# Parameter counts in Machine Learning

**In short:** we have compiled information about the date of development and trainable parameter counts of n=139 machine learning systems between 1952 and 2021. This is, as far as we know, the biggest public dataset of its kind. You can access our dataset [here](https://docs.google.com/spreadsheets/d/1AAIebjNsnJj_uKALHbXNfn3_YsT6sHXtCU0q7OIPuc4/edit#gid=0), and the code to produce an interactive visualization is available [here](https://colab.research.google.com/drive/11m0AfSQnLiDijtE1fsIPqF-ipbTQcsFp?usp=sharing).

We chose to focus on parameter count because previous work indicates that it is an important variable for model performance \[1\], because it helps as a proxy of model complexity and because it is information usually readily available or easily estimable from descriptions of model architecture. 

We hope our work will help AI researchers and forecasters understand one way in which models have become more complex over time, and ground their predictions of how the field will progress in the future. In particular, we hope this will help us tease apart how much of the progress in Machine Learning has been due to algorithmic improvements versus increases in model complexity.

It is hard to draw firm conclusions from our biased and noisy dataset. Nevertheless, our work seems to give weak support to two hypotheses:

*   There was no discontinuity in any domain in the trend of model size growth in 2011-2012. This suggests that the Deep Learning revolution was not due to an algorithmic improvement, but rather the point where the trend of improvement of Machine Learning methods caught up to the performance of other methods.
*   In contrast, it seems there has been a discontinuity in model complexity for language models somewhere between 2016-2018. Returns to scale must have increased, and shifted the trajectory of growth from a doubling time of ~1.5 years to a doubling time of between 4 to 8 months.

The structure of this article is as follows. We first describe our dataset. We point out some weaknesses of our dataset. We expand on these and other insights. We raise some open questions. We finally discuss some next steps and invite collaboration.

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/afce9ae98a1eeac853fb148b12b41fc6162e0fd934bdbdf8.png)

Model size of popular new Machine Learning systems between 1954 and 2021. Includes n=139 datapoints. See expanded and interactive version of this graph [here](https://vega.github.io/editor/#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).

![](https://39669.cdn.cke-cs.com/rQvD3VnunXZu34m86e5f/images/c9f5006bfc9850bbc06a938271f2e17cd9056daca1eee9eb.png)

Model size of popular new Machine Learning systems between 2000 and 2021. Includes n=114 datapoints. See expanded and interactive version of this graph [here](https://vega.github.io/editor/#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).

### Features of the dataset

*   The dataset spans systems from 1952 to 2020, though we included far more information about recent systems (from 2010 onwards).
*   The systems we include encompass many types, including neural networks, statistical models, support vector machines, bayesian networks and other more exotic architectures. However we mostly included systems of the neural network kind.
*   The systems are from many domains and were trained to solve many tasks. However we mostly focused on systems trained to solve vision, language and gaming tasks.
*   We relied on a subjective criteria of notability to decide which systems to include. Our decisions were informed by citation counts (papers with more than 1000 citations), external validation (papers that received some kind of paper of the year award or similar) and historical importance (papers that were cited by other work as seminal). The references to this post include some overviews we used as a starting point to curate our dataset \[2-26\].
*   Several models have versions at multiple scales. Whenever we encountered this in their original publication, we recorded whichever was presented in the paper as the main one, or the largest presented version. Sometimes we recorded multiple versions when we felt it was warranted, e.g. when multiple different versions were trained to solve different tasks.

### Caveats

*   It is important to take into account that model size is hardly the most important parameter to understand the progress of ML systems. Other arguably more important indicators of non-algorithmic progress in ML systems include training compute and training dataset size \[1\].
*   Model size as a metric of model complexity is hardly comparable across domains or even architectures. For example, a mixture-of-expert model can achieve higher parameter counts but invest far less compute into training each parameter.
*   Our selection of systems is biased in many important ways. We are biased towards academic publications (since information on commercial systems is harder to come by). We include more information about recent systems. We tended to include information about papers where the parameter counts were readily available, in particular larger models that were developed to test the limits of how large a model can be. We are biased towards papers published in English. We mostly focused on systems on vision, language and gaming tasks, while we have comparatively fewer papers on e.g. speech recognition, recommender systems or self driving. Lastly, we are biased towards systems we personally found interesting or impressive.
*   Recollecting the information was a time consuming exercise that required us to read through hundreds of technical papers to gather the parameter counts. It is quite likely we have made some mistakes.

