The paper suggests that Deep Learning has improved the performance of many tasks by using vast amounts of computational power, but the paper suggests that it may stall depending on the development of hardware, as the computational power required is growing larger and larger. It suggests that the financial and environmental burdens are also becoming prohibitive, so drastic improvements may be necessary.
TL;DR
The paper suggests that Deep Learning has improved the performance of many tasks by using vast amounts of computational power, but the paper suggests that it may stall depending on the development of hardware, as the computational power required is growing larger and larger. It suggests that the financial and environmental burdens are also becoming prohibitive, so drastic improvements may be necessary.
Why it matters:
Paper URL
https://arxiv.org/abs/2007.05558
Submission Dates(yyyy/mm/dd)
Authors and institutions
Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, Gabriel F. Manso
Methods
Results
Comments