AI and efficiency
Compute Scaling Footnotes A In the sorting example, the “difficulty” of the problem is the length of the list. The cost for quicksort, a commonly used algorithm is denoted in Big O notation: O(nlogn) O(n\log{}n) B Inference costs dominate total costs for successful deployed systems. Inference costs scale with usage of the system, whereas training costs only need to be paid once. C Throughout this post we refer to Moore’s Law as the consistent, long-observed 2-year doubling time of dollars/flop. One could also interpret Moore’s Law as the trend in dollars/flop, that has recently slowed down. D For instance algorithmic progress could change the complexity class on some task from exponential to polynomial cost. Such efficiency gains on capabilities of interest are intractable to directly observe, though they may be observable through asymptotic analysis or extrapolating empirically derived scaling laws. E Making credible forecasts on such topics is a substantial enterprise, we’d rather avoid here than give insufficient treatment. F In fact, this work was primarily done by training PyTorch examples models, with tweaks to improve early learning. G ImageNet is the only training data source allowed for the vision benchmark. No human captioning, other images, or other data is allowed. Automated augmentation is ok. References 1 Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). “ ImageNet: A Large-Scale Hierarchical Image Database(opens in a new window) .” In CVPR09. 2 Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). “ Imagenet classification with deep convolutional neural networks(opens in a new window) .” In F. Pereira, C. J. C. Burges, L. Bottou, & K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25 (pp. 1097–1105). 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NIPS ML SYSTEMS WORKSHOP, 2017(opens in a new window) .” 29 30 31 van den Oord, A., Li, Y., Babuschkin, I., Simonyan, K., Vinyals, O., Kavukcuoglu, K., van den Driessche, G., Lockhart, E., Cobo, L. C., Stimberg, F., Casagrande, N., Grewe, D., Noury, S., Dieleman, S., Elsen, E., Kalchbrenner, N., Zen, H., Graves, A., King, H., Walters, T., Belov, D., & Hassabis, D. (2017). “ Parallel wavenet: Fast high-fidelity speech synthesis.(opens in a new window) .” 32 Authors Danny Hernandez, Tom Brown Acknowledgments We’d like to thank the following people helpful conversations and/or feedback on this post: Dario Amodei, Jack Clark, Alec Radford, Paul Christiano, Sam McCandlish, Ilya Sutskever, Jacob Steinhardt, Jared Kaplan, Amanda Askell, John Schulman, Jacob Hilton, Asya Bergal, Katja Grace, Ryan Carey, Nicholas Joseph, Geoffrey Irving, Jeff Clune, and Ashley Pilipiszyn. Thanks to Justin Jay Wang for design. Thanks to Niki Parmar for providing the relevant points from the original transformer(opens in a new window) learning curves. Also thanks to Mingxing Tan for providing the relevant points from EfficientNet(opens in a new window) learning curves and running an experiment with reduced warmup.