Question 24·Hard·Central Ideas and Details
Computer scientist Melody Sun argues that the future of artificial intelligence (AI) should be judged less by the sophistication of algorithms than by the quality of the data used to train them. She notes that even elegantly designed models will replicate the biases embedded in their datasets, and she warns that current corporate practices favor easily collectible data over representative data. Sun does not dismiss advances in model architecture; rather, she contends that the prevailing fascination with ever-larger neural networks obscures a more pressing concern: ensuring that training data capture the diversity and complexity of human experience. Until that concern is addressed, she predicts, progress in AI will remain impressive but brittle—remarkable in demonstration yet unreliable in real-world application.
Which choice best states the main idea of the text?
For main-idea questions, start by quickly paraphrasing the passage in 1–2 simple sentences, focusing on what the author argues overall, not on a single detail. Pay special attention to the first and last sentences and look for repeated themes or contrasts (here, algorithms vs. data). Then test each answer: eliminate choices that bring in ideas the passage never mentions, exaggerate the author’s point (turning a concern into an absolute), or focus on a side detail instead of the central contrast or claim. The correct choice should capture both the main topic and the author’s attitude or judgment about it.
Hints
Use the beginning and the end
Reread the first and last sentences. What does Sun say AI should be judged by, and what does she predict will happen to AI progress if a certain concern isn’t addressed?
Focus on what Sun thinks is the bigger problem
Sun mentions both algorithms (model architecture, neural networks) and data. Which one does she treat as the more pressing issue that needs to be fixed for AI to be reliable?
Watch out for off-topic or extreme claims
Check each option: does it make Sun’s point more extreme (for example, claiming bias can never be improved) or does it wrongly suggest she thinks algorithms don’t matter at all? If so, look back at how she balances the role of model design with the need for representative data.
Step-by-step Explanation
Identify the task: main idea
The question asks for the main idea of the text, so you need an answer that summarizes the author’s overall argument, not a minor detail or an example. Focus especially on the first and last sentences, then check how the middle supports them.
Restate the author’s argument in your own words
Paraphrase the passage: Sun says we should judge AI more by the quality of its training data than by how fancy the algorithms are. She points out that models will copy any biases in their data and criticizes companies for preferring easy-to-get data instead of truly representative data. She is worried that hype about bigger neural networks is distracting from this deeper problem.
Notice the key contrast and condition
Sun does not reject better algorithms or larger neural networks. Instead, she argues that without fixing the data problem—making training data reflect the full diversity and complexity of human life—AI progress will stay "impressive but brittle," meaning it will look great in demos but fail in real-world use. So the key idea is: bigger/more sophisticated models alone are not enough; the limiting factor is data quality and representativeness.
Match your paraphrase to the answer choices
Now compare your summary to the options:
- Eliminate choices that turn Sun’s concern into an absolute claim (for example, that bias can never be reduced) or that incorrectly say algorithms don’t matter at all.
- Eliminate choices that shift her point into a claim about where the “most exciting advances” are occurring.
- Choose the one that says large neural networks by themselves will not significantly improve AI unless the training data become more representative.
That choice is: “Melody Sun believes that expanding the size of neural networks will not meaningfully improve AI performance unless the data used to train them become more representative.”