# A Year of AI Increasing AI Progress

In July, I made [a post](https://www.lesswrong.com/posts/W3tZacTRt4koHyxbr/examples-of-ai-increasing-ai-progress) about AI being used to increase AI progress, along with [this spreadsheet](http://rsi.thomaswoodside.com/) that I've been updating throughout the year. Since then, I have run across more examples, and had others submit examples (some of which were published before the date I made my original post).

2022 has included a number of instances of AI increasing AI progress. Here is the list. In each entry I also credit the person who originally submitted the paper to my list.

*   [A paper from Google Research](https://ai.googleblog.com/2022/03/offline-optimization-for-architecting.html) used a robust supervised learning technique to architect hardware accelerators \[March 17th, submitted by Zach Stein-Perlman\]
*   [A paper from Google Research and Stanford](https://arxiv.org/abs/2203.14465) fine tuned a model on its own chain-of-thought outputs, to improve performance on reasoning tasks \[March 28th, submitted by Nathaniel Li\]
*   [A paper from OpenAI](https://arxiv.org/abs/2206.05802) used LLMs to help humans find flaws in other LLMs, thereby enabling them to more easily improve those models \[June 12th, submitted by Dan Hendrycks\]
*   [A paper from Google](https://ai.googleblog.com/2022/07/mlgo-machine-learning-framework-for.html) used machine learning to optimize compilers. This is less obviously accelerating AI but an [earlier version of the compiler is used in Pytorch](https://twitter.com/pytorch/status/1452737276564344846?lang=en) so it may end up doing so. \[July 6th, submitted by Oliver Zhang\]
*   [NVIDIA used deep reinforcement learning](https://developer.nvidia.com/blog/designing-arithmetic-circuits-with-deep-reinforcement-learning/) to generate nearly 13,000 circuits in their newest GPUs. \[July 8th, submitted by me\]
*   [Google found](https://ai.googleblog.com/2022/07/ml-enhanced-code-completion-improves.html?m=1) that ML code completion improved the productivity of their engineers. Some of them are presumably working in AI. \[July 27th, submitted by Aidan O'Gara\]
*   [A paper from Microsoft Research and MIT](https://arxiv.org/abs/2207.14502) used language models to generate programming puzzle tasks for other language models. When finetuned on these tasks, the models were much better at solving the puzzles. \[July 29th, submitted by Esben Kran\]
*   [A paper from Google and UIUC](https://arxiv.org/abs/2210.11610) used outputs from a language model to fine tune a language model after a majority vote procedure was used to filter outputs. \[September 30th, submitted by me\]
*   [A paper from DeepMind](https://www.nature.com/articles/s41586-022-05172-4) used reinforcement learning to discover more efficient matrix multiplication algorithms.  \[October 5th, submitted by me\]
*   [A paper from Anthropic](https://www.anthropic.com/constitutional.pdf) used language models, rather than humans, for feedback to improve language models. \[December 16th, submitted by me\]
*   [A paper from a number of universities](https://arxiv.org/abs/2212.10560) used language models to generate examples of instruction following, which were then filtered and used to fine tune language models to follow instructions better. \[December 20th, submitted by Nathaniel Li\].

I'm writing this fairly quickly so I'm not going to add extensive commentary beyond what I said in my last post, but I'll point out here two things:

*   It is pretty common these days for people to use language model outputs to improve language models. This trend appears likely to continue.
*   A lot of these papers are from Google. Not DeepMind, Google. Google may not have declared they are aiming for AGI, but they sure do seem to be writing a lot of papers that involve AI increasing AI progress. It seems important not to ignore them.

Did I miss any? You can submit more [here](https://forms.gle/V6wukPMST6tw2PrB8).