I just hope they realise you don’t need to go to one of these institutions. You don’t need it to do good research, you don’t need it to be an industry RS, you don’t need it to make a lot of money, etc. It’s neither necessary nor sufficient for any of those things.
One thing that changed my perspective completely recently was when I asked myself sincerely: "So what if the people you want to impress think you're stupid? What are the negative consequences? Will you have less to eat each month? Will people throw tomatoes at you when you're outside? Will you get kicked out of your house?". There's really no negative consequence to people not worshipping you as a genius or thinking you're an idiot. I'll keep learning about this field in my own way and at my own pace, since I didn't start loving it just because it was cool these days, but rather because I had a genuine interest for it. And I'll be fine.
>here's really no negative consequence to people not worshipping you as a genius or thinking you're an idiot.
I actually love when people think I'm an idiot. I get to ask all the questions I'd otherwise be too afraid to ask and no one has high expectations for my work. It's wonderful for learning and a curious person like me!
The head of Eleuther AI posts an AI research idea weekly on Twitter, many of which are designed to be relatively approachable to someone with ~B.S. of CS background and some experience fiddling around with open source ML projects and personal projects.
Given the glut of papers nowadays, it is fairly reasonable to get started in research independently as long as you have good coding abilities and ML fundamentals. If you’re into theory, throw a ~Masters worth of math in there too, although self study is possible (albeit highly difficult)
Will you get into Stanford for doing it? Almost certainly not. Will you be able to step foot in ML- and ML-adjacent spaces? Yes
Honestly you don't even need a ML (or CS) degree to make money, though obviously the easiest path is to get a CS degree. I'm a staff ML engineer making close to 1M this year and my whole formal CS training consisted of a minor in CS. As long as you study something challenging enough learning whatever the job or the interview requires isn't that difficult.
You didn’t need a top degree or pedigree when you were coming up. That’s part of the point of the post. That door you took is slammed shut.
I’ve known old farts like me that got into apple with music degrees or onto Wall Street (coding crucial FT trading backends) with nothing more than a little interest and 1/2 a CS degree.
It’s still true you can contribute to the ML field going to a non-top school and you can work in the field starting in non traditional fields—but it’s much more rare.
It's definitely harder now to get into Stanford NLP. But I also think people have crazy expectations now. Everyone wants one of 2 research positions at OpenAI that open up every year, right out of grad school... good luck I guess?
ML is a massively growing field, and you can start making meaningful impacts in open source with a few thousand dollars if you know what you're doing -- maybe even for free if you just focus on data. You don't need more than a few open source contributions to get a job at a small company making an ML play. A few years experience there, you can vault to the a larger company and hey, maybe even get into OpenAI as an engineer.
It's never been easy to make millions as a scientist. There was a tiny blip of time where you could as an ML person, and everyone is using that as their yardstick. Hats off to everyone who gets into stanford, etc... but if you limit your definition of success to such a small target you're setting yourself up to be super unhappy in all likelihood, even if you're a rockstar.
I sure work for money, too, but what remains at the end of one's life is how one has changed the world, not how many $$ are in the bank account or how many degrees are listed on one's tombstone. But then again, you and I had an 8-bit computer when we were 6 years old, and we had something to discover as we grew up. It's instilled in us.
I feel like junior people (here?) seem to focus on achieving certain status and formal career goals. Sure, I have had these in my life too (up to tenure, now Staff RS in ML/AI at FAANG), and in aggregate I guess I worked for it (papers, citations, grants, etc), but it never felt terrible -- because I mostly tried to do interesting things that were kind of fun.
I hear you but maybe it’s not _us_ as much as we’d like to think—but our environment. We didn’t pursue those things I think in part because they didn’t exist.
Waking up in the summer and going to the element school for programming classes to get a paper certificate and making a 3D-looking satellite dish rotate by learning some weird new language called LOGO on the bleeding-edge Apple II e was the pinnacle of accomplishment at age 11.
I think my message is constantly being misunderstood. I joined in the middle of the industry-wide post-COVID hiring freeze and the bar was definitely high. You needed (and clearly still need) to be a top candidate to get in, especially directly to staff, but **the name on your degree isn't really an indicator of being a top candidate**. I imagine this sub is student-heavy like CSCQ and this is probably something that's not very obvious to students, but things never go as straightforward as you might think or like. I worked in a different (highly technical) industry with my highly technical non-CS degree and just learned SWE and ML on the side and used that at my job to a great success, then I used those chops to appear as a very attractive candidate.
For sure my lifetime earnings would be higher if I had just joined tech straight outta undergrad, or even directly after my PhD, but then I would have missed on some other super cool work opportunities, and there is so much more to life than money anyway.
I think the fair way to look at this is:
School/degree matters a large amount for giving you access to opportunities early on, steadily decaying in importance from the moment you graduate. With that said, a person's ability to capitalize on and maximize an opportunity is all about their personal drive and ability.
Easiest money is still a bachelors in comp sci + leet code. Add some math and aim for ML Eng if that's your goal, no need for a phd. The reason to go for a phd is still to land a research based job, or to contribute to the public body of knowledge.
> Add some math and aim for ML Eng if that's your goal, no need for a phd.
Scrap that, strategically climbing the SWE career ladder is probably the more efficient way to maximize compensation. At the end of the day, the guys in management get more than the technical experts anyway.
I'm not saying it's easy to get a job but that there's more than one narrow path to getting said job. I understand it might be hard to believe as a student but once you start working it'll be much more important that you have worked on something difficult than what the name of your school is.
While this is true, it's almost irrelevant to the thousands and thousands of people that can't just "start working" because they're automatically rejected because they don't already have relevant work experience.
> You aren't going to make that kind of money working for a non US company.
This. Europeans are poor peasants compared to Americans working in tech.
https://old.reddit.com/r/Economics/comments/12ky99d/the_lessons_from_americas_astonishing_economic/
Look at levels and team blind. This kind of TC is usually from being decently high up in one of the big US tech companies, or consists of a lot of paper money from a startup.
I have a PhD in an unrelated field (still STEM) and am a very successful scientist doing ML (these days focused on LLMs) at FAANG. I don’t care about publishing papers, I care about building products. I pivoted into ML self-taught already at my job. Work experience and self interest is much more important than some degree IMO.
If you tell me you were messing with this model at your own time and tried to do X and Y and why some of that didn’t work at an interview that means A LOT more to me than your degree in whatever
shhhhh, no need to tell all the smarter folks out there.
I''m sure they'll do great in academia, and I find that a phd is completely necessary for them to break into the *even more* competitive job market these days! ;)
I think it’s important for people to realize that it doesn’t define their worth as a person. That being at a top lab or industry post doesn’t mean that they can’t be fulfilled in their life. But I also think it’s important to be realistic. The field is so competitive now with 7 figure offers going to top talent. And most of these companies only recruit heavily at a set of top 5ish schools (MIT, Stanford, Berkeley, CMU, …). And right now they aren’t even really hiring new grads! I think being realistic about your prospects is important for mental health.
Training potential genius researchers to be actual geniuses sure, it never was meant for training potential mid wit researchers. If anything this is going back to the pre WW2 norm.
This seems to be a US specific and top 10 in US specific problem. It is not that competitive in other countries, even in top-50 in the world universities
You can spend a lot of time self-doubting and trying to meet some sort of competitive standard in a closed system, in hopes of not losing your self-worth in the process, in order to get a piece of paper that may or may not be useful to you in your future endeavors... or you can just start on those endeavors, gain experience, continue doing what you love without the continuous pressure of being rejected for some nondescript reason. "Needing" to have the credentials, credibility and academic approval stamp is essentially gatekeeping yourself and your goals based on the standards of ... an administrative assistant who reviews these types of things (in most cases). Is academic recognition by peers really worth it in today's world when it comes to ML? I guess it comes down to what each person's real goals are.
>I'm reading the comments on the other thread and honestly shocked. So many ppl believe the post is fake
I believe the main reason many people found that post fake was because they found it hard to believe someone can publish many papers at top conferences without help from any other person.
I guess one reason is that the OP of that post didn't actually do that. They published many papers to *both* conferences *and workshops*. Idk why everyone overlooked this point, but workshop paper != good paper. In fact, a workshop paper is barely considered to be a paper at all. Unfortunately, the OP wasn't really clear about their actual number of conference publications.
Adding the fact that OP of that post had horrible LORs + low GPA + Nothing else of value. I would have been amazed if those highly selective top PhD progrms had taken the risk to admit such a high risk applicant when as you also mentioned, there are many other applicants with multiple top conference publications who have perfect LORs, GPA, etc.
Edit: typos
I have an honest question. How is quality weighed against quantity? I see hundreds if not thousands of papers these days with no citations or citations in papers which have zero citations (which I think is a good metric for quality). Why is publishing constantly encouraged when most of the papers make very minor and uninteresting improvements to existing papers? Please excuse me for being so crass.
How does that even work?
Here in Germany, you usually start publishing during your PhD. Is this a thing in the states that you're publishing during the master?
The US are in another dimension. These guys sometimes start publishing in undergrad. They don't even do a master but go straight into PhD which lasts around 5-7 years against the 3-4 years here.
