Solving the big computing problems in the twenty-first century

Proceedings Paper Computer Science, Artificial Intelligence

Compute Trends Across Three Eras of Machine Learning

Jaime Sevilla et al.

Summary: This paper studies the trends in compute, the most readily quantifiable factor in machine learning. Three novel contributions are made, including curating a dataset with training compute of 123 milestone ML systems, framing the trends in compute in three eras, and finding a longer compute doubling time in the Deep Learning Era. The work highlights the fast-growing compute requirements for training advanced ML systems.

2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) (2022)

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Article Engineering, Electrical & Electronic

How Fast Do Algorithms Improve? [Point of View]

Yash Sherry et al.

Summary: Algorithms are crucial in computer science, enabling scientists to tackle larger problems and explore new domains. However, there is uncertainty regarding the extent and generalizability of progress in algorithms.

PROCEEDINGS OF THE IEEE (2021)

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Article Multidisciplinary Sciences

Highly accurate protein structure prediction with AlphaFold

John Jumper et al.

Summary: Proteins are essential for life, and accurate prediction of their structures is a crucial research problem. Current experimental methods are time-consuming, highlighting the need for accurate computational approaches to address the gap in structural coverage. Despite recent progress, existing methods fall short of atomic accuracy in protein structure prediction.

NATURE (2021)

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Review Physics, Applied