I understand that Andriej Karpathy (my favourite author/lecturer in Deep Learning, by a large margin) had a wonderful PhD, in a fast-growing field, with a golden fall-back option. But most PhD students I know (including my former self) do things in disciplines no-one else cares about and are tied to their institute/advisor/place with little to no opportunity to change things when they go awry (cf. it's super easy to change a company). A non-trivial fraction of my friends suffered from depression or had a serious mental breakdown (again, including myself).
In this light, while it contain a large number of helpful tips and valuable pieces of advise, why is it called "survival guide"?
Karpathy has the benefit of being one of the best known bloggers/teachers in the most popular graduate-level course in possibly the most financially successful subfields ever while graduating at the peak of industry spending (so far). He's almost certainly the student described in http://www.nytimes.com/2016/03/26/technology/the-race-is-on-... His experiences and successes are not generalizable.
But he's probably my favorite blogger too and its at the very least interesting to hear his take on his experiences.
I'd assumed Karpathy was the the $1M student too, but apparently it was someone else (source: inter-university gossip). The same source did say the big4 were marking $300K offers on the NIPS floor last year, which I find even more extreme.
It seems that a larger-than-I-expected fraction of the response to my post concentrates on a very small part of it, especially the part where I enumerate some considerations for thinking about whether a PhD might be a good fit for you.
By far the largest fraction of the post is concerned with tips/tricks for effectively navigating the PhD experience once you commit to going through it. I jokingly refer to it as a "survival guide" because (as I mention in the disclaimer paragraph) the experience is by no means a walk in the park.
Also do you mind modifying the title to say a Survival Guide to a C.S. PhD?
Absolutely everything the GP says is spot on, the C.S. PhD is really atypical amongst all the Ph.D. disciplines. E.g. the resource constraints that exist in many disciplines don't even exist in C.S. - it is almost completely the product of thought/ideas and ever cheapening computing power. I would suggest sending out your article to Ph.D. students in different disciplines, look at all the feedback, and then incorporate them if you are so inclined. The article might look quite different.
Edit: after reading the comment from pgbovine, I felt I made an unnecessary personal comment, which I omitted. I rewrote the sentence to keep the main point.
At most, link to this HN thread so that readers can see different perspectives, but you wrote the article you wanted to write, not the one that anonymous online critics wanted you to write. If others want to write a response saying why you're misguided, by all means go ahead.
I disagree. It's true that it's easier to get funding for CS but the career strategies described are exactly the same for most other fields. Many phd students just can't understand this post though; they think they're 'students' like they were in undergrad but it's whole different ballgame. The concept of the symbiotic relationship between adviser and student for example - most phd students just cannot see that, even after it's spelled out for them. Ito requires a certain mental maturity that most 23 year olds just do not have, it seems.
A friend of mine did a PhD in Biochemistry. Here is what I learnt, based on the many conversations I had with him while he was doing his PhD:
Freedom to choose the topic: Only to the extent that he could choose the advisor amongst a list of 3-4, which gets narrower after advisor selection
Ownership: I understand the sense in which the author wrote this, so I cannot disagree too much. But my friend would often explain how he could be most productive precisely by being a 'cog in the wheel' in the sense of how much cooperation was required from his lab mates for him to make any amount of reasonable progress
Exclusivity: The exclusivity is true in the sense it is described, but unlike in CS, did not lead to any major benefits (so sort of diminishes the leading statement about the appeal of the PhD). He went through a couple of post-docs, and then eventually landed what was a coveted position in his field - which still pays not very much
Status: This is the only part which I completely agree, and I actually respect non C.S. Ph.D.s all the more for their persistence because there is very rarely an escape hatch.
Personal freedom: Almost 100% not applicable. As I mentioned before, my friend could not make any progress without a ton of cooperation from lab mates, needed to be in the lab usually based on timings of other lab mates.
Maximizing future choice: Nearly every discipline other than CS would disagree with this. If you read stared's story, you get the distinct sense that his choices were maximized because he came into data science (i.e. he didn't feel that way while in his discipline)
Maximizing variance: This is immensely difficult for science Ph.D.s from what I understand, because the process from Bachelors to PhD, and often with a PostDoc or two on the way, is already too long for most of them and takes up their best years. So the statement "You’re young and there’s really no need to rush" is, well, a bit impractical.
I cannot comment on 'Personal Growth' and 'Expertise' - I don't know if you need a PhD for the former, and the latter is wonderful as long as the cost is not exorbitant (this exorbitant cost is common in other disciplines)
If the advice here is widely generalizable, then I would really like to see a few links to PhDs in, e.g. the physical sciences talking about their experiences with similar pleasure.
On the other hand, the stories I heard from my friends who did their Ph.D.s in other disciplines (Chemical Engineering, Mechanical Engg., Civil Engg., Chemistry, Physics to name some I remember) all had very similar patterns in their horror stories of the lack of resources and its impact on their journey.
So, is it at all possible that a CS Ph.D. who worked on an excellent topic (for which karpathy gets my kudos) in an internet-friendly, internet-visible, exploding field of work at a top institute might not paint anything close to the full picture?
True, that is the not the full article. But the best way to be safe is to never get into a fight - don't get into a PhD without understanding what you are signing up for. The section answering the question: "First, should you want to get a PhD?" is not well researched or widely sourced once you consider that Computer Science is not the only discipline in which people get their PhDs. Hence the suggestion to change the title to C.S. Ph.D.
Yes, it is a small part of text, but one that may persuade someone into doing PhD, or give a false impression (e.g. the typical one, in which I used to believe: "follow your dreams in academia or get money at a dull job").
