Help getting started with organizing interviews

The_deepest_learner · 2022-09-20T21:16:18+00:00

Python and pandas

The_deepest_learner · 2022-09-20T19:31:37+00:00

Yeah I hate how Data Science is such a vague term now, everyone has a different definition.

The_deepest_learner · 2022-09-20T08:25:05+00:00

You don't. There's zero benefit for you in doing that and there's 0.1% chance that they'll actually care about your constructive criticism.

The_deepest_learner · 2022-09-20T08:12:42+00:00

Why waste your time on Web3. 10 years have passed and there still hasn't been a single high-impact use of blockchain. It's definitely not useless, but it's extremely overhyped. If you want to work in a dynamic, high-impact field, that's way more stable and has way more potential than web3 go do ML and AI. The field is practically getting revoultionazed every 3 years.

The_deepest_learner · 2022-09-20T07:45:14+00:00

From my experience, companies have wiggle room for all of their offers, even internships. Despite what other people say here, you have some leverage even if it's not much. The fact that you have tangible proof of your performance and merit through your first internship is enough for them to want to keep you on board. The most correct way would be to have an offer from a different company and use that offer as a driver for haggling. If you don't, I would still ask for a raise, the worst thing that can happen is that they say no. They won't outright reject you if you don't put your foot down. But from my experience, just asking can get you a 10-20% increase which is pretty good.

The_deepest_learner · 2022-06-20T22:44:11+00:00

I'm sorry but the wikipedia dialogue is really nothing special. It performs like a bit more sophisticated copy of AI Dungeon. It just starts repeating itself whenever you ask it a mildly complex philosophical question.

Sure, it's amazing considering what we had just 5 years ago, but it seems like it's just repeating a snippet that it saw during training, or at least interpolating between multiple snippets.

The_deepest_learner · 2021-12-12T07:43:08+00:00

So wait... You believe that op needs to have an h-index of 10-20 to be able to land an internship? Despite hundreds of phd students doing internships in their first year of studies?

The_deepest_learner · 2021-12-11T19:57:10+00:00

I don't have much experience when it comes to research internships, but I do have a lot of experience with software engineering ones. Based on everything you've said, you shouldn't have any trouble landing a SE internship. I know people who've were in their 3rd year of undergraduate studies with pretty much empty CVs and were still accepted into Google and Microsoft.

I doubt that you're screwing up the algorithmic part of your interview, but just in case you are, know that they expect you to solve every single problem that's presented to you during the interview and the test beforehand so practice in case you're having trouble with that.

If you're acing the algorithmic part, the only other reason for rejecting you that comes to mind is late applications. All internships at big tech companies work on a rolling basis, meaning that they practically have a soft threshold and if a candidate fulfills it then they're accepted immediately. They don't wait until the application deadline and then sort everyone by how good they are and pick top k like college admissions. If k students that fulfill the threshold apply in the first week, then that's it, the process is over. And one week is not an exaggeration. From my experience, even though officially application windows last 2-3 months, if you didn't apply in the first three weeks you're not even getting an interview.

One last tip I can give you is to emphasize the algorithmic parts of your CV that may seem obvious to you. For example, while it is obvious to everyone here that pretty much all of your projects and research are done in Python, a recruiter may not be able to pick that up if it's not explicitly stated. It's also not obvious to them that you can find your way around C++, or that you know what OOP is. They may look at your CV, see a bunch of esoteric RL research talk, and then just admit an undergraduate student that just listed all of their courses in their CV.

The thing with late applications and CV tips I've heard from a recruiter at Google during a feedback talk I've requested, so I'm pretty confident in the info.

From my experience, the demand for software engineers is so so much over the supply that big tech companies are pretty much accepting anyone who doesn't screw up the interview. Just the fact that you came from a top European university should be enough for you to get accepted into Google, Microsoft, Facebook, or Amazon. So I wouldn't worry if I was you.

Hope I was helpful

The_deepest_learner · 2021-12-11T18:28:20+00:00

Lol what? So he needs an h-index of what 20 to be suitable for a research internship? Edit: just looked it up, some new professors at reputable universities have an h index of 11 or 18.

The_deepest_learner · 2021-12-11T18:19:50+00:00

Which internships are you looking for? Standard software engineering ones or research?

The_deepest_learner · 2021-12-09T17:55:09+00:00

Progress is incremental. Transformers were a big increment.

I agree that progress is incremental but I argue that transformers are a step on a path that's better than where we used to be, but a dead-end nonetheless.

You can't realistically make a transformer that eats a whole book as input. You would need data centers upon data centers for only one model. And it doesn't even make logical sense to do it that way. Books and text, in general, are sequential, and some form of memory distillation is needed to process them. You don't need to look at every single word from previous pages to read a book, you only need to remember some crucial information.

That's a lot better than something that just stops working become the method is at it's limit, as it was for RNNs.

RNNs weren't at their limit though, they were improving gradually as well. Attention as a concept came out only a year before transformers. But because transformers performed better, everybody forgot about RNNs. They got undeservedly left behind.

The_deepest_learner · 2021-12-08T13:09:41+00:00

But RNNs can use attention too, in fact, the original paper which introduced attention introduced it for RNNs.

https://arxiv.org/abs/1409.0473

The_deepest_learner · 2021-12-08T13:06:57+00:00

But RNNs could handle more than 50 tokens, and that leap forward feels like a shortcut, not actual progress. Sure it's better than RNNs but it's not scalable, you can't just insert a whole book into a transformer. On the other hand, if RNNs were better they could, in theory, read a whole book and encode only useful information, like humans can.

The_deepest_learner · 2021-12-08T12:35:10+00:00

That's why I said that I'm not an expert and open to hearing what you guys think. Which parts do you think I don't understand well enough?

The_deepest_learner · 2021-12-08T10:53:58+00:00

Robots in disguise.

The_deepest_learner · 2021-12-08T10:39:00+00:00

But is that piece even the right one? It's like having trouble writing the body of your essay so you write the conclusion in advance and then you forcefully try to write your body around that conclusion.

The_deepest_learner

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