Question: In a concise essay, explain the relationship between the age of deep learning ideas and the recent surge in their real-world impact, referencing the two drivers Ng identifies.

Sample answer: Deep learning's core ideas, such as neural networks, have existed for decades but lacked the resources to reach their potential. Recent progress is not due to new theoretical breakthroughs but to two enabling factors: data availability, meaning far more labeled data now exists to train models, and computational scale, meaning far more processing power is available to train larger models on that data. Together, these two factors allowed old ideas to finally demonstrate strong performance, explaining why deep learning is 'taking off now' rather than decades ago.

Key points:

• Deep learning ideas are decades old
• Progress is driven by data availability and computational scale, not new ideas
• Both factors work together to enable performance gains
• This explains why deep learning is only now taking off

Rubric: Full credit requires identifying that the ideas are old, naming both data availability and computational scale as drivers, and explaining how their combination enabled progress. Partial credit for naming only one driver or omitting the historical context.