(BTW: I guess you know http://www.pgbovine.net/PhD-memoir.htm. Also from an uber-successful PhD student, but the full story, rather than a set of advice.)
It may be something about the field (growth, competition with industry). I think it was not a coincidence that out of many friends of mine who did their PhDs, only Wojciech Zaremba (now in OpenAI) had some non-trivial impact on the world.
I don't want to imply that even if everything works (topic, advisor, funding, the sense of meaning, the sense of progress, ...) it is any easy path. And I am really sure that even with your skills, work ethics (and luck) it was a challenge. Still, even if one field is rosy (DL or maybe CS in general), a typical PhD experience is hardly sth I would recommend blindly (vide links there: https://pinboard.in/search/u:pmigdal?query=academia+depressi...).
(On an unrelated note: thank you for "The Unreasonable Effectiveness of Recurrent Neural Networks", ConvNetJS and CS231N - they brought me into the deep learning world. :))
> I do data science freelancing. That is, I take contracts related to machine learning (predicting things, e.g. user growth of a company), data visualization (custom charts in D3.js), preparing and conducting trainings in data analysis [...]
Would you mind sharing a bit on your approach to contracting in this space?
Here are a few questions: Do you do blind calls? Do you use a freelancing site? Do you work remotely? What is the typical contract, how much do you bill? Does one have to do public talks to get recognised? How much do clients value your having a PhD? How do you animate the networking? Do clients find you, or do you find them? Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject? If you had to specialise in one niche market, what would be, what would be your approach? Basically: what would be the steps you would take should you start only with the data science technical knowledge?
It's a longer story, with summary is in "My story" from http://p.migdal.pl/2016/03/15/data-science-intro-for-math-ph.... Since it had some turbulent nature, it was hard for me to put it into a coherent narrative. And it may be even harder to put it in a way that is beneficial for others (involves my particular situation, skillset, network of contacts, personality).
If you mail me (my website's footer), I will send you a quick&dirty summary of my path & projects. In any case, some answers:
> Do you do blind calls?
Never! But if there is an opening for a full-time position sometimes I mailed them anyway, if they are interested in some specialised contracts; sometimes they were.
> Do you use a freelancing site?
No. I followed a mailing list with freelance projects in data viz. (By far the easiest place to start, as they can SEE my previous projects and current progress.)
> Do you work remotely?
Almost entirely. But for the last few weeks I am a bit more related to a particular company, and then I prefer to be on site (easier to talk etc).
> What is the typical contract, how much do you bill?
Since it varies a lot (and I want to increase it a bit), I am not comfortable to put in publicly. Expect for things I do it is also dependent on place in which I live, particular projects, negotiation skills, project uncertainty (data science is not webdev - each project has a research part).
> Does one have to do public talks to get recognised?
Yes. I mean, maybe it is not strictly necessary, but public talks (meetups, conferences, etc) and other public activity (blog posts, running communities) helped me a lot! (But I love it anyway.)
> How much do clients value your having a PhD?
A wonderful discussion starter, never a deal-maker.
> How do you animate the networking?
(Answered above.)
> Do clients find you, or do you find them?
In the last year (or more) it's only clients who contact me, and I accept (/follow up) or decline projects.
> Why are they buying, FOMO on a marketing dataset, or just plain curiosity on the subject?
?
> If you had to specialise in one niche market, what would be, what would be your approach?
If you were an animal, which... ;)
> Basically: what would be the steps you would take should you start only with the data science technical knowledge?
I recently met a professor, he said that most of his students are working in Deep Learning and he is finding it difficult to get people to work on other interesting problems. So there is really a demand of students to work on other areas as well. But most of the students get carried away by the trending fields.
I had a very different personal experience, but Karpathy did a wonderful PhD and I did a very marginal one (at a good school, which in many ways makes it worse). From the perspective of the "anchor" rather than the valedictorian, I'd say that he's right on almost all points as to what you should look to do if you decide to be a PhD. However, I do take exception at the rather false dichotomy between industry and academia that he creates.
A good PhD leads to many of the nice things he describes: freedom and ownership and personal growth and all that stuff. An average PhD (or worse) leads to pretty much the opposite. Most of the superstars I know went on to do pretty much whatever they are interested in at top schools. The non-superstars (and the real lumps, like me) can easily wind up in a death spiral - where your mediocre publishing record and mediocre PhD afford entry only into 3rd-tier institutions, where you will work with worse and worse people, more or less guaranteeing steadily declining quality of work. A mediocre result more or less guarantees that you will be a low-status drone in academia, trying to wedge world-class work in with a bunch of other activities (teaching, being a glorified research assistant, and other 'service').
No-one sets out to do a bad PhD, but people need to understand that the average outcome isn't nearly as glowing as Karpathy outlines. Similarly, the outcome of going to industry also has a huge range. I found myself immediately - I mean on Day 1 - doing more pure Computer Science going to work for a startup than I had any reasonable hope of doing as a semi-failed academic, and have had a steadily improving experience subsequently (some of this stems from a rather delayed growing-up on my part, so it's not entirely a judgement on industry vs academia).
I think it's more of a continuum than you suppose in your comment. It's not a binary of "great" vs "average" PhD. There's plenty in between and many types of averages. Karpathy had it good, as did Might, Guo, and other purveyors of wisdom on PhD. But that doesn't invalidate their arguments and observations.
I think I covered some of that. And yes, it definitely is a continuum, agreed. A semi-superstar could probably have a pretty decent life working very comfortably on an interesting problem, if not necessarily a Hot Problem At The Best Institution.