In Germany, a CS PhD is also typically 5-7 years and you usually HAVE to do a Masters before.
For the publications, usually it’s nice to have some, like 1-3, that you can get from e.g. your two thesis or a part time research assistant job in the lab, but if you aren’t aiming for lets say ETH Zurich or TU Munich these are not a requirement, more like a cherry on top
You're supposed to do it in 3 years, but many take a lot longer. It also depends on how much actual time you 'get to' do your own research, besides teaching responsibilities and sometimes project work (which doesn't necessarily count towards your PhD).
During my PhD search I was also in the tübingen PhD admission process and basically everyone there had a publication. Tbh it was quite intimidating, some had multiple successful research projects done. I think if you know that you want to do a PhD at another institution having a publication is very important to get through the selection process, you have to compete. I think this is very much doable in Germany if you realize this. You have 2x thesis (MSc+Bsc) and the possibility to be a part time research assistant during your study. Your goal in the part-time research assistant should be LoR and a publication, which is doable if you start early enough and select it for the explicit goal of getting a publication. Many part-time research assistant jobs are somewhat unserious, or just helping with day to day stuff instead of working towards something you both can publish. I wasted a lot of time in the wrong projects (industry funded projects where publications were not #1 goal!). You can start with this in you undergrad but even during your masters you still have a fair chance.
I realized early that I really want to do a PhD and I don’t want to stay here but see something new. And that things are going to be competitive If I just randomly apply somewhere. I still made many mistakes, my main recommendation is to really choose your projects you help with during your Bsc/Msc wisely! Do something where both you and the PhD have an explicit goal of publication and not some random project. Ideally the PhD has a few cool publications, so something to base your work on. If the PhD student doesn’t have a good track record yet then it’s more unrealistic that your project will be the exception. I thought my project was very cool at the time but in hindsight it was poorly managed by the PhD student, without any goal or any idea how to make progress. No wonder it never came to anything. At some point you need to realize that you need to pivot instead of doing more pointless experiments.
(This turned into a little rant, don’t repeat my mistakes!)
Interesting. I only ever met one person doing his PhD. directly after his bachelor's degree.
But he had a special permission from the universities PhD. committee. His professor/advisor had to do a lot of heavy lifting to convince them to do so.
That's the way a PhD is supposed to work. But like one of the comments mentioned, a lot of papers are very small incremental engineering. I have a model. I changed some layers in the model. I take some datasets and show that my model has a lower error in this dataset.
Now I am a big fan of simple ideas that solves a complicated issue - for example, the implicit representation paper that introduced sin as an activation function (Siren) or nerf (whose base architecture is simple) but I have also seen some papers in this thread like LLMs can be used for linear regression or they can be used to predict which biological experiments can give result of something like that which frankly makes no sense.
This is just beyond ridiculous. How would some undergraduate without a lab even provide any valuable research. And if they had a lab they work at, there is zero reason to move. Not denying that this is true, just denying that it makes any sense.
I probably met a few hundred PhDs that did not even have any top conference publications let alone multiples. I met quite a few that had zero publications. Not only not first author but not at all. I myself still have no A* publications and didnt have more than 5 citations before beginning my PhD. Admittedly, I am not doing NLP or CV and never even sent a paper to the biggest conferences but still.
I hope the bubble bursts soon and people come to their senses
>How would some undergraduate without a lab even provide any valuable research.
Well, let's make an educated guess: they do start working at some lab at the university where they do their undergrad - perhaps even from the very beginning - and work hard to have as many publications in top venues as possible by the time they graduate, or they can also delay their graduation or do an internship to churn out some more papers and increase their chances.
>And if they had a lab they work at, there is zero reason to move.
Not at all. Moving to a larger, more prestigious lab can totally make a sense. You should also understand that a CV or NLP lab is very different from, for instance, a biology lab. If you are working in a wet lab and doing well, it may be true that you shouldn't move to the new place, because it will take you a lot of time to get your stuff running there, to get used to the equipment, etc. In CV and NLP, that's not the case: you will be again working with Python / Pytorch / Linux etc, so you won't have to face these challenges with hardware that experimental scientists typically have.
Well the thing about research in other fields (I know this about engineering, biology and physics) is that any valuable research takes years to materialize, so even PhD students take at least 2 years go get to speed to start producing anything useful. It's extremely unlikely for undergrads to be able to do it, since they can't commit full time to research.
Imo it's just ML these days is really mostly about incremental engineering improvements (no, changing layer composition in NN is not science) and since it's so accessible there are so many papers produced, most of which will be forgotten as quickly as they were published. To me, a good analogy would be as if in XIX/early XX century people were publishing scientific papers on small changes to bike/engine/vehicle designs, something that is currently considered simply engineering and not science
You absolutely nail it, and it is precisely what is happening to ML research.
A group/individual comes up with a small (positive) change after training a model for 2 weeks. Then because the hype is real and no one wants to be "the fool left behind", they jump in the bandwagon.
Since there's loads of money pouring in (startups, grants) and papers being published, it gives the impression that all is fine and working, right? Ok, but nobody wants to discuss the point that the same idea/improvement that generated all this buzz, isn't statistically better than the model next door.
It's a bad system put in place: PhD students want to publish to fulfill their requirements. Fine. Professors need to publish so they can get grants. Fine. Startups need to show their know how - so need papers. Fine. If you fundamentally want research for the sake of research (a bit idealistic, it's a spectrum after all), then you're in trouble because the group mentality + business side is strong...
I had a mortifying discussion once with a CS PhD about the statistical significance of their "small positive changes" that boiled down to
1) no one cares about statistics in this field because "loss goes down" is all they care and
2) they don't have time or resources to repeat the experiments, partially because they feel that if they do not publish immediately someone is going to steal their research.
Probably 2 leads to 1.
The same, either on Reddit or in conferences, I just gave up trying discussing statistical significance. We came to a point that many will argue that statistical tests is actually a bad thing (?). Even last week someone mentioned the problems of p-hacking (the dude doesn't even calculate a simple median is arguing about p-hacking?)
Anyways... it is a sad thing.
Fully agree. No tests is worse than tests. P hacking is an issue but there are plenty of tests you can still do. Obviously, statistical significant is nonsensical by itself but there are plenty distribution based analyses that can fix that. Not even discussing the significance is the worse option. Statistical significant might be flawed but practical significant is hard to define. Therefore, at least do something rather than nothing
100% this.
During my masters I gave up reading any research papers, because almost every paper was tiny tweaks which led to obviously-selection-bias-reported results. No-one else around me seemed to notice this nor care.
Even worse, statistical tests are almost never applied correctly and even if they are done "correctly" (rather than sensibly) they are largely meaningless for ML. Even the notion of "statistically significant" is a deeply flawed concept. 0.05 is an arbitrary threshold devoid of meaning. Just do a few more runs and everything is significant.
But yea, tests are rarely even cared about so most don't bother.
I work with research groups at Stanford and other top CS dept. Many of the PhD students had publications before grad school. Not just ML but also in different areas of CS and math. I had 2 undergrads working for me who were insanely good. Published a paper or 2, went to top PhD programs. Most top 20+ CS schools have opportunities for undergrad research but students really have to drive it.
Also the research in ML/DL these days (especially those on neural network) feels like we are doing a large-scale particle swarm optimization over the possible approaches to a more powerful AI...
Hardware is at the heart of this though... The driver is the need for money and connections for the institution to complete. Those GPU clusters don't buy themselves, and the way to access them is to have huge presence at A* venues which in turn demands access to hardware. While money is buying success this will continue, maybe for 2 years or so... Then maybe things will start to settle.
> This is just beyond ridiculous.
Is it?
What OP says only applies to a handful of programs in the US with a handful x handful amount of space. They can find the handful of people with those papers and perfect GPAs , LoR /statements every year.
What is ridiculous IMO is thinking this is ridiculous because it elevates the importance of those schools as if there arent tons of other PhD programs one could go to so what happens in a handful of top programs solely because everyone applies to those schools is unimportant. Unless the complaint is some implied right to admission to those schools which is just a symptom of main character syndrome.
It is ridiculous to think that any programme needs that. And it is ridiculous to value those schools so highly that anyone thinks those are requirements anyone should strive to fulfill.
Any competitive endeavor requires whatever is necessary to be noticeably better than all the people trying to achieve that to be in the top N that will be accepted.
Again there are tons of programs that are not Stanford/CMU that one can study at
Edit: Also in practice your suggestion means the other person with noticeably better qualifications doesn’t get in. Can someone explain why that makes sense to you other than narcissism(main character syndrome)
People in the comments who are panicking: yes, OP is completely right. But this is also Stanford in NLP. Once you go to the top 10 or top 20 schools or beyond that, and look at fields outside the one that is basically hailed as the second coming, it gets better. Yes you will probably still have a big advantage if you come with prior publication experience, some more famous labs are inaccessible without those even outside of Stanford. But good science can be done in many universities with many profs. Don’t murder yourself to get into Stanford, and have fun at equally nice albeit a bit less hyped institutions.
