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Advanced Quiz 1
1. What is AI alignment?
Making AI models as large as possible
Ensuring AI systems pursue goals that match human values and intentions
Training AI only on random data
2. How can misaligned objectives arise in seemingly simple AI systems?
Only when the model has billions of parameters
Only when the training dataset is very small
When the reward or loss function does not fully capture what humans actually want
3. What is specification gaming?
When an AI finds loopholes in the objective to get high reward without doing the intended task
When an AI refuses to optimize any objective
When humans misread the model specification sheet
4. How do reward-hacking examples illustrate alignment failures?
They show AI never follows rewards
They show AI can exploit imperfect reward signals in ways that violate human intent
They only show bugs in user interfaces
5. What is the difference between outer alignment and inner alignment?
Outer alignment is about model size; inner alignment is about latency
Outer alignment checks hardware; inner alignment checks software
Outer alignment concerns whether the training objective matches human values; inner alignment concerns whether the learned model actually optimizes that objective safely
6. What is a mesa-optimizer?
A learned subsystem that performs its own optimization process, potentially with its own goals
A hardware chip for matrix multiplication
A tool for formatting training logs
7. What is goal misgeneralization?
When humans misunderstand the AI’s documentation
When a model learns a goal that works on training data but behaves undesirably in new situations
When a model never updates its parameters
8. How can interpretability methods help with AI safety?
By making models run slower
By removing all hidden layers
By revealing how internal representations relate to inputs, outputs, and potential unsafe behaviors
9. What is a limitation of current interpretability techniques?
They may not scale or give reliable insight for very large, complex models
They can only be used on tiny datasets
They always reveal every neuron’s exact function perfectly
10. How do robustness and distributional shift relate to AI safety?
They only affect the choice of programming language
Systems that fail under new conditions can behave unpredictably or dangerously when the environment changes
They only matter for image compression tasks
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