However, continuum aside, there are a LOT of PhDs produced worldwide, and a LOT of good ones, and very few particularly good academic jobs. A lot of my observations of why things are kind of shitty lie not from my own career (which was largely my fault) but from observing smarter, harder-working, more organized people a couple of rungs up and realizing what a cruddy experience they were having in the middle tier. And also thinking: "man, if I just got my shit together and worked insanely hard for a few years, I could be as unhappy as Professor XYZ".
I wouldn't say "don't do a PhD". Just "know the odds"; i.e. know how many of the positions you could see yourself doing are available vs. PhD students emerging at a similar tier, and ask yourself whether you're going to be in that percentage, and what your plans are if you aren't. I learned a lot from my PhD, even if I sucked, and it turned out my backup plan was really much better than I realized it would be.
I very much enjoyed reading through Guo's PhD grind, which came out as I was nearing the end of my PhD. However, the picture he paints of the grind, where he stumbles along a few different projects that end up in top tier journals/conference with intermittent internships at high profile companies is a thing to envy for the vast majority of PhD students. I'm not saying he didn't work hard, that his experience wasn't genuine, etc., but the memoir is hard to read as anything other than a string of incredible successes when compared to a typical PhD student's experience. In the narrative of the book they only sound like failures because he is comparing himself to Stanford professors who are at the top of the food chain.
Well now I feel a bit embarrassed about the sibling comment I made... since the man himself has appeared I just wanted to reiterate that I enjoyed reading your memoir, as did several students in the lab I worked in. We even went and tried out IncPy for a bit, which was fun :)
Isn't what you are saying a bit extreme? I mean, PhDs in Europe are very short and thus students don't have the opportunity to do that much. So in a way they are quite mediocre. Yet some recover afterwards. But I agree it's hard to revert the trend if you're in a down-spiral.
It's not as straightforward a distinction as you imply, and certainly not grounds for attributing a lower value.
In Europe (Germany in particular), you have to do a 2-year Masters degree which tends to be ~1.3 years of coursework and ~0.7 years of research culminating in a thesis.
Judged by the quality of your thesis, you then try to get accepted into a group for a PhD which takes 3 years on average.
In the US you go straight to a 5-year PhD program and within that, the coursework-research duration split is similar to what you find in Europe.
At CMU people were occasionally referred to as 'being on the 4-year plan', which meant you were like a superior alien intelligence (and, more importantly, focused on some good problem rather than, say, beer and girls or whatever you're into). But I think 7 years was typical, especially in "systems" or anything where you'd have to build something. Places that provision for 3 years total for a PhD are doing something fundamentally different, as people doing good quality PhDs at CMU were already casting around for topics quite early in those 6-7 years. I'm sure that in the long run it evens out for good people coming from good places in either system, as of course, the euro-PhD person can do 3 more years of post-doc under similar conditions.
Most phds I was involved with (Europe) were 4 years with the understanding there'd be a 2 year post doc if there was money (which usuallybcan be found if they want to keep you; even if it means 75% teaching), if you're not a complete cock up and if you want it.
Imho people make a bigger deal of EU/US phd differences than there actually is. The structure is different, but the end result not so much.
"I’ll assume that the second option you are considering is joining a medium-large company (which is likely most common). Ask yourself if you find the following properties appealing: ..."
Why is every conversation about PHDs always cast in the light of as-opposed-to-working-for-the-man? I don't see discussions ever bring up the plethora of other life courses one can take. It is though the author sees a very clear binary: PHD or go work on fixing bugs in Gmail (or some other such cog-in-a-machine project).
Where is the discussion of starting a business? Of making your own company? Breaking free of the political shackles of academia and blazing your own path to glory?
I am all for PHDs, and all for people pursuing, and pushing, the boundaries of human knowledge. I would just like to see that discussion live on its own, without comparison, if that is possible.
* Getting into a PhD program: references, references, references
* Student adviser relationship
* Pre-vs-post tenure
* Impressing an adviser
* (Topic) Plays to your adviser’s interests and strengths
Much of your freedom goes to learning to play politics and manage up to a level employees never have to. Reading this, I'm so thankful I didn't enter academia. I still pursue my academic interests on my own.
Edit: A few people insist on casting a float into a bool. Every job has politics, ranging from 0.01% (anonymous author of 1-man SaaS) to 99% (politician). It's not very informative to note that both are non-zero.
> Much of your freedom goes to learning to play politics
That's crazy. The way to get a great reference is to do outstanding work. If by 'playing politics' you mean not pissing people off by being an asshole or being unreliable, then, yeah, take care to be a good colleague. But almost entirely: do really good work.
Bash other people's novel work while peer reviewing it anonymously, then steal it and submit for publication at another conference before the rejection notification goes to the original author.
Once someone makes it into the review pools for top conferences, they receive a constant feed of good ideas to harvest in the form of submissions.
From my super-limited experience in academia, I found politics is more important at top schools and much less important when you get to ~40-80 ranked US schools for PhD programs.
I went through a PhD at a non-top-tier school (borderline between second-tier and third-tier). I definitely dealt with politics extensively. A huge amount of people-pleasing, both with your advisor and with others in your chosen research area and the broader field it sits in. A fair bit of funding-related politics. And a fair bit of actual politics politics, if you want to fit in.
There's a lot of in-group / out-group politics in academia; don't make life more difficult for yourself when you know a straightforward way to survive. Learn to deeply bury any opinions you might have that don't completely align with those of people who control your fate, especially in areas fundamental to their in-group identity, and if that fails, lie convincingly. (Remember that dodging a question where enthusiastic agreement was expected is a form of answer, and not the one you want to give.) You might get lucky and work with people accepting of differences of opinion, but I wouldn't recommend risking it when years of your life are on the line.