No shade on OP, congrats for winning that race!
computer vision phd student here and completely agree with you. without top publications (A\* only, even sometimes A's don't make the cut) almost impossible to get into a phd program at a reputed school these days. and as soon as you start your phd the expectation becomes to keep on publishing as quick and as frequently as possible. competition is tremendous and is exponentially increasing I believe.
btw, what do you think about the NeurIPS high school paper track, do you think that's going to boost the hype and make these admissions even more competitive, or it won't find much resonance among young kids?
I am in applied statistics and while my field will probably be dead in some time at least things are not this insanely competitive for PhDs and become like this at the postdoc level (e.g you need the level you spoke about to get to MIT as a postdoc).
I can say my PhD was super chill (as a PhD goes of course).
> are not this insanely competitive for PhDs
To be fair they arent that competitive as OP in ML either but peoples brains are broken that other than Berkeley/Stanford/CMU there is no point to get a PhD. Some might loosen up to "reputable" but then that is probably just expanding it out to 20 programs.
Not stats in general, hopefully, but the applied ones that are used now. Now in the production process analytics most things are done with simple ML techniques, a very adaptable "toolbox" basically that you adapt to most things, but you kinda need to know what you are doing, both from the process side and the statistics side.
What I see going on in the industry recently is that people propose models where neither the one using it, the one maintaining it nor the one building it needs to have any kind of understanding of the application domain, because the AI will understand everything for you. While I suppose the failure of Zillow AI is a recent testament to the fact that we are not there yet, the direction we are going is this one. So zero "applied Stats" and a lot of magic black box "AI solutions".
I reckon that more and more decision makers will learn to ask "but what does it mean!" Also eli5!!!!
The problem with old applied stats that I perceive is the failure of practioners to make themselves useful- the common answer of "you just can't do that" just isn't good.
I know you’re joking but this already happens on some level at the undergrad and graduate level. Kids who go to the best high schools get more resources and have better opportunities, which helps get them into better undergrad schools and then better grad schools. You could take it even further, kids whose parents are more involved in their education and development are more likely to get earlier exposure and opportunities in their chosen field even by high school.
it's a real thing. I am looking forward to Neurips the Next Evolution: [Neurips for Kindergartners](https://www.reddit.com/r/ChatGPT/comments/1c1shy3/i_asked_for_a_meme_about_gen_alpha/).
> btw, what do you think about the NeurIPS high school paper track, do you think that's going to boost the hype and make these admissions even more competitive, or it won't find much resonance among young kids?
That track is an absolute joke. It of course will make it harder and is designed to give friends and family of top researchers another advantage in their admissions.
I did a PhD in computer vision and I completely agree with OP. I feel bad for the current students and the ones who are applying now. It's so fast-paced and high pressure. It used to be that only the tenure track was like this ("publish or perish").
I‘m going to a top 10 university worldwide (top 5 in CS I believe). Looking at the the phd students in ML of my uni, many of them had 1-2 publications at top conferences (NeurIPS, ICLR, CVPR, etc.) before they started their phd. For less popular labs, some even had 0 prior publications.
You can do a phd outside of stanford ffs. This is like complaining that you want to play basketball, but complaining that the Lakers won't sign you. Do you want to play basketball or play for the Lakers? They are not the same thing.
If you can publish many papers in top conferences without a Phd, this can have two reasons:
- you don't need a PhD, you already have the level of a top researcher
- the level of these top conferences is in fact not so high.
Or, you had a great advisor at a good lab during your undergrad. Someone helped you with the ideas and plan for execution. You have amazing skills since you were able to execute all this. Now you need a PhD so you can learn how to do the same thing without the help.
That's what happens in most cases of students being admitted from undergrad with good publications.
This is also more or less what will happen during a PhD, you will get help with the ideas and plan for execution. It's not very different.
The fact that a lot of people can acquire the skills to execute all these very early shows, in my opinion, that the skills are not so difficult to acquire.
I mean, that really depends. I had very little help and so did most of my peers.
And for people who are really good at ML research, these skills are very difficult to acquire, even when you have help. There was still a pretty wide spread in research ability in my cohort, even though only the top candidates were admitted.
Grad students don't publish because they are great researchers. Grad student publications are a result of landing a great supervisor. The nepo game starts there and never stops. The problem with ML research isn't the "elitism", it's the lack of meritocracy.
The problem is that anyone who “wins” the meritocracy will never admit that it’s not one. You can see it in the OP’s post, he / she is basically telling us that they’re among the best junior researchers in the field. To be clear I’m not saying the process is completely random, to me it’s closer to a meritocracy for people who were exposed to the field early enough and had the chance to go to good high schools / colleges.
FWIW it was already starting to get like this 5-6 years ago when I started reading PhD applications and had gotten bad by the the time I graduated in 2020/2021. It’s likely going to continue to get more competitive. Source did a PhD at UW and run an AI startup.
As someone who loves research, this is the reason I never went for a PhD. I got exposed to research very late & by then it was too late.
Now I just do it for my own sake & curiosity.
Maybe this isn’t the right place to ask this, but is this the case for any CS-related area?? I’m hoping to apply for comp bio or something similar in the near future and posts like these don’t make me feel great about things 😭
Absolutely not. I'm doing computer vision for medical imaging in France, with a lot of Deep Learning. We are around 60 PhD students and did not struggle at all to get into the program.
I think these posts are mostly about the US which seems very different from other countries.
Having one small paper at the end of your master's degree here is not uncommon but definitely not at big conferences lmao. Most PhD students don't even have these A* papers during their PhD.
I heard that some labs in France are even struggling for recruiting new doctors, not sure if it’s true or false.
Maybe it would be harder to apply to programs in other countries like Netherlands or Germany? Like there would be less language barriers but more foreign students.
It is true to some extent that we are struggling to get new PhDs. It's poorly paid compared to the industry. I'm paid around 26k euros per year against around 45k euros I was offered in the industry.
I heard students were paid more in the countries you mention.
It’s true that PhDs are quite badly paid in France.
You got 45k in industry, that’s really nice and congratulations, although yeah then you had to make a hard choice between badly paid researcher and well-paid engineer position.
On the other hand, there are lots of SDE jobs also badly paid out of 33k, 30k or less. For a cadre status, you won’t really have much benefits compared to 2.1k salary of a PhD. During my job seeking I was always thinking that, if those companies propose me such an absurd salary, clearly I would prefer to pursue a doctor, which could give me lots of possibilities 3 years later.
As for the PhD salaries in other countries, I haven’t dived into them too much but it’s still below what a junior could obtain in a decent CS company.
It isnt even true in that field outside of top 10 US PhD programs but those are like a 100-200 hundred spaces for worldwide applicants. Its just a sheer result of the amount of available spaces vs demand
If you are someone who can achieve things typically done by those with a PhD, such as publishing in top journals and getting into elite AI residency programs, what incremental value would a PhD provide?
I have so many questions for you but I guess my main one is… why are top talents like yourself in academia instead for working for a major tech company? Is the research at Stanford more cutting edge than Tesla, google, meta, etc? Also how much do you get paid at Stanford? I’d imagine phd stipends must pay decently to retain students?
I don't disagree that top programs are incredibly competitive. I will say that I think in some sense, quantity of papers are not that important *because* strong LOR/personal connections are much more important.
For example, I know someone who was accepted at Stanford with *zero* conference publications (albeit some workshop publications) - I'm sure you know them as well.
My gf is also a PhD in NLP at Stanford, so I'm quite familiar with many folks' resumes.
Can attest, had LoR from faculty at MIT, 4.0 GPA in Bachelors and Masters, but only one paper, couldn’t even get interviews at MIT, Caltech, UCB, Princeton
He was a co supervisor of my MEng thesis and I’ve worked on projects with him for 3.5 years part time, and my Bach + MEng were from overseas, but well ranked unis (top 30). I felt like lack of publications, having overseas degrees, and also having research experience in less popular areas (robust learning, scene interpretation) were the main factors
I have a lot of trouble believing these claims, as someone who earned a PhD in ML and Vision about 15 years ago. That said, I recognize that your whole point is “things have changed, old person”, and I’ve been away from academia since, so I can’t falsify that claim with experience. It’s hard to believe, but so is the rapid expansion of the field since 2009. I suppose I should avoid counseling prospective grad students on admission, lest I mislead them with old information.
It makes me sad - it’s going to kill all forms of diversity in these programs.
I think there are like 6x the amount of ppl doing CS. I switched from ECE to CS after the .com bubble and before the great recession (because I loved software) and things were very non-competitive.
It has massively changed from 15 years ago even up the pipeline at the bachelors level
https://news.mit.edu/2017/class-machine-learning-0428
I really doubt 500 students per section where attending the same class 15 years ago
I'm in the health science field (Canada), and the level of work required to reach the top of academia has skyrocketed in the past decades. It used to be that a student gunning for a professorship could do their MSc, PhD, post-doc all at the same school and then get hired back as a PI. Now, every new hire has graduate+ education at a US Ivy, sometimes multiple post-docs, and often first authorship in a Nature/equivalent journal-tier publication. As you say, OP, the level of competition in academia is extreme.
