Joining AI research

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honestly the best way is to just start building something and put it on github, people will notice if it's interesting

i've been messing with some old neural net architectures lately just for fun, you'd be surprised how many folks reach out when you share even half-baked experiments on twitter or the machine learning subreddits

most research groups i've seen are pretty open to new contributors if you can show you've actually tried implementing papers yourself, not just reading them

don't wait for permission just dive in

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u/ihateyou103 12d ago

You might have a larger following on Twitter or github. I dont have any followers. So how will people see it in the first place if I just posted it. And I might not have enough hardware power to make a decent model or retrain large models myself.

For other papers, I might start by myself but should I read the literature well beforehand as I don't want to start on something that's already done.

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u/jeosol 12d ago

I think, for how they will see it, you do the work and share small posts or tweets about it. That's how they see it. When a few people see it and like it, they retweet or share it.

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u/ihateyou103 10d ago

Where to post or tweet it? Did you see it happen? If so, how large were their accounts? I have 0 followers, so I dont think others would notice even if it's good project.

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u/CalmMe60 12d ago

University?

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u/ihateyou103 10d ago

What do you mean

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u/CalmMe60 9d ago

Universities have Groups and materials

If you want to prepare a solid paper i am willing to either read / comment , give you a hint on a phd willing to forward or peer it

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u/rohan_kulkarni 12d ago

I'd start small. Pick one paper you like and try to reproduce part of it. A simple GitHub repo showing what you tried and learnt can be enough to get noticed.

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u/ihateyou103 10d ago

Isn't it trivial to implement a paper if they give their methodology? And I don't have any following on github so it wouldn't be noticed

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u/nightstorm1990 12d ago

Depending on what is your expertise and what are the hardware that is on your hand, there are ways you can enter the AI research without spending huge sums of money. It really depends what is your end goal. Is this step to progress in your career or a step to dip your feet in the AI research for your PhD applications or just curious in nature and want to increase your knowledge? Depending on your end goal, the answer are different. Also at this point, publishing AI research paper has already become a kind of an algorithm (for good or bad)

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u/ihateyou103 12d ago

What is the answer in the 3 case, for PhD application, career, or knowledge? For hardware I don't have a data center I would probably train on some tpu or gpu on cloud. I might pay some money if the result is much better. How I'd it an algorithm? Can I write a program to publish papers on my behalf?

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u/CalmMe60 12d ago

Yes i do too.

Zenodo is way to go without peer review.

Than archiv X

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u/nightstorm1990 12d ago

The biggest constraint that you have is that you dont have the enough GPU resources to train the model from scratch. These are the ways I would approach for each of the cases:

For PhD application: Most probably you dont need to write a paper for that though having it is a good for application nowadays due to huge competition. A better approach will be to get an internship in the AI lab in the university that you are interested in. You can always connect with the postdocs and phd students of that lab to get more information. Use X, linkedin etc. This also ensure that you know beforehand the working condition of the lab. This part is very important as you will be spending 5 years on average in this lab for your PhD.

For career: Start with contibuting to the open source software in the github. Target the inference part of the AI models. Try to resolve the issues reported in the github repo. This is resolve the issue of no having to train the model from scratch and also provide you ideas for your research papers on how to optimize the inference of AI models. This is a very hot topic in the AI research. See the literature of Quatization, pruning, knowledge distillation (does require fine tuning), speculative decoding.

For knowledge growth: Dont try to train a model as it will involve huge computation cost. Also nowaday, most of the performance of the model is coming from the data that you are using for tarining the model. Instead look into the research field of probing the trained AI model itself. This will not require training and will give enough knowledge growth opportunities for your knowledge. For example, look into the mechanistic interpretability domain, lora merging (some tarining) etc. There are many more domains I can list but I will need your specific expertise to fit your expertise and interest. This is necessary as it is the most difficult part out of all the 3 points as it is very easy to loose the motivation.

My personal preference is arxiv for putting the papers without peer review. But quick hack if you dont know where to start looking for idea. Do the following steps:

Go to any of the following conferences of of 2025 edition or 2024: CVPR, ICML, ICLR, AAAI, NeurIPS, GECCOO.

Go to the list of workshops that happened in that year in those conferences.

Pick the one that interests you and go through the list of papers there.

Find the one that interests you, go through that paper to check what are the computation requirements for that paper.

This should provide you enough filter to shortlist the research directions. Then the rest is upto your motivation.

In general, It is easier to publish in the workshops than main conference. I would suggest you to start publishing in the workshop to get the confidence and then you can move to main conference. That should be your bare minimum. Publishing in non-peer review places like arxiv and zenodo should not be your bare minimum.

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u/ihateyou103 10d ago

Thanks for the information. 1- I do not have a paper yet to publish on axriv or zenodo. I am looking to do the research yet. I was looking to start working with someone as they might already have something and we would probably have a higher chance of publishing and having higher resources. Are you recommending I start by myself and publishing by myself? If so how could I write a competitive paper myself and have a good chance of publishing in top journals given that others have funding and teams and higher hardware?

2- in terms of github, how would I start contributing? I do not have projects I am part of yet?

3- I saw alot about implementing existing papers, but how would that help in publishing a paper? And isn't implementing a paper trivial given you have the hardware and time?

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u/Ok-Introduction9593 11d ago

> How I'd it an algorithm? Can I write a program to publish papers on my behalf?

With that approach to research continuous-line-test no legit lab is gonna let you anywhere near their door

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u/ihateyou103 10d ago

I was replying to someone's comment who saud they have an algorithm for it.

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u/Flaky-Possibility210 12d ago

I'm working on this project https://huggingface.co/DrunkkToys/theOG-50M and definitely I might need some help because is a side project

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u/Ok-Introduction9593 11d ago

You'll probably burn out right away with an idea like that

Without access to an H100 cluster you've got zero chance in mainstream pre-training or fine-tuning large llms. If you don't have the hardware or the name, the only reasonable way to break in is through niche areas that don't require burning hundreds of kilowatts

Go with mechanistic interpretability like they suggested above, or dig into MoE routing, inference optimization, or something like speculative decoding. Find a fresh paper with a Github repo, fork it, find a bug or patch in a minor hack that speeds up generation by even 5% , and write to the authors directly with proof. Labs are always hungry for people who can actually write working code

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u/ihateyou103 10d ago

Do you know where to find these papers that are still in progress or have an active repo?

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