I don't regret the experience, and quite frankly I learned a few other survival skills in the process that simply hadn't come up as an undergrad, but don't go into it thinking it's a purely technical experience, or that you won't have to deal with a pile of utterly ridiculous BS and unpleasantness.
Having spent time at schools of various "ranks" (whatever that means), "politics" (whatever that means ... that's super vague) is everywhere. It's what you make of it. Whenever you have people, money, and status mixing, there is politics. That's not exclusive to academia. Every company I've worked at also had those dynamics. Want to eliminate politics? Be independently wealthy and work alone :) Otherwise everyone needs to learn to work with and transcend the system in their own unique way.
> Much of your freedom goes to learning to play politics and manage up to a level employees never have to.
Yeah, internal corporate politics never gets people's entire project canceled with no notice in the corporate world. That would just be mean, so it doesn't happen /s.
That sucks, but politics in the workplace can be dialed from mega-corp to startup to consultancy to freelancer to anonymous author of a SaaS. How much politics did patio11 deal with when running Bingo Card Creator?
I found the academia to be way more vicious and politics than a company.
In academia, teachers/referees/directors were commonly there for 10-20 years, some will probably never have to live their positions and they have full unlimited authority for many things.
The worse that could happen in academia as a student is to have politics goes against your back. You will be blocked from graduating, you will loose X years of study unlikely to start over or graduate at another place, and be stuck with your debt and no diploma.
In a company, the worst that could happen is simply to be fired. You look for another job and keep your money, you keep your diploma, keep your experiences.
In fact, if you have a problem with your boss at work, you can always change job. If you have a problem with your PhD (or master's) teacher in academia, you can't leave without failing your studies and you're fucked.
Yes, but (importantly) degrees vary. The politics of working for an adviser chasing tenure are different from, say, running a SaaS, writing an ebook, or selling a plugin.
You mean, politic is different if you're not working for anyone? I can't even agree with that. You will still have politic in place if you have people working for you, or if you have critics reviewing your ebook, or if you're trying to get your plugin accepted in a store, etc...
Agreed completely. I did a PhD knowing that I had zero interest in going into academia, and it provided a major boost to my professional career. I find it surprising when people talk about doing a PhD as exclusively useful for academics.
Agreed. The main reason I got my PhD was because all of the jobs I found interesting after finishing my BS said "PhD required" on their application. I never planned on going into academia.
A friend and I (both PhDs) were just discussing how difficult having a PhD has made getting engineering positions.In the course of interviewing at top-5 companies, we both have experienced a$$hats/interviewers with complexes who seem bent on showing off how they are smarter than the PhD interviewee .. curious if you have run into this as well, and any strategies to guard against this? I've also seen people who think PhDs can't code to save their life (I've seen such people but those are very obvious ... people who couldn't do fizbuzz) or think PhDs would get bored and leave easily. How does a competent PhD job seeker deal with this crap?
Seek jobs for which a PhD is a minimum requirement, or else leave the fact that you have a PhD out of your application materials if you're dead set on jobs without that requirement (and it's possible to do). I've seen some success in both cases. Also consider that you may have some confidence/arrogance issues of your own. Not to be accusatory, and certainly I'm projecting a bit here, but having left my academic life behind some years ago, when I look back on it I see a certain arrogance in the pride of having a PhD that I'm embarrassed by now.
If you're running into these problems for jobs that do list PhD as the minimum requirement, you should consider it a strong signal that avoiding working there is a net positive for you.
And if you think you have it rough, try getting jobs after having dropped out of a PhD program a few years into it. For some reason, at least in the sphere of my friends and associates, this signals "quitter" and "not as smart as a PhD person". I've interviewed such candidates and I'd say it's a mixed bag with respect to whether I've preferred them over PhD candidates I've interviewed for jobs, but almost uniformly I've had one or more colleagues raise the question of said candidates' ability and determination.
People (employers and PhD holders alike, really) need to understand that having a PhD is a strong signal for exactly three things, in this order: a person's willingness and ability to tolerate a certain kind of experience in the very narrow context of academia, some minimum level of expertise in the field above the typical undergraduate degree holder, and some further expertise in a very narrow slice of the field above a typical Master's degree holder.
I didn't run into any of that in any of the companies I interviewed with; if anything, I found that coming in with a PhD resulted in correspondingly higher expectations. I'd treat any of the items you mentioned as warning signs and look for a better opportunity.
To specifically counteract the "can't code" item, have plenty of practical items on your resume; include your published papers and conference presentations, but also include projects you've contributed to and similar. (For people doing a PhD who plan to work in industry, make sure you present at some industry conferences, not just academic conferences, and publish your code as/into Open Source projects.) A CV for an academic position doesn't work unmodified as a professional resume (with possible exceptions if you want to work in an industry research lab).
In the specific context of CS, if you look at medium to large companies, you should already ask questions about the technical job ladder (such as making sure that one exists, rather than the only promotion path leading into management). Ask specific questions about how a PhD affects your starting position on that ladder. If it doesn't at all, then seriously consider looking for somewhere that it does. A company that values PhDs seems less likely to hold the misconceptions you mentioned.
Yes I've run into this. It's frustrating and there's not much you can do because of the power-imbalance between interviewer and interviewee. I try to be content with the fact that interviewing is mostly arbitrary with high variance. If you're set on a single company you want to work for keep trying and never burn bridges there and eventually you'll get in.