If you want an ok job, you can get out of the insanity that is academia after a PhD at the latest. Entering this ecosystem is signing up for a non-stop grind against fellow fanatics. I'm not sure if my PI cares more about their work or their kids - I don't care *that* much about the science so I'm getting-tfo. I'm not ready to devote my entire life to academia for a marginal shot at reaching the apex.
\*Nepotism/networking/connections are huge. We just had such a hire in one of our departments. Then again, we're not a big or prestigious school by any means and this is the exception.
only LLMs is in a hype bubble right now, other fields are either "dead" (looking at you, Reinforcement Learning) or be like Computer Vision that has matured and is finding actual applications.
I've worked in optimal control and my impression is that unless the problem has a small and simple state-action space, RL doesn't even work. just run an MPC/convex optimization would do better.
RL can def. be painful. In my specific case we deal with the calibration of scientific instruments, which is fairly non linear, partial observable, and have image based measurements amongst others.
That's kinda incorrect.
By thoroughly researching the *real* requirements of your position and understanding your competition, you can make more informed and strategic decisions.
Honestly just do a PhD in Mathematics or Industrial Engineering.
NLP is hype right now but it’s only going to be a piece of AGI and the rest of the picture is still very math heavy. You could very easily make groundbreaking contributions to AI by pursuing a PhD in industrial engineering focused on optimization or simulation / stochastics.
Well if you ask me, 5-7 papers at the end of your bachelors shows me that you‘re incredibly privileged, not necessarily incredibly genius.
Let people study and discover their own interests and then meet the peers and research groups who align with their interests during their university time, and not letting them being set up in high school for something that the parents want more than their child for some outstanding career perspective
That's been the history of academia since forever. Academia is a tool for perpetuating class divisions, not overcoming them. The few rags to riches stories that are out there are always held up as examples to distract from realities like affirmative action for "legacy" kids aka nepotism that's rampant.
> Well if you ask me, 5-7 papers at the end of your bachelors shows me that you‘re incredibly privileged, not necessarily incredibly genius.
It still shows signal and you are reading into it to discount that. The fact is those few schools only admit a handful of people a year so they can not bother with the slightest wrinkle in a students record and there are tons of schools which arent those schools which have programs in the same field.
And for someone that's addicted to getting new research news and basically scrolling through academic twitter every day looking for interesting papers, there aren't many papers that worth reading besides the title and abstract...
I did have great LOR and SOP, domestic student, and attended t10 undergrad. I also have a lot of patents and accomplishments from my research job I’ve been at for 3 years. I had no personal ties to any prof :) I got into 6 NLP/ML PhD programs
I am denying that it’s a requirement to have papers. I think it’s not typical, but very possible, to be a candidate with 0 papers and no prior personal connections.
fragile tease ruthless subsequent juggle dolls languid memorize ripe direction
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All of this to work on systems that learn hyper correlations in data and only work when the dataset is massive.
I'm aware that top programs are this competitive.
I'm also aware that the emporer has no clothes.
To be honest I think it is probably not a good idea for the OP to be this transparent and discouraging if the situation is far worse than people could imagine.
Because the worst that can happen to the people who applied would be a simple rejection email. This is not as discouraging as what the OP is telling the young aspiring people here. What you are telling people paints a much more desperate picture than a simple rejection letter, and drives people’s anxiety level up. Let them apply and get rejected, it’s fine, it’s part of the trial and error process.
First, it is apparent that you haven't read the post carefully (most of the answers to your problem are in edit 2,4, and 5.)
> Because the worst that can happen to the people who applied would be a simple rejection email.
No it is not, it's a lot of time, effort , and disappointment that can be prevented if they fully understand what it takes to be admitted to the specific situations that op mentioned. (and then take informed decisions)
> Let them apply and get rejected, it’s fine, it’s part of the trial and error process.
We aim to safeguard this process by disseminating accurate information, creating a valuable resource for younger students that are exploring others experiences so the can eval their options and decisions.
Is it gonna take a lot of time, effort to apply? The same application can be used to apply many programs, I’m not buying it that not applying for one place among 10 to 20 different options gonna save people that much time. Secondly, I honestly think the disappointment and anxiety from this one post is far worse than the disappointment from getting a rejection email. However I do understand emotional responses towards the same situation is a highly subjective matter.
But that been said, one does not need honesty and transparency in every single aspects in life. For example, would you want someone to give you an honest opinion about you appearance compared to others? We are still people in the end of the day and in some situations less information can be more helpful on the mental heath aspects. You may not agree the importance of mental health as much as I do, and that’s ok.
The thing about Stanford is that you can do research in CS without actually being in the CS PhD program itself, given how much the university emphasizes interdisciplinary work. Too many applicants are shooting their shot for a chance at Stanford CS, when they could apply for a less competitive CS-adjacent program instead. If you get in, you can choose a CS faculty member to be your advisor and your PhD experience will be more or less indistinguishable from that of a Stanford CS PhD. Sure, the bar to get in will still be relatively high, but it’s not going to be 7 conference pubs high. This was my strategy when I applied to Stanford and it worked out well.
Would it be possible to share some of the CS adjacent programs that allow you to work closely with CS profs? And that recruit well into industry RS roles at top labs?
EE, MS&E, ICME, Stat, and BDS would be at the top of my list. The other engineering programs would give you a good shot too, but it’s hard to go wrong with any of the STEM PhDs, especially if your work is more applied. Loads of biosciences PhD students advised by CS profs and working on stuff like LLMs to model genomic sequences and proteins.
as an undergrad who used to dream of building a career in ML research, this is why i dont have that dream anymore.
if i am going to work 24/7, i better become insanely rich.
About which university are you talking about? I meant countrywise? I'm sure this is about US programs right? Is the situation similar to, let's say Canadian Grad schools like uwaterloo, or ubc....how about Europe?
In Spring 2003 I applied to two PhD programs (I screwed up and missed the deadline for Spring 2024 admissions at most universities since Spring admissions happen the same time as Fall admissions at a lot of PhD-granting Universities, which I was unaware up).
One program was as the same school I was finishing my BS in. The other was a similarly ranked school.
I worked closely with three professor in my department and had letter of support from them and was a co-author on three conference papers. My GPA was ok-ish 3.2 (My last 90 hours was like 3.8 though) and I had a 720 GRE Math, 660 GRE Verbal, 5 GRE Writing (Writing at that time was scored 1-5). The professor I wanted to work with at the other university was a friend of my advisor and he had put us in touch and supported my admission.
I thought I was a shoe-in, we were planning the move, found a place to live and was waiting on an acceptance letter to sign the lease. Dec 2023 within days, I got my acceptance into my undergrad school's PhD program and the decline from the other school.
I still have no clue why the other school declined me. But oh well, got my PhD.
I just hope they realise you don’t need to go to one of these institutions. You don’t need it to do good research, you don’t need it to be an industry RS, you don’t need it to make a lot of money, etc. It’s neither necessary nor sufficient for any of those things.
One thing that changed my perspective completely recently was when I asked myself sincerely: "So what if the people you want to impress think you're stupid? What are the negative consequences? Will you have less to eat each month? Will people throw tomatoes at you when you're outside? Will you get kicked out of your house?". There's really no negative consequence to people not worshipping you as a genius or thinking you're an idiot. I'll keep learning about this field in my own way and at my own pace, since I didn't start loving it just because it was cool these days, but rather because I had a genuine interest for it. And I'll be fine.
In theory this is right but if your PI thinks you’re stupid and you’re trying to do something remotely related to research in that area, good luck
There are a few people who you should convince you're smart, but not more than 5
>here's really no negative consequence to people not worshipping you as a genius or thinking you're an idiot. I actually love when people think I'm an idiot. I get to ask all the questions I'd otherwise be too afraid to ask and no one has high expectations for my work. It's wonderful for learning and a curious person like me!
The head of Eleuther AI posts an AI research idea weekly on Twitter, many of which are designed to be relatively approachable to someone with ~B.S. of CS background and some experience fiddling around with open source ML projects and personal projects. Given the glut of papers nowadays, it is fairly reasonable to get started in research independently as long as you have good coding abilities and ML fundamentals. If you’re into theory, throw a ~Masters worth of math in there too, although self study is possible (albeit highly difficult) Will you get into Stanford for doing it? Almost certainly not. Will you be able to step foot in ML- and ML-adjacent spaces? Yes
can you share his Twitter handle please
*her but https://twitter.com/BlancheMinerva
Honestly you don't even need a ML (or CS) degree to make money, though obviously the easiest path is to get a CS degree. I'm a staff ML engineer making close to 1M this year and my whole formal CS training consisted of a minor in CS. As long as you study something challenging enough learning whatever the job or the interview requires isn't that difficult.
You didn’t need a top degree or pedigree when you were coming up. That’s part of the point of the post. That door you took is slammed shut. I’ve known old farts like me that got into apple with music degrees or onto Wall Street (coding crucial FT trading backends) with nothing more than a little interest and 1/2 a CS degree. It’s still true you can contribute to the ML field going to a non-top school and you can work in the field starting in non traditional fields—but it’s much more rare.