### Insights

*   Unsurprisingly, there is an upward trend in model size. The trend seems exponential, and seems to have picked up its pace recently for language models. An eyeball estimate of the slope of progress suggests that the doubling rate was between 18 and 24 months from 2000 to 2016-2018 in all domains, and between 3 and 5 months from 2016-2018 onward in the language domain.
*   The biggest models in terms of trainable parameters can be found in the language and recommender system domains. The biggest model we found was the 12 trillion parameter Deep Learning Recommender System from Facebook. We don’t have enough data on recommender systems to ascertain whether recommender systems have been historically large in terms of trainable parameters.
*   Language models have been historically bigger than in other domains. This was because of statistical models whose parameterization scales with vocabulary size (e.g. as in the Hiero Machine Translation System from 2005) and word embeddings that also scale with vocabulary size (e.g. as in Word2Vec from 2013).
*   Arguably Deep Learning started to proliferate in computer vision before it reached language processing (both circa 2011-2013), however the parameter counts of the second far surpass those of the first today. In particular, somewhere between 2016-2018 the trend of growth in language model size apparently greatly accelerated its pace, to a doubling time of between 4 and 8 months.
*   Architectures on the game domain are small in terms of trainable parameters, below  vision architectures while apparently growing at a similar rhythm. Naively we expected otherwise, since playing games seems more complicated. However, in hindsight, what determines model size is what are the returns to scale; in more complex domains we should expect lower effective model sizes, as the models are more constrained in other ways.
*   The trend of growth in model size has been relatively stable through the transition into the deep learning era in 2011-2012 in all domains we studied (though it is hard to say with certainty given the amount of data). This suggests that the deep learning revolution was less of a paradigm change and more of a natural continuation of existing tendencies, which finally surpassed other non-machine learning methods.

### Open questions

*   Why is there a discrepancy in the trainable parameters magnitude and trend of growth in e.g. vision systems versus e.g. language systems? Some hypotheses are that language architectures scale better with size, that vision models are more bottlenecked on training data, that vision models require more compute per parameter or that the language processing ML community is ahead in experiment with large scale models (e.g. because they have access to more compute and resources).
*   What caused the explosive growth in the size of language models from 2018 onwards? Was it a purely social phenomena as people realized the advantages of larger models, was it enabled by the discovery of architectures that scaled better with size, compute and data (e.g. transformers?) or was it caused by something else entirely?
*   Do the scaling laws of Machine Learning for pre-and-post-deep-learning actually differ significatively? So far model size seems to suggest otherwise, what about other metrics?
*   How can we more accurately estimate the rates of growth for each domain and period? For how long will current rates of growth be sustained?

### Next steps

*   We are interested in collaborating with other researchers to grow this dataset to be more representative and correcting any mistakes. As an incentive, we will pay $5 per mistake found or system addition (up to $600 total among all submissions; please contact us if you want to contribute with a donation to increase the payment cap). You can send your submissions to jaimesevillamolina at gmail dot com, preferably in spreadsheet format.
*   We are interested in including other information about the systems, most notably compute and training dataset size.
*   We want to include more information on other domains, specially on recommender systems.
*   We want to look harder for systematic reviews and other already curated datasets of AI systems.

### Acknowledgements

*This article was written by Jaime Sevilla, Pablo Villalobos and Juan Felipe Cerón. Jaime’s work is supported by a Marie Curie grant of the NL4XAI Horizon 2020 program.*

*We thank Girish Sastry for advising us on the beginning of the project, the Spanish Effective Altruism community for creating a space to incubate projects such as this one, and Haydn Belfield, Pablo Moreno and Ehud Reiter for discussion and system submissions.*