In data science which is the track I'm on, I've run into situations during interviews where
1) I can only scratch the surface of what's being asked
2) I answer the question to the satisfaction of the interviewer's skill level
3) I answer the question beyond the interviewer's skill level.
Only #2 is good. #3 happens rarely but you'd be surprised at how often it happens even at top5 companies. I've been dinged from interviewers in the #3 camp who were looking for the simpler answers. #1 is always a learning experience for me :)
"Where is the discussion of starting a business? Of making your own company? Breaking free of the political shackles of academia and blazing your own path to glory?"
Good point. Several, nice examples from my field of study are AbsInt, Kesterel, Galois, and AdaCore.
AbsInt turns elite work of Ph.D.'s into commercial products and enhances them. Doubt it's boring. Kesterel has people doing academic-style research on hard problems and applying it to real-world. Galois does that too. AdaCore does a combo of tooling for grunt work, advanced tooling (esp compiler), and cutting-edge work (eg SPARK provers) involving academics remote or in-sourced. A good PhD in relevant subjects might get a job at any of these companies with similarly interesting work. Or, if tool is good enough, make a company out of it by partnering with people good at the business/sales side.
So, definitely opportunities here beyond black and white of academic-only or grunt work.
>Where is the discussion of starting a business? Of making your own company? Breaking free of the political shackles of academia and blazing your own path to glory?
The discussion of what now? Not everyone wants to start a business. Not everyone has the domain and market knowledge to start a successful business. "Blazing your own path to glory" fails 90% of the time, usually repeatedly.
If you're gonna be that damned persistent, you might as well have the freedom of academia.
Hm... are there any options other than hoping against all odds to successfully navigate the tenure-track rat-race, working for The Man as an anonymous cog at BigCorp, or starting a business and pouring myself into businessy businessperson busywork? I'm afraid... I'm afraid I may be a defective individual in that none of these sound terribly appealing as a way to spend the bulk of my life.
Please, don't do a PhD. The tenure track treadmill is brutal and on the wane. You would be much happier just getting a job. A normal job. With ordinary people. And I say that after a masters degree, many years in a PhD program and the having a normal job. A PhD is sort of useless at this point in history.
Good advice, but bad news: Too late. (Well, I quit ABD (with a consolation Master's) and got a BigCorp job, but wasn't too thrilled with that, and since now I've got this monkey on my back, I keep trying to finish writing my dissertation again, pointless though I know it to be... Actually, sounds like our experiences are quite similar except for the last bit!)
[No one's ever going to put in the effort to de-anonymize me off Hacker News, right? Just in case: Dear future potential employers reading this, A) Please don't, and B) Hey, I was totally kidding about not being thrilled with the BigCorp job. I love working for The Man! Go you! You The Man!]
Sure! I merely presented a few of the more common case paths! For instance, a good friend of mine (who shall remain nameless for anonymities sake) is currently exploring the world, working as a cook in cafes, restaurants, etc, never staying anywhere for long, living out of a backpack. Very romantic, I could never do it myself, but his life is his own.
I dont think man has ever been as truly free as we are today, you can really do anything you set your mind to, not confined to the short, somewhat stuffy list you and I have walked over together.
Break free of the political shackles of academia and go on trying to convince people to invest in your business, become employees, and convince customers to use your services? At least the political shackles of academia are intellectually stimulating and not pure dreary salesmanship like the political shackles of business ownership.
Eventually I decided that I wanted to do research and I couldn't be more happy about it. My experience as a PhD student was awesome, I did a postdoc on a topic that interested me very much but quite different than what I did during my PhD, and I've just landed a permanent position in academia as an associate professor!
All valid advice, but god is some of it depressing. Papers can be evaluated by flipping through and looking for pretty graphs and equations. Incremental, replication, or comparison work is discouraged. Never include the dead ends or what didn't work. Get into only the elite colleges and betwork at conferences as much as possible - it's not what you know but who you know. Hype up and make your paper as sexy and short as possible. Tell a story. Good teaching, blogging, and sharing software probably hurts you.
> Good teaching, blogging, and sharing software probably hurts you.
Karpathy is pretty famous for his blogging and open software. His code and blog post about recurrent neural networks practically got him a language model named after him.
He also recommend you to release code in this article, so I'm not really sure what you are getting at.
> Karpathy is pretty famous for his blogging and open software.
Yes, he's famous for that, but that didn't earn him his PhD nor would it have gotten him tenure. As he remarks about his teaching of his course, him doing a good job actively worked against him because... it's not writing a sexy new paper or networking.
> He also recommend you to release code in this article, so I'm not really sure what you are getting at.
Releasing code isn't the same thing as creating polished end-user applicable stuff on the level of char-rnn. In ML, you're increasingly expected to at least chuck over the wall a barebones implementation to demonstrate it works at all, but there is no expectation that it will be generalized, well-written, or polished, or maintained, and typically they are not. (Most ML releases I've looked at are kind of horrifying from a software engineering perspective. Just thinking about improved-gan makes me shudder.)
Most of the "Release your code" section is about how you should put your code in public because in makes you do the extra effort to write a better implementation.
> but that didn't earn him his PhD nor would it have gotten him tenure.
He doesn't have tenure. He works at a private organisation. We don't know how he landed it, but I think his internet famous helped.
In the "Don't play the game" section, which is where I think you got the "don't teach"-thing, he writes:
> but I did them anyway, I would do it the same way again, and here I am encouraging others to as well.
How you read this as anything but an encouragement to teach, write software and blogging is a mystery to me.
