How to find what problems the top machine learning research teams in the world are working on?

In the third week of the Neural Networks and Deep Learning interview course, interviewee Ian Goodfellow said that engineers working in machine learning can help by providing solutions to research problems that world-class machine learning research teams are focusing on solving, and Upload it to GitHub so that it is likely to be noticed by the school’s research team and receive the opportunity to join the research team. My question is, how do I know what problems the top machine learning research teams in the world are working on? How to find out?

Hey @Carrie_Young,
The process is relatively simple. The first step is to curate a list of the “top ML research teams” in the world. These could include teams from companies like Meta, Google, DeepMind, OpenAI, Microsoft, IBM, etc, and even from independent research organizations. They are relatively easy to find with some Google searches.

Once you have curated the list, you can go through the website of each of these research teams, and they usually have a list of some of their ongoing projects. You can go through these projects to see which one you would like to work upon. I hope this helps.

Cheers,
Elemento

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Thank you very much, you have provided me with great help and guidance in finding my way.

To the previous advice I would like to add email alerts on Google Scholar and Arxiv. Stanford labs for instance work on highly interesting theoretical subject and communicate about it on a wide audience, so it’s easy to find blog articles on interesting contemporary subjects, then see who works on that theme, and then subscribe to their publications on Scholar.

This way you can have your weekly alerts. On articles you can start by reading the abstracts and introductions to get familiar with the contextes.

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Hi, thanks for your valuable suggestions! I tried to create alert on Scholar and subscribe to their publications by following them. Hope my procedure on Scholar and Arxiv is correct. :nerd_face:





You’re doing very well :smiley:
Are you initialy a physics researcher ? Or was it just a coincidence that you found an article on ML for Feynman diagrams ? :slight_smile:

Hi, thanks. I found feynman diagrams by accident when I was searching for neural networks. I am very happy that neural networks intersect with many disciplines, but I should say that this is a coincidence. :smile:
I’m a junior machine learning engineer. I have done some projects in Reinforcement Learning, Deep Learning and Machine Learning. I feel very interested in research and I want to improve myself in the research field and read more papers to expand my project experience. :nerd_face:

Owww what a pity :grin: It would have been so nice to find a physicist !
Another thing I think about is that you can see what editor you can access through your university library. For instance I use my library to access Springer books, Nature articles and many others, that I download to read during my commute time (then erase as soon as finished sine I care about legality and author rights :slight_smile: ). There you can find many reports of annual conferences on specialized topics (medical and so on…)
I can’t find example now since I’m in a waiting room, but can send some later