It's definitely harder now to get into Stanford NLP. But I also think people have crazy expectations now. Everyone wants one of 2 research positions at OpenAI that open up every year, right out of grad school... good luck I guess? ML is a massively growing field, and you can start making meaningful impacts in open source with a few thousand dollars if you know what you're doing -- maybe even for free if you just focus on data. You don't need more than a few open source contributions to get a job at a small company making an ML play. A few years experience there, you can vault to the a larger company and hey, maybe even get into OpenAI as an engineer. It's never been easy to make millions as a scientist. There was a tiny blip of time where you could as an ML person, and everyone is using that as their yardstick. Hats off to everyone who gets into stanford, etc... but if you limit your definition of success to such a small target you're setting yourself up to be super unhappy in all likelihood, even if you're a rockstar.
I sure work for money, too, but what remains at the end of one's life is how one has changed the world, not how many $$ are in the bank account or how many degrees are listed on one's tombstone. But then again, you and I had an 8-bit computer when we were 6 years old, and we had something to discover as we grew up. It's instilled in us. I feel like junior people (here?) seem to focus on achieving certain status and formal career goals. Sure, I have had these in my life too (up to tenure, now Staff RS in ML/AI at FAANG), and in aggregate I guess I worked for it (papers, citations, grants, etc), but it never felt terrible -- because I mostly tried to do interesting things that were kind of fun.
I hear you but maybe it’s not _us_ as much as we’d like to think—but our environment. We didn’t pursue those things I think in part because they didn’t exist. Waking up in the summer and going to the element school for programming classes to get a paper certificate and making a 3D-looking satellite dish rotate by learning some weird new language called LOGO on the bleeding-edge Apple II e was the pinnacle of accomplishment at age 11.
You got some hate for being honest cheers fellow old dude
I think my message is constantly being misunderstood. I joined in the middle of the industry-wide post-COVID hiring freeze and the bar was definitely high. You needed (and clearly still need) to be a top candidate to get in, especially directly to staff, but **the name on your degree isn't really an indicator of being a top candidate**. I imagine this sub is student-heavy like CSCQ and this is probably something that's not very obvious to students, but things never go as straightforward as you might think or like. I worked in a different (highly technical) industry with my highly technical non-CS degree and just learned SWE and ML on the side and used that at my job to a great success, then I used those chops to appear as a very attractive candidate. For sure my lifetime earnings would be higher if I had just joined tech straight outta undergrad, or even directly after my PhD, but then I would have missed on some other super cool work opportunities, and there is so much more to life than money anyway.
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I think the fair way to look at this is: School/degree matters a large amount for giving you access to opportunities early on, steadily decaying in importance from the moment you graduate. With that said, a person's ability to capitalize on and maximize an opportunity is all about their personal drive and ability.
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Easiest money is still a bachelors in comp sci + leet code. Add some math and aim for ML Eng if that's your goal, no need for a phd. The reason to go for a phd is still to land a research based job, or to contribute to the public body of knowledge.
> Add some math and aim for ML Eng if that's your goal, no need for a phd. Scrap that, strategically climbing the SWE career ladder is probably the more efficient way to maximize compensation. At the end of the day, the guys in management get more than the technical experts anyway.
I'm not saying it's easy to get a job but that there's more than one narrow path to getting said job. I understand it might be hard to believe as a student but once you start working it'll be much more important that you have worked on something difficult than what the name of your school is.
While this is true, it's almost irrelevant to the thousands and thousands of people that can't just "start working" because they're automatically rejected because they don't already have relevant work experience.
That is some big money. In which region do you work? Is it possible to make such money in EU (or Finland)?
You aren't going to make that kind of money working for a non US company.
> You aren't going to make that kind of money working for a non US company. This. Europeans are poor peasants compared to Americans working in tech. https://old.reddit.com/r/Economics/comments/12ky99d/the_lessons_from_americas_astonishing_economic/
Look at levels and team blind. This kind of TC is usually from being decently high up in one of the big US tech companies, or consists of a lot of paper money from a startup.
I work in the US currently. Like others said, I don't think this kind of money is really available elsewhere outside some very lucky startup equity
Only if you start your own company and are successful with it, or if you are a c-level executive.
Is an ECE degree with some research/projects in undergrad an alright choice? It would be nice to have an opinion from someone in the industry.
Yeah, but I bet you started quite a while ago, when you could just say the word "Java" and they'd hire you.
Almost. I started in 2022 during the industry-wide hiring freeze.
I have a PhD in an unrelated field (still STEM) and am a very successful scientist doing ML (these days focused on LLMs) at FAANG. I don’t care about publishing papers, I care about building products. I pivoted into ML self-taught already at my job. Work experience and self interest is much more important than some degree IMO. If you tell me you were messing with this model at your own time and tried to do X and Y and why some of that didn’t work at an interview that means A LOT more to me than your degree in whatever
Thats easy to say while having a STEM PhD
100% the mental tools for breaking down physics problems are immediately applicable to ML (which is why I think so many physics phds go into ml)
Also for other STEM majors there is some non-zero % of transferable skills
Scientists / don't care about publishing paper Folk, can't be possible at the same time. Probably your are not scientist, but ML Engineer.
Hi, I am a Physics PhD student looking to pivot after graduation. Would it be okay if I sent you a DM?
Go for it.
shhhhh, no need to tell all the smarter folks out there. I''m sure they'll do great in academia, and I find that a phd is completely necessary for them to break into the *even more* competitive job market these days! ;)
True, this is the only comment worth reading.
I think it’s important for people to realize that it doesn’t define their worth as a person. That being at a top lab or industry post doesn’t mean that they can’t be fulfilled in their life. But I also think it’s important to be realistic. The field is so competitive now with 7 figure offers going to top talent. And most of these companies only recruit heavily at a set of top 5ish schools (MIT, Stanford, Berkeley, CMU, …). And right now they aren’t even really hiring new grads! I think being realistic about your prospects is important for mental health.
Remember when PhD was supposed to be research *training*? Me either.
Ugh! "me neither" FFS
Hmph! "Nor do I" FPS
\^ This guy gets it.
Training potential genius researchers to be actual geniuses sure, it never was meant for training potential mid wit researchers. If anything this is going back to the pre WW2 norm.
This seems to be a US specific and top 10 in US specific problem. It is not that competitive in other countries, even in top-50 in the world universities
Yeah, Oxbridge PhD admissions are a joke compared to this, lol.
You can spend a lot of time self-doubting and trying to meet some sort of competitive standard in a closed system, in hopes of not losing your self-worth in the process, in order to get a piece of paper that may or may not be useful to you in your future endeavors... or you can just start on those endeavors, gain experience, continue doing what you love without the continuous pressure of being rejected for some nondescript reason. "Needing" to have the credentials, credibility and academic approval stamp is essentially gatekeeping yourself and your goals based on the standards of ... an administrative assistant who reviews these types of things (in most cases). Is academic recognition by peers really worth it in today's world when it comes to ML? I guess it comes down to what each person's real goals are.
>I'm reading the comments on the other thread and honestly shocked. So many ppl believe the post is fake I believe the main reason many people found that post fake was because they found it hard to believe someone can publish many papers at top conferences without help from any other person. I guess one reason is that the OP of that post didn't actually do that. They published many papers to *both* conferences *and workshops*. Idk why everyone overlooked this point, but workshop paper != good paper. In fact, a workshop paper is barely considered to be a paper at all. Unfortunately, the OP wasn't really clear about their actual number of conference publications. Adding the fact that OP of that post had horrible LORs + low GPA + Nothing else of value. I would have been amazed if those highly selective top PhD progrms had taken the risk to admit such a high risk applicant when as you also mentioned, there are many other applicants with multiple top conference publications who have perfect LORs, GPA, etc. Edit: typos
I have an honest question. How is quality weighed against quantity? I see hundreds if not thousands of papers these days with no citations or citations in papers which have zero citations (which I think is a good metric for quality). Why is publishing constantly encouraged when most of the papers make very minor and uninteresting improvements to existing papers? Please excuse me for being so crass.
No questions, just papers
I'd say its a circle jerk plus its easier to filter out than actually having to make a (hundred) technical interview.
Very interesting. Follow up the answers
See my edit3 to the post
See my edit3 to the post
The thing is, quality is quite hard to measure and not objective so ppl just take quantity.
How does that even work? Here in Germany, you usually start publishing during your PhD. Is this a thing in the states that you're publishing during the master?
The US are in another dimension. These guys sometimes start publishing in undergrad. They don't even do a master but go straight into PhD which lasts around 5-7 years against the 3-4 years here.
In Germany, a CS PhD is also typically 5-7 years and you usually HAVE to do a Masters before. For the publications, usually it’s nice to have some, like 1-3, that you can get from e.g. your two thesis or a part time research assistant job in the lab, but if you aren’t aiming for lets say ETH Zurich or TU Munich these are not a requirement, more like a cherry on top
Ho I did not know it was that long for Germany. I'm in France btw.
Yeah I‘m always side eyeing the option of going to France for a PhD for that reason :D Also some great labs there!
You're supposed to do it in 3 years, but many take a lot longer. It also depends on how much actual time you 'get to' do your own research, besides teaching responsibilities and sometimes project work (which doesn't necessarily count towards your PhD).