I feel one disclaimer that karpathy modestly left out is that he is perhaps one of the most successful PhD students in his field. This likely impacts his view of a PhD program. I personally survived a PhD myself (also in CS, also in ML, also in Computer Vision!), and I've passed through without so many warm, fuzzy feelings.
Could you maybe go over some experiences of your PhD that are orthogonal to those of Karpathy? I'm really interested and I'm sure there are others who are as well.
> Personal freedom. As a PhD student you’re your own boss. Want to sleep in today? Sure.
This is largely true, but only if you're on good terms with your advisor and they're happy with your progress. God, I miss being able to sleep in until 2pm.
> Personal growth. ... you’ll become a master of managing your own psychology
Yes, it's definitely a roller coaster. I know what happens to your body after a month when your only calorie source is peanut butter and white bread. Depression, random trips to Canada, and more.
> Picking the school. ... your dream school should 1) be a top school
No, at least, in mine and other's experience, you should go to the best school where you're still capable of being in the top ~1% of your graduating class. You'll feel like you're the best and that's almost all that matters (Malcolm Gladwell's talk [1]).
> So you’ve entered a PhD program and found an adviser. Now what do you work on?
You will not be interested in the same exact topic for ~5 years straight, so keep that mind. Try to keep it broad.
> Giving talks
Do this / practice this as often as possible. It's how you'll get hired (or not). I've had to sit through some embarrassingly bad ones, where the candidate then has to survive the next 7-hours of interviewing where everyone knows they're not getting hired. (In my experience the talk is first thing in the morning.)
[0] He gave the fields of "Computer Science / Machine Learning / Computer Vision research" as a disclaimer, my disclaimer is that I only know about experiences in molecular biology / chemistry / materials science / synthetic biology / microbiology.
[1] https://www.youtube.com/watch?v=3UEwbRWFZVc
> > Picking the school. ... your dream school should 1) be a top school
> No, at least, in mine and other's experience, you should go to the best school where you're still capable of being in the top ~1% of your graduating class. You'll feel like you're the best and that's almost all that matters (Malcolm Gladwell's talk [1]).
Isn't that a bit short-sighted? What happens after you want to progress to the next environment?
Is it realistic to be in the top 1% anywhere? Isn't that like being the best graduate ever from a small program, or the best in a decade at a larger one? You're not competing with a large population anymore, you're competing with someone who has passed dozens of filter steps in their lifetime and have gotten just as far as you have.
> What happens after you want to progress to the next environment?
It does sound odd, or just wrong. But from Gladwell's example [0], and others, top students in their class, regardless of the school, perform better than expected following graduation. From my experience, that's at least partially due to the increased attention (and better training) that top students receive. The lower ranked students are more-or-less ignored and pushed out.
> Is it realistic to be in the top 1% anywhere?
No. But you may have a higher chance of being a top student at a cheap in-school school than at Harvard.
What "graduating class"? In undergrad studies, your peers are your classmates, but in PhD studies, your peers are the global community of your sub-subfield. For feeling like you're the best, your competition will be someone across the ocean who happens to work on a topic very close to your thesis, and most of students in your department will be largely irrelevant since they'll work in a different field and interact with different topics, papers, ideas and people.
If anything, being in one of the few labs that work on "top" things in a specific domain pretty much guarantees that you'll be in that 1% of your subfield; and if your lab is weak in that (though possibly world class in other fields), then you won't.
Many universities have a "College of Science." Many of those universities give out some sort of "Top Doctoral Student" award, with a different name.
And I largely agree with what you say. That would be how any rational person would measure themselves, you would think. But in reality, you're comparing yourself to the people you're surrounded by, not with the smart guy in Iceland.
Nice comment. In my opinion, in reality you are not comparing to your peers. Instead, you are your peers. There is a quote, something like, you are the average of your best 6 friends. To some degree, I think it makes sense because they basically serve as your inspiration and collaborators.
Personally I would not want to attend a second-tier school/lab as a PhD student, spending half a decade with some mediocre people. I mean, if one truly wants to stand out among the peers/crowds, isn't it a better idea to do really excellent research rather than lower your peers' quality.
Currently I am in a top research group and I might not be the top 1% of my graduating class; yet, I am confident that I am having my best time to learn from my great peers.
How do you quantify being top 1% of your graduating class in a PhD?
I can potentially see how your advice could work for undergrad, but doing good research is difficult if your peers aren't as good. Your discussions are less interesting, your ambitions are scaled down, and so on.
These reasons for getting a PhD seem extremely one-sided and, frankly, inaccurate.
> Exclusivity. There are very few people who make it to the top PhD programs.
Top companies are even more exclusive than PhD programs in terms of acceptance rate.
> you’re strictly more hirable as a PhD graduate or even as a PhD dropout and many companies might be willing to put you in a more interesting position or with a higher starting salary
This is 100% false. Many many people have found a PhD to be a handicap when it comes to getting a job, particularly in software engineering. A large number of employers have anti-PhD biases which will work against you.
> Ownership. The research you produce will be yours as an individual. Your accomplishments will have your name attached to them.
My experience with academic research is just the opposite. I think it's patently ridiculous that professors get "authorship" credit on papers even when they had a minimal, at best, role in it. Meanwhile in companies you can have a tangible impact and see real results/credit from it (bonuses, promotions). Not to mention that many universities have draconian IP policies.
> Top companies are even more exclusive than PhD programs in terms of acceptance rate.
Acceptance rate is meaningless unless the base populations are the same. Considering only the (hopefully) top graduates are interested in applying for PhD, whereas most graduates have to look for jobs, acceptance rates between grad school and companies are really comparable.
