For an AI system that forecasts how its surroundings will change, which task would benefit most if its forecasts became more accurate?

Answer: Guiding a robot through a crowded room by predicting where people will move

Training the system without collecting new examples by trusting its model alone — Wrong. A better internal model can make learning more efficient and help the system use what it already knows. But it does not remove the need for data; the system still needs examples to learn and adapt. Trusting the model alone would not replace collecting new examples.

Guiding a robot through a crowded room by predicting where people will move ✓ — Correct! This is real-world planning: the robot must predict where people will move and choose a path through the room. More accurate forecasts of how the surroundings change would make that prediction and planning better. That matches the core claim that stronger internal models help first with planning and acting in reality.

Making an app's settings screen easier to read by enlarging its text — Wrong. Enlarging text can make an app easier to use, so it looks like a useful improvement. But this is interface polish; it does not require the system to predict how objects or people will move in the physical world. A stronger internal model would not be the main reason the text becomes readable.