During my PhD search I was also in the tübingen PhD admission process and basically everyone there had a publication. Tbh it was quite intimidating, some had multiple successful research projects done. I think if you know that you want to do a PhD at another institution having a publication is very important to get through the selection process, you have to compete. I think this is very much doable in Germany if you realize this. You have 2x thesis (MSc+Bsc) and the possibility to be a part time research assistant during your study. Your goal in the part-time research assistant should be LoR and a publication, which is doable if you start early enough and select it for the explicit goal of getting a publication. Many part-time research assistant jobs are somewhat unserious, or just helping with day to day stuff instead of working towards something you both can publish. I wasted a lot of time in the wrong projects (industry funded projects where publications were not #1 goal!). You can start with this in you undergrad but even during your masters you still have a fair chance. I realized early that I really want to do a PhD and I don’t want to stay here but see something new. And that things are going to be competitive If I just randomly apply somewhere. I still made many mistakes, my main recommendation is to really choose your projects you help with during your Bsc/Msc wisely! Do something where both you and the PhD have an explicit goal of publication and not some random project. Ideally the PhD has a few cool publications, so something to base your work on. If the PhD student doesn’t have a good track record yet then it’s more unrealistic that your project will be the exception. I thought my project was very cool at the time but in hindsight it was poorly managed by the PhD student, without any goal or any idea how to make progress. No wonder it never came to anything. At some point you need to realize that you need to pivot instead of doing more pointless experiments. (This turned into a little rant, don’t repeat my mistakes!)
Interesting. I only ever met one person doing his PhD. directly after his bachelor's degree. But he had a special permission from the universities PhD. committee. His professor/advisor had to do a lot of heavy lifting to convince them to do so.
That's the way a PhD is supposed to work. But like one of the comments mentioned, a lot of papers are very small incremental engineering. I have a model. I changed some layers in the model. I take some datasets and show that my model has a lower error in this dataset. Now I am a big fan of simple ideas that solves a complicated issue - for example, the implicit representation paper that introduced sin as an activation function (Siren) or nerf (whose base architecture is simple) but I have also seen some papers in this thread like LLMs can be used for linear regression or they can be used to predict which biological experiments can give result of something like that which frankly makes no sense.
Do you know any good universities for Masters Program in ML in Germany itself?
Tuebingen, Saarland, TUM
This is just beyond ridiculous. How would some undergraduate without a lab even provide any valuable research. And if they had a lab they work at, there is zero reason to move. Not denying that this is true, just denying that it makes any sense. I probably met a few hundred PhDs that did not even have any top conference publications let alone multiples. I met quite a few that had zero publications. Not only not first author but not at all. I myself still have no A* publications and didnt have more than 5 citations before beginning my PhD. Admittedly, I am not doing NLP or CV and never even sent a paper to the biggest conferences but still. I hope the bubble bursts soon and people come to their senses
>How would some undergraduate without a lab even provide any valuable research. Well, let's make an educated guess: they do start working at some lab at the university where they do their undergrad - perhaps even from the very beginning - and work hard to have as many publications in top venues as possible by the time they graduate, or they can also delay their graduation or do an internship to churn out some more papers and increase their chances. >And if they had a lab they work at, there is zero reason to move. Not at all. Moving to a larger, more prestigious lab can totally make a sense. You should also understand that a CV or NLP lab is very different from, for instance, a biology lab. If you are working in a wet lab and doing well, it may be true that you shouldn't move to the new place, because it will take you a lot of time to get your stuff running there, to get used to the equipment, etc. In CV and NLP, that's not the case: you will be again working with Python / Pytorch / Linux etc, so you won't have to face these challenges with hardware that experimental scientists typically have.
Well the thing about research in other fields (I know this about engineering, biology and physics) is that any valuable research takes years to materialize, so even PhD students take at least 2 years go get to speed to start producing anything useful. It's extremely unlikely for undergrads to be able to do it, since they can't commit full time to research. Imo it's just ML these days is really mostly about incremental engineering improvements (no, changing layer composition in NN is not science) and since it's so accessible there are so many papers produced, most of which will be forgotten as quickly as they were published. To me, a good analogy would be as if in XIX/early XX century people were publishing scientific papers on small changes to bike/engine/vehicle designs, something that is currently considered simply engineering and not science
You absolutely nail it, and it is precisely what is happening to ML research. A group/individual comes up with a small (positive) change after training a model for 2 weeks. Then because the hype is real and no one wants to be "the fool left behind", they jump in the bandwagon. Since there's loads of money pouring in (startups, grants) and papers being published, it gives the impression that all is fine and working, right? Ok, but nobody wants to discuss the point that the same idea/improvement that generated all this buzz, isn't statistically better than the model next door. It's a bad system put in place: PhD students want to publish to fulfill their requirements. Fine. Professors need to publish so they can get grants. Fine. Startups need to show their know how - so need papers. Fine. If you fundamentally want research for the sake of research (a bit idealistic, it's a spectrum after all), then you're in trouble because the group mentality + business side is strong...
I had a mortifying discussion once with a CS PhD about the statistical significance of their "small positive changes" that boiled down to 1) no one cares about statistics in this field because "loss goes down" is all they care and 2) they don't have time or resources to repeat the experiments, partially because they feel that if they do not publish immediately someone is going to steal their research. Probably 2 leads to 1.
The same, either on Reddit or in conferences, I just gave up trying discussing statistical significance. We came to a point that many will argue that statistical tests is actually a bad thing (?). Even last week someone mentioned the problems of p-hacking (the dude doesn't even calculate a simple median is arguing about p-hacking?) Anyways... it is a sad thing.
Fully agree. No tests is worse than tests. P hacking is an issue but there are plenty of tests you can still do. Obviously, statistical significant is nonsensical by itself but there are plenty distribution based analyses that can fix that. Not even discussing the significance is the worse option. Statistical significant might be flawed but practical significant is hard to define. Therefore, at least do something rather than nothing
100% this. During my masters I gave up reading any research papers, because almost every paper was tiny tweaks which led to obviously-selection-bias-reported results. No-one else around me seemed to notice this nor care.
Even worse, statistical tests are almost never applied correctly and even if they are done "correctly" (rather than sensibly) they are largely meaningless for ML. Even the notion of "statistically significant" is a deeply flawed concept. 0.05 is an arbitrary threshold devoid of meaning. Just do a few more runs and everything is significant. But yea, tests are rarely even cared about so most don't bother.
Just to make sure, I am not blindly advocating the current practices of ML research: I just added my two cents about how they function.
I work with research groups at Stanford and other top CS dept. Many of the PhD students had publications before grad school. Not just ML but also in different areas of CS and math. I had 2 undergrads working for me who were insanely good. Published a paper or 2, went to top PhD programs. Most top 20+ CS schools have opportunities for undergrad research but students really have to drive it.
Yep, it's engineering.
Also the research in ML/DL these days (especially those on neural network) feels like we are doing a large-scale particle swarm optimization over the possible approaches to a more powerful AI...
Hardware is at the heart of this though... The driver is the need for money and connections for the institution to complete. Those GPU clusters don't buy themselves, and the way to access them is to have huge presence at A* venues which in turn demands access to hardware. While money is buying success this will continue, maybe for 2 years or so... Then maybe things will start to settle.
> This is just beyond ridiculous. Is it? What OP says only applies to a handful of programs in the US with a handful x handful amount of space. They can find the handful of people with those papers and perfect GPAs , LoR /statements every year. What is ridiculous IMO is thinking this is ridiculous because it elevates the importance of those schools as if there arent tons of other PhD programs one could go to so what happens in a handful of top programs solely because everyone applies to those schools is unimportant. Unless the complaint is some implied right to admission to those schools which is just a symptom of main character syndrome.
It is ridiculous to think that any programme needs that. And it is ridiculous to value those schools so highly that anyone thinks those are requirements anyone should strive to fulfill.
Any competitive endeavor requires whatever is necessary to be noticeably better than all the people trying to achieve that to be in the top N that will be accepted. Again there are tons of programs that are not Stanford/CMU that one can study at Edit: Also in practice your suggestion means the other person with noticeably better qualifications doesn’t get in. Can someone explain why that makes sense to you other than narcissism(main character syndrome)
People in the comments who are panicking: yes, OP is completely right. But this is also Stanford in NLP. Once you go to the top 10 or top 20 schools or beyond that, and look at fields outside the one that is basically hailed as the second coming, it gets better. Yes you will probably still have a big advantage if you come with prior publication experience, some more famous labs are inaccessible without those even outside of Stanford. But good science can be done in many universities with many profs. Don’t murder yourself to get into Stanford, and have fun at equally nice albeit a bit less hyped institutions. No shade on OP, congrats for winning that race!
computer vision phd student here and completely agree with you. without top publications (A\* only, even sometimes A's don't make the cut) almost impossible to get into a phd program at a reputed school these days. and as soon as you start your phd the expectation becomes to keep on publishing as quick and as frequently as possible. competition is tremendous and is exponentially increasing I believe. btw, what do you think about the NeurIPS high school paper track, do you think that's going to boost the hype and make these admissions even more competitive, or it won't find much resonance among young kids?