> I think it's patently ridiculous that professors get "authorship" credit on papers even when they had a minimal, at best, role in it.
Think of professor as a spiritual mentor. I don't think Jony Ive put his hands on the drawing of latest apple watches anymore, and he gets to sit in the white room every year. It's the philosophical guides he puts into his team that earn him the privilege.
In terms of material compensation, I totally agree with you that companies generally pay much better. The credit part is really nominal. I've know some people working in google, facebook. Their projects range from google cardboard, google daydream, fb live video, to fb 360 video. Honestly, I have never seen their names/credits anywhere else but our private conversations. It's nowhere to be found.
>> you’re strictly more hirable as a PhD graduate or even as a PhD dropout and many companies might be willing to put you in a more interesting position or with a higher starting salary
> This is 100% false. Many many people have found a PhD to be a handicap when it comes to getting a job, particularly in software engineering. A large number of employers have anti-PhD biases which will work against you.
I was wondering if you could comment on this? Personal experience? I may be naive for not believing this in the first place; why would there be disdain expressed towards PhD applicants at a company? Is it a business perspective ("PhDs are too expensive"), technical ("they are too specialized, cannot practically implement solutions we expect of a new hire"), social ("I don't understand academia and couldn't achieve that high"), or something else?
> I was wondering if you could comment on this? Personal experience?
I do not have a PhD. My experience is based on personal experience with hiring people, speaking to friends and other hiring managers, and anecdotes from HN.
In general, the reasons are (for better or worse):
1. PhDs aren't very good programmers or don't follow software engineering best practices.
2. PhDs want to do "research" and will get bored with the basic software production required for 90% of industry jobs.
3. PhDs expect to be paid/respected at a higher level of seniority, even though skill-wise they'e often barely above a recent BA graduate.
I don't know how valid all of these are, but in general I would personally always choose someone with 5 years of industry work experience over someone with a PhD for a software job.
I agree. If you are not having even a single problem in your company which makes you wish you had an expert (not that all PhDs are experts) in a specific domain (all the more if that domain is specialized in a way that you do not normally encounter in a typical software engineer job), it will be an unhappy marriage for both employer and employee.
> Top companies are even more exclusive than PhD programs in terms of acceptance rate.
False. Do you even know how many people apply online to Uber, Slack, Facebook, and Google? More than the entire population of flagship state universities.
> > you’re strictly more hirable as a PhD graduate or even as a PhD dropout and many companies might be willing to put you in a more interesting position or with a higher starting salary
> This is 100% false. Many many people have found a PhD to be a handicap when it comes to getting a job, particularly in software engineering. A large number of employers have anti-PhD biases which will work against you.
I agree.
> Meanwhile in companies you can have a tangible impact and see real results/credit from it (bonuses, promotions). Not to mention that many universities have draconian IP policies.
The results come from products you did not solely produce. There are plenty of other co-workers or teams which helped contribute large components to your product.
> False. Do you even know how many people apply online to Uber, Slack, Facebook, and Google? More than the entire population of flagship state universities.
I'm not sure if you're agreeing or disagreeing. That's precisely my point: Google, for example, gets tens of thousands of applications. Their acceptance rate is incredibly low.
> The results come from products you did not solely produce.
Sure, but I don't see how that detracts from the fact that you can see meaningful rewards for your work in corporate life.
I'm pretty sure the acceptance rate for those applying for jobs at retailers such as Best Buy are lower than the admission rates of many top graduate programs so the acceptance rate only tells part of the story. The barrier to even apply for these graduate programs is significantly higher and very self selecting.
>> False. Do you even know how many people apply online to Uber, Slack, Facebook, and Google? More than the entire population of flagship state universities.
> I'm not sure if you're agreeing or disagreeing. That's precisely my point: Google, for example, gets tens of thousands of applications. Their acceptance rate is incredibly low.
I spoke with an engineer (a PhD) and a recruiter at a recent USENIX conference; they stated Google gets much more than tens of thousands of applications (a factor of ten more than what you stated). I'm not sure if they meant applica_tions_ or unique applica_nts_.
A PhD is super fun and super hard. But it is key to be realistic about the outcomes. It is a life changing experience, but definitely have a plan for the end of it. There are so few academic tenure jobs and so it pays to do a bit of research on what you want at the end off it beyond just academic life. If you can do that the experiences are amazing, learning to think and work at the higher level and learning to compromise and work through others work is utterly refreshing. It is definitely a space to let yourself really explore thinking and researching.
Experiences differ. A lot. Personally, I had an awful time during my PhD, and between the penury and the toil and the bleak prospects afterward, I'd say you're definitely in the minority on this one.
I wouldn't recommend a PhD to anyone who isn't dead set on a job that requires one.
> Personally, I had an awful time during my PhD, and between the penury and the toil and the bleak prospects afterward, I'd say you're definitely in the minority on this one.
Do you have any evidence for the minority claim beyond the personal anecdote?
Also, this is field dependent, so a mention of the field could be helpful.
My claim is based on my own experience as a CS PhD student and my conversations with other graduate students within the department and outside it during my studies. Christ, there was so much bitching. And it consisted of the kind of grim complaints I never heard during undergrad days or later on the job.
That said, CS PhDs are pretty lucky. The worst case fallback position of a job as a software developer isn't bad. It gets much worse for less commercial disciplines. God help the poor blighter with a PhD in Medieval Literature who doesn't snag an academic post.
