What is the main practical limit on repeatedly making a neural network much larger?

True or False: With enough model capacity and enough labeled data, a learning system can in principle achieve strong results on many tasks.

Training a very large model is _____, so increasing model size has practical limits.

Match each scaling approach with the limitation it eventually runs into.

Order the steps that explain why model and data scaling have practical limits.

Why Infinite Scaling Is an Idea, Not a Plan

Practical limits when scaling a fraud model

What two practical bottlenecks limit endless growth in model size and training data?

What can stop a team from relying on ever more training data?

True or False: Making a neural network much larger always makes training faster and removes compute bottlenecks.