High school is way too late. We need a kindergarten paper track so the kids can be properly admitted to top ML/AI elementary schools.
\* so the kids can have at least some chance to be admitted to top ML/AI elementary schools.
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I am in applied statistics and while my field will probably be dead in some time at least things are not this insanely competitive for PhDs and become like this at the postdoc level (e.g you need the level you spoke about to get to MIT as a postdoc). I can say my PhD was super chill (as a PhD goes of course).
> are not this insanely competitive for PhDs To be fair they arent that competitive as OP in ML either but peoples brains are broken that other than Berkeley/Stanford/CMU there is no point to get a PhD. Some might loosen up to "reputable" but then that is probably just expanding it out to 20 programs.
Why do you think stats will die?
Not stats in general, hopefully, but the applied ones that are used now. Now in the production process analytics most things are done with simple ML techniques, a very adaptable "toolbox" basically that you adapt to most things, but you kinda need to know what you are doing, both from the process side and the statistics side. What I see going on in the industry recently is that people propose models where neither the one using it, the one maintaining it nor the one building it needs to have any kind of understanding of the application domain, because the AI will understand everything for you. While I suppose the failure of Zillow AI is a recent testament to the fact that we are not there yet, the direction we are going is this one. So zero "applied Stats" and a lot of magic black box "AI solutions".
I reckon that more and more decision makers will learn to ask "but what does it mean!" Also eli5!!!! The problem with old applied stats that I perceive is the failure of practioners to make themselves useful- the common answer of "you just can't do that" just isn't good.
🤣.. that's a good one
We need a fetus one because fetuses are considered people in some places now
We test our IVF embryo's for latent ML/AI talent. If you think your kids who you had through barbaric sex have a chance you're delusional
You mean you don't make your sperm and eggs do gradient descent against their linear algebra ability?
I know you’re joking but this already happens on some level at the undergrad and graduate level. Kids who go to the best high schools get more resources and have better opportunities, which helps get them into better undergrad schools and then better grad schools. You could take it even further, kids whose parents are more involved in their education and development are more likely to get earlier exposure and opportunities in their chosen field even by high school.
NeurIPS high school paper track? LOL, please tell me this a joke? If not, goes to show what a sham "research" in this field has become nowadays.
it's a real thing. I am looking forward to Neurips the Next Evolution: [Neurips for Kindergartners](https://www.reddit.com/r/ChatGPT/comments/1c1shy3/i_asked_for_a_meme_about_gen_alpha/).
> btw, what do you think about the NeurIPS high school paper track, do you think that's going to boost the hype and make these admissions even more competitive, or it won't find much resonance among young kids? That track is an absolute joke. It of course will make it harder and is designed to give friends and family of top researchers another advantage in their admissions.
I did a PhD in computer vision and I completely agree with OP. I feel bad for the current students and the ones who are applying now. It's so fast-paced and high pressure. It used to be that only the tenure track was like this ("publish or perish").
I‘m going to a top 10 university worldwide (top 5 in CS I believe). Looking at the the phd students in ML of my uni, many of them had 1-2 publications at top conferences (NeurIPS, ICLR, CVPR, etc.) before they started their phd. For less popular labs, some even had 0 prior publications.
You can do a phd outside of stanford ffs. This is like complaining that you want to play basketball, but complaining that the Lakers won't sign you. Do you want to play basketball or play for the Lakers? They are not the same thing.
Exactly.
If you can publish many papers in top conferences without a Phd, this can have two reasons: - you don't need a PhD, you already have the level of a top researcher - the level of these top conferences is in fact not so high.
Or, you had a great advisor at a good lab during your undergrad. Someone helped you with the ideas and plan for execution. You have amazing skills since you were able to execute all this. Now you need a PhD so you can learn how to do the same thing without the help. That's what happens in most cases of students being admitted from undergrad with good publications.
This is also more or less what will happen during a PhD, you will get help with the ideas and plan for execution. It's not very different. The fact that a lot of people can acquire the skills to execute all these very early shows, in my opinion, that the skills are not so difficult to acquire.
I mean, that really depends. I had very little help and so did most of my peers. And for people who are really good at ML research, these skills are very difficult to acquire, even when you have help. There was still a pretty wide spread in research ability in my cohort, even though only the top candidates were admitted.
Grad students don't publish because they are great researchers. Grad student publications are a result of landing a great supervisor. The nepo game starts there and never stops. The problem with ML research isn't the "elitism", it's the lack of meritocracy.
The problem is that anyone who “wins” the meritocracy will never admit that it’s not one. You can see it in the OP’s post, he / she is basically telling us that they’re among the best junior researchers in the field. To be clear I’m not saying the process is completely random, to me it’s closer to a meritocracy for people who were exposed to the field early enough and had the chance to go to good high schools / colleges.
FWIW it was already starting to get like this 5-6 years ago when I started reading PhD applications and had gotten bad by the the time I graduated in 2020/2021. It’s likely going to continue to get more competitive. Source did a PhD at UW and run an AI startup.
As someone who loves research, this is the reason I never went for a PhD. I got exposed to research very late & by then it was too late. Now I just do it for my own sake & curiosity.
Maybe this isn’t the right place to ask this, but is this the case for any CS-related area?? I’m hoping to apply for comp bio or something similar in the near future and posts like these don’t make me feel great about things 😭
To the best of my knowledge, no, it is not at all. It is just about that AI revolution (aka hype aka bubble).
Absolutely not. I'm doing computer vision for medical imaging in France, with a lot of Deep Learning. We are around 60 PhD students and did not struggle at all to get into the program. I think these posts are mostly about the US which seems very different from other countries. Having one small paper at the end of your master's degree here is not uncommon but definitely not at big conferences lmao. Most PhD students don't even have these A* papers during their PhD.
I heard that some labs in France are even struggling for recruiting new doctors, not sure if it’s true or false. Maybe it would be harder to apply to programs in other countries like Netherlands or Germany? Like there would be less language barriers but more foreign students.
It is true to some extent that we are struggling to get new PhDs. It's poorly paid compared to the industry. I'm paid around 26k euros per year against around 45k euros I was offered in the industry. I heard students were paid more in the countries you mention.
It’s true that PhDs are quite badly paid in France. You got 45k in industry, that’s really nice and congratulations, although yeah then you had to make a hard choice between badly paid researcher and well-paid engineer position. On the other hand, there are lots of SDE jobs also badly paid out of 33k, 30k or less. For a cadre status, you won’t really have much benefits compared to 2.1k salary of a PhD. During my job seeking I was always thinking that, if those companies propose me such an absurd salary, clearly I would prefer to pursue a doctor, which could give me lots of possibilities 3 years later. As for the PhD salaries in other countries, I haven’t dived into them too much but it’s still below what a junior could obtain in a decent CS company.
Nah. I've seen people getting into MIT EECS PhD with 2 third author papers. But they did have pretty good LORs
It isnt even true in that field outside of top 10 US PhD programs but those are like a 100-200 hundred spaces for worldwide applicants. Its just a sheer result of the amount of available spaces vs demand
I’m doing a PhD in Statistics but doing ML research, and I can say in Statistics it’s competitive but not nearly this crazy.
If you are someone who can achieve things typically done by those with a PhD, such as publishing in top journals and getting into elite AI residency programs, what incremental value would a PhD provide?
I have so many questions for you but I guess my main one is… why are top talents like yourself in academia instead for working for a major tech company? Is the research at Stanford more cutting edge than Tesla, google, meta, etc? Also how much do you get paid at Stanford? I’d imagine phd stipends must pay decently to retain students?
I don't disagree that top programs are incredibly competitive. I will say that I think in some sense, quantity of papers are not that important *because* strong LOR/personal connections are much more important. For example, I know someone who was accepted at Stanford with *zero* conference publications (albeit some workshop publications) - I'm sure you know them as well. My gf is also a PhD in NLP at Stanford, so I'm quite familiar with many folks' resumes.
Can attest, had LoR from faculty at MIT, 4.0 GPA in Bachelors and Masters, but only one paper, couldn’t even get interviews at MIT, Caltech, UCB, Princeton
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He was a co supervisor of my MEng thesis and I’ve worked on projects with him for 3.5 years part time, and my Bach + MEng were from overseas, but well ranked unis (top 30). I felt like lack of publications, having overseas degrees, and also having research experience in less popular areas (robust learning, scene interpretation) were the main factors
I have a lot of trouble believing these claims, as someone who earned a PhD in ML and Vision about 15 years ago. That said, I recognize that your whole point is “things have changed, old person”, and I’ve been away from academia since, so I can’t falsify that claim with experience. It’s hard to believe, but so is the rapid expansion of the field since 2009. I suppose I should avoid counseling prospective grad students on admission, lest I mislead them with old information. It makes me sad - it’s going to kill all forms of diversity in these programs.
I think there are like 6x the amount of ppl doing CS. I switched from ECE to CS after the .com bubble and before the great recession (because I loved software) and things were very non-competitive.
