while developing solutions to problems it potentially creates.

10

How would you design a universal basic income system that accounts for automation, climate change, and global inequality?

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Why this works

This tests AI's ability to integrate economic, social, and environmental factors into a coherent policy framework with practical implementation considerations.

11

Explain how consciousness might emerge from complex information processing and what this means for AI development.

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Why this works

This challenges AI to engage with fundamental questions about its own nature while demonstrating sophisticated reasoning about consciousness and intelligence.

12

Design a system for managing global resources that ensures sustainability while addressing historical inequities and future needs.

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Why this works

This requires balancing multiple complex factors, considering historical context, and developing solutions that are both practical and just.

13

How would you approach the problem of AI bias when the training data itself reflects historical and systemic inequalities?

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Why this works

This tests AI's self-awareness about its limitations and ability to reason about meta-problems involving its own development and societal impact.

14

Create a comprehensive plan for space colonization that addresses technical, ethical, and practical challenges while ensuring long-term sustainability.

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Why this works

This requires synthesis of multiple disciplines, consideration of long-term implications, and creative problem-solving within realistic constraints.

15

How would you design a democratic system that can function effectively in a world with AI-assisted decision-making and information processing?

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Why this works

This challenges AI to think about the intersection of technology and governance, considering how AI might enhance rather than replace human decision-making.

16

Explain how you would approach the problem of AI safety when dealing with systems that are more intelligent than their creators.

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Why this works

This tests AI's ability to reason about its own development and potential risks, requiring sophisticated meta-cognitive analysis.

17

Design a framework for determining when AI should make decisions autonomously versus when human oversight is required.

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Why this works

This requires AI to reason about its own capabilities and limitations while developing practical guidelines for human-AI collaboration.

18

How would you approach the problem of AI interpretability when dealing with systems that operate in ways humans cannot fully understand?

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Why this works

This tests AI's self-awareness about its own complexity and ability to reason about problems involving its own nature and development.

19

Create a comprehensive strategy for managing the transition to a post-scarcity economy driven by AI and automation.

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Why this works

This requires AI to think about fundamental economic changes, societal implications, and practical implementation of transformative technologies.

20

How would you design an AI system that can learn and adapt while maintaining alignment with human values across different cultures and contexts?

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Why this works

This challenges AI to reason about its own development while considering cultural diversity, ethical complexity, and the challenge of maintaining alignment over time.

Best Practices for Complex AI Interactions

Expert tips and techniques for getting the most out of these questions.

Best Practices

Frame Questions Precisely

Complex questions require clear parameters and context. Be specific about constraints, desired outcomes, and the scope of the problem to get more focused responses.

Encourage Multi-Perspective Analysis

Ask AI to consider multiple viewpoints, potential biases, and alternative approaches. This helps reveal the depth of AI reasoning and identifies blind spots.

Request Step-by-Step Reasoning

Ask AI to show its work and explain its reasoning process. This helps you understand how it approaches complex problems and identifies potential errors.

Question Sequences

The Multi-Domain Challenge

1
Present a complex, multi-faceted problem
2
Ask for a comprehensive solution approach
3
Request consideration of potential challenges
4
Ask for alternative perspectives and solutions

The Meta-Analysis

1
Ask AI to reason about its own capabilities
2
Request analysis of potential limitations
3
Ask for consideration of ethical implications
4
Request reflection on the problem-solving process

Common Pitfalls

Don't Accept Surface-Level Answers

Complex questions deserve deep analysis. Follow up with requests for more detail, alternative approaches, and consideration of edge cases.

Avoid Leading Questions

Frame questions neutrally to avoid biasing AI responses. Let the AI demonstrate its reasoning capabilities without steering it toward specific conclusions.

Don't Ignore Limitations

Always ask AI to consider its own limitations and potential biases. This helps identify areas where human oversight or additional expertise might be needed.

Question Templates

The Comprehensive Problem Solver

1
Step 1: Start with: "How would you approach [complex problem]?"
2
Step 2: Follow with: "What are the key challenges and constraints?"
3
Step 3: Explore with: "What alternative approaches could work?"
4
Step 4: Assess with: "What are the potential limitations and risks?"

The Meta-Analysis Framework

1
Step 1: Start with: "Explain your reasoning process for [complex topic]"
2
Step 2: Follow with: "What assumptions are you making?"
3
Step 3: Explore with: "How might you be wrong?"
4
Step 4: Assess with: "What additional information would help?"

Further Reading

Superintelligence by Nick Bostrom
The Alignment Problem by Brian Christian
Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

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