A vast amount of technological knowledge (e.g. how to create poisons, manufacture bombs) has mass destructive potential if it can be scaled. The difficulty, just like with AI, is scaling, and this mostly self-regulates (with help from the government).

For example, you can build dangerous explosive devices in your garage. That knowledge is widely available (google "Anarchists Handbook"). If you try and build thousands of them (enough to cause mass destruction) the government will notice, and most likely, you aren't going to have enough money and time to do it.

The exact same thing will happen for "dangerous uses of AI". The only actors which have the hardware and capital to cause mass destruction with AI are the big tech firms developing AI. Try running inference at scale on even a 30B parameter model right now. It's extremely difficult unless you have access to multiple server-grade GPUs which are very expensive and hard to get ahold of even if you had the money.

This is great. I would encourage my organization to use this, except the restriction to T4 GPUs renders this somewhat unusable for us. What’s the ETA on more modern GPUs?

If you have the time, reach out to professors and ask if they have internships at their lab. If you have the financial resources, you can mention that you’re not looking to get paid. Again, this will work best if you already have some previous research you can show that attests to your research ability.

In my case, I did research on my own before reaching out to potential advisors. They were all impressed by me having published papers on my own. I want to emphasize that publishing on your own isn’t difficult especially if you target conferences that are lower tier (look up ML conferences by acceptance rates) or workshops at top conferences (CVPR, NeurIPS, etc). You don’t even have to publish, having a high quality paper on the arXiv is often good enough.

That would be my recommendation to you. DO NOT wait for a mentor to take you under his wing or a professor to “help” you with research. They will not do that. Most of them are super busy and are looking for someone who can immediately jump in on a project and know what to do. I promise you that you’re smart enough to start doing research now, and if you start now it will be immensely easier for you to get opportunities 5-6 months from now.

OP, I was in the same spot a couple of years ago. You're facing an uphill battle. The reality of it is that academia is a very closed world, and you'll find it very difficult to get in unless you have any of the following: (a) an elite college background (b) publications (c) personal relationship with a potential advisor.

Realistically, (a) and (c) are not attainable. I was in the same position. But I was able to get into a top 15 ML PhD program after a couple of years of sustained effort. Look into two things: doing a masters program at a good uni for CS (use csrankings.net, notice that many of the most productive CS schools aren't super difficult to get into) and most importantly — getting published. You don't need to get published at a top conference like NeurIPS or CVPR. As a non-PhD student, if you can get 1-2 workshop papers (very easy, workshops have like a 50% acceptance rate) and a paper at a lower tier conference (again, high acceptance rate, like 50%), you'll be in a very good position. This is how I did it. Combine that with reaching out to potential advisors beforehand and you'll have a extremely good shot at getting in and having your pick.

Let me know if you need any more advice.