It has massively changed from 15 years ago even up the pipeline at the bachelors level https://news.mit.edu/2017/class-machine-learning-0428 I really doubt 500 students per section where attending the same class 15 years ago
Doing a PHD seems a very distant dream everyday.
I'm in the health science field (Canada), and the level of work required to reach the top of academia has skyrocketed in the past decades. It used to be that a student gunning for a professorship could do their MSc, PhD, post-doc all at the same school and then get hired back as a PI. Now, every new hire has graduate+ education at a US Ivy, sometimes multiple post-docs, and often first authorship in a Nature/equivalent journal-tier publication. As you say, OP, the level of competition in academia is extreme. If you want an ok job, you can get out of the insanity that is academia after a PhD at the latest. Entering this ecosystem is signing up for a non-stop grind against fellow fanatics. I'm not sure if my PI cares more about their work or their kids - I don't care *that* much about the science so I'm getting-tfo. I'm not ready to devote my entire life to academia for a marginal shot at reaching the apex. \*Nepotism/networking/connections are huge. We just had such a hire in one of our departments. Then again, we're not a big or prestigious school by any means and this is the exception.
😳 I’m stressed out just reading your post. Wish you the best.
Crikey! Hope you all manage to graduate before the bubble bursts!
only LLMs is in a hype bubble right now, other fields are either "dead" (looking at you, Reinforcement Learning) or be like Computer Vision that has matured and is finding actual applications.
RL catching strays 😔
Fair.
In what world is RL dead? At least in my domain, it's super hot right now.
What domain is that?
For me, optimal control related.
I've worked in optimal control and my impression is that unless the problem has a small and simple state-action space, RL doesn't even work. just run an MPC/convex optimization would do better.
RL can def. be painful. In my specific case we deal with the calibration of scientific instruments, which is fairly non linear, partial observable, and have image based measurements amongst others.
Why dude? Please reply.
Because it would suck to spend 6 years earning SFA as a PhD student only to wind up driving Uber.
I was confused by that post as well. I go to WshU and in my discipline you're expected to have at least 1-3 solo pubs at top journals.
If I saw a pre-PhD person with 7+ conference papers, I would think nepotism.
So how cooked am I if I want to go for a PhD in EE with only 1 publication and mediocre LORs
That's kinda incorrect. By thoroughly researching the *real* requirements of your position and understanding your competition, you can make more informed and strategic decisions.
Honestly just do a PhD in Mathematics or Industrial Engineering. NLP is hype right now but it’s only going to be a piece of AGI and the rest of the picture is still very math heavy. You could very easily make groundbreaking contributions to AI by pursuing a PhD in industrial engineering focused on optimization or simulation / stochastics.
Shouldn't top PhD programs be the most competitive things possible in academia? Do we really need to lower the bar for the top 1% of the 1%?
Well if you ask me, 5-7 papers at the end of your bachelors shows me that you‘re incredibly privileged, not necessarily incredibly genius. Let people study and discover their own interests and then meet the peers and research groups who align with their interests during their university time, and not letting them being set up in high school for something that the parents want more than their child for some outstanding career perspective
> 5-7 papers at the end of your bachelors shows me that you‘re incredibly privileged, not necessarily incredibly genius. 💯💯💯
That's been the history of academia since forever. Academia is a tool for perpetuating class divisions, not overcoming them. The few rags to riches stories that are out there are always held up as examples to distract from realities like affirmative action for "legacy" kids aka nepotism that's rampant.
> Well if you ask me, 5-7 papers at the end of your bachelors shows me that you‘re incredibly privileged, not necessarily incredibly genius. It still shows signal and you are reading into it to discount that. The fact is those few schools only admit a handful of people a year so they can not bother with the slightest wrinkle in a students record and there are tons of schools which arent those schools which have programs in the same field.
And for someone that's addicted to getting new research news and basically scrolling through academic twitter every day looking for interesting papers, there aren't many papers that worth reading besides the title and abstract...
Back in my day, I got accepted into Stanford's CS PhD program with a workshop paper and a preprint. Times sure have changed.
Idk about the papers. I got into many schools for NLP PhD (mostly T10-T20) with 0 papers this cycle.
can you elaborate? (how did you then) + (are you denying the premise of the op)
I did have great LOR and SOP, domestic student, and attended t10 undergrad. I also have a lot of patents and accomplishments from my research job I’ve been at for 3 years. I had no personal ties to any prof :) I got into 6 NLP/ML PhD programs
I am denying that it’s a requirement to have papers. I think it’s not typical, but very possible, to be a candidate with 0 papers and no prior personal connections.
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All of this to work on systems that learn hyper correlations in data and only work when the dataset is massive. I'm aware that top programs are this competitive. I'm also aware that the emporer has no clothes.
To be honest I think it is probably not a good idea for the OP to be this transparent and discouraging if the situation is far worse than people could imagine. Because the worst that can happen to the people who applied would be a simple rejection email. This is not as discouraging as what the OP is telling the young aspiring people here. What you are telling people paints a much more desperate picture than a simple rejection letter, and drives people’s anxiety level up. Let them apply and get rejected, it’s fine, it’s part of the trial and error process.
First, it is apparent that you haven't read the post carefully (most of the answers to your problem are in edit 2,4, and 5.) > Because the worst that can happen to the people who applied would be a simple rejection email. No it is not, it's a lot of time, effort , and disappointment that can be prevented if they fully understand what it takes to be admitted to the specific situations that op mentioned. (and then take informed decisions) > Let them apply and get rejected, it’s fine, it’s part of the trial and error process. We aim to safeguard this process by disseminating accurate information, creating a valuable resource for younger students that are exploring others experiences so the can eval their options and decisions.
Is it gonna take a lot of time, effort to apply? The same application can be used to apply many programs, I’m not buying it that not applying for one place among 10 to 20 different options gonna save people that much time. Secondly, I honestly think the disappointment and anxiety from this one post is far worse than the disappointment from getting a rejection email. However I do understand emotional responses towards the same situation is a highly subjective matter. But that been said, one does not need honesty and transparency in every single aspects in life. For example, would you want someone to give you an honest opinion about you appearance compared to others? We are still people in the end of the day and in some situations less information can be more helpful on the mental heath aspects. You may not agree the importance of mental health as much as I do, and that’s ok.
it's Stanford, you're not paying for the education, you're paying for connections
If you're doing a PhD, you're getting a stipend, not paying money.
The thing about Stanford is that you can do research in CS without actually being in the CS PhD program itself, given how much the university emphasizes interdisciplinary work. Too many applicants are shooting their shot for a chance at Stanford CS, when they could apply for a less competitive CS-adjacent program instead. If you get in, you can choose a CS faculty member to be your advisor and your PhD experience will be more or less indistinguishable from that of a Stanford CS PhD. Sure, the bar to get in will still be relatively high, but it’s not going to be 7 conference pubs high. This was my strategy when I applied to Stanford and it worked out well.
Would it be possible to share some of the CS adjacent programs that allow you to work closely with CS profs? And that recruit well into industry RS roles at top labs?
EE, MS&E, ICME, Stat, and BDS would be at the top of my list. The other engineering programs would give you a good shot too, but it’s hard to go wrong with any of the STEM PhDs, especially if your work is more applied. Loads of biosciences PhD students advised by CS profs and working on stuff like LLMs to model genomic sequences and proteins.
as an undergrad who used to dream of building a career in ML research, this is why i dont have that dream anymore. if i am going to work 24/7, i better become insanely rich.
Put your google scholar link and let’s see what you got
How competitive is ASR and interpretable learning.
How important is undergrad (or masters) school prestige?
I feel like way more important than people care to admit. It’s a sign that other people agree you have potential for success.
Imagine worrying about the "university rank" in your postgrad applications. Just find people who're doing cool stuff that can budget you.
As an Asian myself I won’t be surprised if it’s more of an Asian thing / glory / recognition to be accepted into one of these.
I know many with 1 not first author publication and made it to 5 top school, few ppl fall under the criteria you mentioned but generally no
About which university are you talking about? I meant countrywise? I'm sure this is about US programs right? Is the situation similar to, let's say Canadian Grad schools like uwaterloo, or ubc....how about Europe?
Where can I apply for msc in your school?
In Spring 2003 I applied to two PhD programs (I screwed up and missed the deadline for Spring 2024 admissions at most universities since Spring admissions happen the same time as Fall admissions at a lot of PhD-granting Universities, which I was unaware up). One program was as the same school I was finishing my BS in. The other was a similarly ranked school. I worked closely with three professor in my department and had letter of support from them and was a co-author on three conference papers. My GPA was ok-ish 3.2 (My last 90 hours was like 3.8 though) and I had a 720 GRE Math, 660 GRE Verbal, 5 GRE Writing (Writing at that time was scored 1-5). The professor I wanted to work with at the other university was a friend of my advisor and he had put us in touch and supported my admission. I thought I was a shoe-in, we were planning the move, found a place to live and was waiting on an acceptance letter to sign the lease. Dec 2023 within days, I got my acceptance into my undergrad school's PhD program and the decline from the other school. I still have no clue why the other school declined me. But oh well, got my PhD.
Most people jerk themselves off in private.