[leakydropout]

High school dropout with AI research cited in Nature.

You are going to have a hard road in front of you if you want to seriously make a dent in AI research. ML research is a bit more accessible to outsiders than AI research, because a lot of fundamental AI research can be a bit "out there", and is praised/discarded depending on the tenure/authority of the authors. But you probably already knew this. Just be careful to avoid (meta-)theoretical research that is close to futurism or philosophy without any credentials: It is easier to label outsiders as kooks.

First, create a blog. Write articles in a way that they are accessible to your skill/knowledge level one year ago. Get in the habit of writing and performing write-ups of your experiments. Share good articles on social media (Twitter, DataTau, /r/machinelearning).

(Co-)author a workshop paper for an AI or ML conference. Workshops have lower bars for acceptance. If anything, you'll receive valuable feedback.

Find an (assistant) professor who is an expert in a topic/subject you are interested in (don't go for Hinton in the first try). Familiarize yourself with their work and send them a polite short email asking for (search term) pointers on your research, their research, or related works.

Replicate as many research papers as you can. Implement the papers that don't have accompanying source code. Get in the habit of running many experiments. Post these on Github. Publish on social media, mentioning original authors. Mint a DOI.

Benchmarks (competitions) don't care about your credentials. Win one / do very well, and you'll create a platform for your research and methods, based purely on practical results and in compared to many other techniques. Papers based on winning results are fairly easy to write and do well impact-wise.

Really, don't worry about getting scooped, or making an error that embarrasses your supervisor for years. Leave that for the PhD's. Just get something out there, a Wordpress blog is enough. If your research is useless, no harm done (it won't get any cites if you managed to publish it in a journal). If your research has value, you'll have plenty of researchers read it and get inspired: This is your contribution to science (and, unfortunately, don't expect much cites to a blog post).

Optionally, solve the college dropout problem instead. If you want to dedicate yourself to being a researcher on AI, give your "startup" some rocket fuel and get a higher education in the field of data science/physics/computer science/AI. The synergy of young smart people and older wiser academics is something that is very hard to replicate on your own.

[leakydropout]

One I forgot is to work with either algorithms or datasets created by other researchers. Once I send an email asking for access to a certain dataset, describing the idea I had for it, and had a very famous researcher reply that they'd be interested in a cooperation.

The other side of this advice is to create a dataset that is interesting to other researchers.

[mindcrime]

Once I send an email asking for access to a certain dataset, describing the idea I had for it, and had a very famous researcher reply that they'd be interested in a cooperation.

I had a similar experience, as somebody who is also a "college dropout" and not formally associated with academia at all. I emailed a professor who wrote a book I was reading and asked for access to some of the datasets he cited in the book, and explained that I wanted to try re-implementing his technique using a newer tech stack (the book was from the 80's mind you) and then look at extending the ideas somewhat.

He quickly replied with the data, a pre-print of a new paper he was working on, and an invitation to keep him in the loop on my work. Not an outright invitation to collaborate, but I suspect if I achieve a useful result, the opportunity may well be there.

[supermdguy]

Those are some great tips.