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Anticipating Scaling Challenges in Data Availability and Architecture
Challenges in data availability and architecture may impact the scalability of fundamental theories in the field of study. Running out of data or surpassing the available compute power are practical concerns, although the speaker doubts these scenarios. From a fundamental standpoint, the speaker believes it is improbable for scaling laws to halt abruptly. The reliance on specific architectures like transformers, rather than LSTMs or RNNs, is crucial due to their ability to attend to distant past information. Overall, the speaker would be surprised if scalability issues arise from architecture limitations, as current model capabilities suggest no significant distinctions between tasks they can or cannot perform.