Tao Zhexuan highly recommends and checks it personally: Just follow this list to learn AI for Math

In the field of AI for Math, if you have been unable to find the right resources, this list may be helpful.

Just now, the personal blog of famous mathematician Terence Tao has been updated again. This time they have compiled a list of useful resources, which focus on AI for Math and are designed for those who want to enter the field of mathematical AI. provide help.

The initiative of this list can be traced back to last year. The initiating organization was proposed by the seminar "Artificial Intelligence Assisted Mathematical Reasoning" organized by the National Academy of Sciences, Engineering and Medicine of the United States. Terence Tao served as the host of the seminar.

Currently, the URL resources have been made public.

Website: https://docs.google.com/document/d/1kD7H4E28656ua8jOGZ934nbH2HcBLyxcRgFDduH5iQ0/edit

In the introduction to the list we see that this is a preliminary resource list, originally provided by UIUC It has been compiled by Professor Talia Ringer for those who want to enter the field of AI mathematics. However, this document has not yet been fully finalized. Tao Zhexuan and other researchers are still improving it (we can still see various traces of modifications).

According to the catalog, we can see that the list resources are very rich. There are recommended textbooks, course resources, community discussions, recommended tools, and more.

In the course column, we also see the machine learning course of the well-known AI scholar Andrew Ng appearing in the recommended list (you can go directly to it by clicking on the link, which is very convenient).

For more details on this list, let’s read on.

Regarding education, the list recommends some available textbooks and survey reports, wikis and glossaries, tutorials, data sets and benchmarks, course materials, etc.

Since AI for Math is a highly collaborative cross-cutting field, it is very beneficial to communicate with those who have complementary expertise and experience. Based on this, the list recommends some community forums to facilitate discussion.

Studying AI and mathematics is of course inseparable from tools and code libraries. The recommended list includes machine learning frameworks, proof assistants, mathematical tools, mathematical libraries, etc.

In today's world where large models are flooding the screen, AI for Math naturally requires LLM. This resource list gives accessible general models. The familiar LLMs are all in the list:

for mathematics LLM for formal proof:

LLM for formal proof:

and chatbot:

After seeing this list, everyone said that it is very helpful for both students and teachers.

One last reminder, this list is still being improved, and you can check the changes at any time.

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