### Bibliography

1.  *Kaplan et al., “Scaling Laws for Neural Language Models,” 08361.*
2.  *1.6 History of Reinforcement Learning*. (n.d.). Retrieved June 19, 2021, from [http://incompleteideas.net/book/first/ebook/node12.html](http://incompleteideas.net/book/first/ebook/node12.html)
3.  *AI and Compute*. (n.d.). Retrieved June 19, 2021, from [https://openai.com/blog/ai-and-compute/](https://openai.com/blog/ai-and-compute/)
4.  *AI and Efficiency*. (2020, May 5). OpenAI. [https://openai.com/blog/ai-and-efficiency/](https://openai.com/blog/ai-and-efficiency/)
5.  *AI Progress Measurement*. (2017, June 12). Electronic Frontier Foundation. [https://www.eff.org/ai/metrics](https://www.eff.org/ai/metrics)
6.  *Announcement of the 2020 ACL Test-of-Time Awards (ToT) | ACL Member Portal*. (n.d.). Retrieved June 19, 2021, from [https://www.aclweb.org/portal/content/announcement-2020-acl-test-time-awards-tot#:~:text=Each%20year%2C%20the%20ACL%20Test,papers%20from%2010%20years%20earlier.&text=The%20winners%20were%20announced%20at%20ACL%202020.](https://www.aclweb.org/portal/content/announcement-2020-acl-test-time-awards-tot#:~:text=Each%20year%2C%20the%20ACL%20Test,papers%20from%2010%20years%20earlier.&text=The%20winners%20were%20announced%20at%20ACL%202020.)
7.  Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? 🦜. *Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency*, 610–623. [https://doi.org/10.1145/3442188.3445922](https://doi.org/10.1145/3442188.3445922)
8.  *Best paper awards—ACL Wiki*. (n.d.). Retrieved June 19, 2021, from [https://aclweb.org/aclwiki/Best\_paper\_awards](https://aclweb.org/aclwiki/Best_paper_awards)
9.  *bnlearn—Bayesian Network Repository*. (n.d.). Retrieved June 19, 2021, from [https://www.bnlearn.com/bnrepository/](https://www.bnlearn.com/bnrepository/)
10.  *Brian Christian on the alignment problem*. (n.d.). 80,000 Hours. Retrieved June 19, 2021, from [https://80000hours.org/podcast/episodes/brian-christian-the-alignment-problem/](https://80000hours.org/podcast/episodes/brian-christian-the-alignment-problem/)
11.  *Computer Vision Awards – The Computer Vision Foundation*. (n.d.). Retrieved June 19, 2021, from [https://www.thecvf.com/?page_id=413](https://www.thecvf.com/?page_id=413)
12.  DARPA Grand Challenge. (2021). In *Wikipedia*. [https://en.wikipedia.org/w/index.php?title=DARPA\_Grand\_Challenge&oldid=1021627196](https://en.wikipedia.org/w/index.php?title=DARPA_Grand_Challenge&oldid=1021627196)
13.  Karim, R. (2020, November 28). *Illustrated: 10 CNN Architectures*. Medium. [https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d](https://towardsdatascience.com/illustrated-10-cnn-architectures-95d78ace614d)
14.  Mohammad, S. M. (2020). Examining Citations of Natural Language Processing Literature. *Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*, 5199–5209. [https://doi.org/10.18653/v1/2020.acl-main.464](https://doi.org/10.18653/v1/2020.acl-main.464)
15.  Mudigere, D., Hao, Y., Huang, J., Tulloch, A., Sridharan, S., Liu, X., Ozdal, M., Nie, J., Park, J., Luo, L., Yang, J. A., Gao, L., Ivchenko, D., Basant, A., Hu, Y., Yang, J., Ardestani, E. K., Wang, X., Komuravelli, R., … Rao, V. (2021). High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models. *ArXiv:2104.05158 \[Cs\]*. [http://arxiv.org/abs/2104.05158](http://arxiv.org/abs/2104.05158)
16.  Nilsson, N. (1974). Artificial Intelligence. *IFIP Congress*. [https://doi.org/10.7551/mitpress/11723.003.0006](https://doi.org/10.7551/mitpress/11723.003.0006)
17.  Posey, L. (2020, April 28). *History of AI Research*. Medium. [https://towardsdatascience.com/history-of-ai-research-90a6cc8adc9c](https://towardsdatascience.com/history-of-ai-research-90a6cc8adc9c)
18.  Raschka, S. (2019). A Brief Summary of the History of  Neural Networks and Deep Learning. *Deep Learning*, 29.
19.  Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2020). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. *ArXiv:1910.01108 \[Cs\]*. [http://arxiv.org/abs/1910.01108](http://arxiv.org/abs/1910.01108)
20.  Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020). The Computational Limits of Deep Learning. *ArXiv:2007.05558 \[Cs, Stat\]*. [http://arxiv.org/abs/2007.05558](http://arxiv.org/abs/2007.05558)
21.  Vidal, R. (n.d.). *Computer Vision: History, the Rise of Deep Networks, and Future Vistas*. 60.
22.  Wang, B. (2021). *Kingoflolz/mesh-transformer-jax* \[Jupyter Notebook\]. [https://github.com/kingoflolz/mesh-transformer-jax](https://github.com/kingoflolz/mesh-transformer-jax) (Original work published 2021)
23.  *Who Invented Backpropagation?* (n.d.). Retrieved June 19, 2021, from [https://people.idsia.ch//~juergen/who-invented-backpropagation.html](https://people.idsia.ch//~juergen/who-invented-backpropagation.html)
24.  Xie, Q., Luong, M.-T., Hovy, E., & Le, Q. V. (2020). Self-training with Noisy Student improves ImageNet classification. *ArXiv:1911.04252 \[Cs, Stat\]*. [http://arxiv.org/abs/1911.04252](http://arxiv.org/abs/1911.04252)
25.  Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018). Recent Trends in Deep Learning Based Natural Language Processing. *ArXiv:1708.02709 \[Cs\]*. [http://arxiv.org/abs/1708.02709](http://arxiv.org/abs/1708.02709)
26.  Zhang, B., Xiong, D., Su, J., Lin, Q., & Zhang, H. (2018). Simplifying Neural Machine Translation with Addition-Subtraction Twin-Gated Recurrent Networks. *ArXiv:1810.12546 \[Cs\]*. [http://arxiv.org/abs/1810.12546](http://arxiv.org/abs/1810.12546)
27.  Zoph, B., & Le, Q. V. (2016). *Neural Architecture Search with Reinforcement Learning*. [https://arxiv.org/abs/1611.01578v2](https://arxiv.org/abs/1611.01578v2)