Yes... the two languages requirement is A LOT easier in technical field than in the humanities. Try being fluent or functionally fluent for an exam in two human, spoken, academic languages vs learning a couple of programming languages. At least that was the experience I had. Learning enough French and German to read and write in French and German at the PhD level was absolutely soul destroying whereas now that I have had several normal business jobs and been asked to learn various programming languages as part of the work was basically... easy by comparison. A few jobs in software development are rapidly becoming the "webmaster" of our time.
I just finished my PhD in CS. Took me about 7 years, damn it feels good to be done.
Mine experience was completely driven by my choice of advisor. He did not push me and therefore I had complete freedom. I was glad to have the funding through him, but did not get much direction. I did almost everything.
And while it took me 7 years to complete, I don't look back on the experience as only "getting my Phd". I learned so much about myself; I traveled around the world; I received my private pilots license; I learned about cars; I played a lot of golf; I had a great time. There were some shitty times where I had to push through, but I have no regrets. Most of my friends are buying houses and having kids, so I'm a little behind on my career/savings. But hey, I have three degrees in a pretty good field and my career is full steam ahead.
>Over time you’ll develop a vocabulary of good words and bad words to use when writing papers. Speaking about machine learning or computer vision papers specifically as concrete examples, in your papers you never “study” or “investigate” (there are boring, passive, bad words); instead you “develop” or even better you “propose”. And you don’t present a “system” or, shudder, a “pipeline”; instead, you develop a “model”. You don’t learn “features”, you learn “representations”. And god forbid, you never “combine”, “modify” or “expand”. These are incremental, gross terms that will certainly get your paper rejected :).
This seems unfair. Many otherwise good students don't get taught this coded language. I understand that heuristic or incremental developments might not be accepted at top conferences, but the work should be judged on what it does rather than the inexact word choice of a student. It feels a bit cliquey.
> Many otherwise good students don't get taught this coded language.
Almost no one is taught this coded language. You absorb it by reading tons and tons of papers, then more papers. Karpathy is unusual and fun to make it explicit like this. He's a canny person.
The reception to your work cannot be judged just on its technical merit. Writing good papers is part of your job in academia; that involves explaining your work in a descriptive, easy-to-follow (for experts in the field) manner.
I got my phd in the humanities. After following academic and grad school subreddits for the last years I could almost be convinced that grad school in the sciences is a pyramid scheme.
So much of the crap that I see people complaining about in the sciences simply doesn't occur in the humanities. Some of it I wish would (co-authorship would be a good way for those of us in the humanities to learn how to write an article for publication) but I am glad I didn't have to deal with a lot of the lab and advisor drama that I have seen (or arguing if someone should be fifth author or sixth...).
Of course I was making slightly more than half my peers in the sciences at the same university were making and they have a lot more career prospects than I do so maybe it is worth it....
18 papers now (bioinformatics & molecular biology) and I have never seen a single authorship dispute. If you are a middle author, complaining about position is biting the hand that feeds you. Most people know better than that. Also, it doesn't even matter as there are only 3 real positions on papers: first, last, and everything else.
Coauthorship IME has always been extremely amiable. Often, when I am a middle author, I almost feel that I'm being done a favor by being put as a coauthor when I didn't do that much work (but I did make a material contribution). Conversely, as a first or senior author, it costs you nothing to add coauthors and gains you goodwill. It's win-win. It's very easy to get coauthorships if you keep your eyes open.
In my experience, if you want to succeed as a Ph.D. in the sciences you have to find a niche that puts you in the position of being useful to other people rather than in competition with them. If you don't, you will fail. If you do, it is almost comically easy to be at least moderately successful.
As a PhD survivor, I liken it to a pyramid scheme these days. There aren't enough tenure track positions to have viable careers for all, so do not go into a doctorate program without considering your non-academia route to happiness and fulfillment.
My experience of getting the PhD was pretty positive (finished after three and half years, good university, good subject, great supervisor) but I still see so much truth in these essays: http://100rsns.blogspot.co.uk/
I walked this path. Think about where you want to be when you're done. Are you married? Do you have kids? Do you want to do those things? If you want to be an academic, you have at least a decade of grueling work ahead. That's 4 years to do the Ph.D., and another 6 to get tenure, which is like doing three more dissertations worth of research while trying to manage a small group of young, inexperienced engineers. And that's if everything goes well. Precious little time for a spouse and kids. Ignore this ridiculous talk about learning to golf and taking vacations abroad. Nobody I know did that.
Personally, I finished the PhD in May and moved on to a really fantastic job in my field. I have time and money that I wouldn't have as an academic.
I'd like to clear up a few misconceptions I had about the academic lifestyle going in. This is about the professor job, not grad school.
1. Being a professor is not about teaching. I went into grad school thinking I could make a difference. Address the rampant gaps in my colleagues' education. It turns out that professors generally abhor teaching, and that departments actually use teaching assignments to punish underperforming faculty. Performance means papers and even more importantly grant money. Teaching doesn't enter into it.
2. Professors do not have any free time. Even tenured ones. That appealing academic calendar is a mirage. If you are a professor, you're a professor every waking minute. You spend your vacations at conferences, your summers trying to get ahead on research. You read papers before breakfast and after dinner. Or you burn out after getting tenure, get a heavy teaching load as punishment for your lack of productivity, and turn into the kind of professor everybody has an anecdote about. The one with nutjob politics who never turns up for office hours, or the one who lectures while hung over or still drunk.
Sorry to rant, grad school is no picnic and I wish I'd gone into it with my eyes wider open. I'm happy with the end result, personal growth, seeing things on a higher level, being "doctor" so-and-so, having an awesome job, but it's not at all an easy way out if you're tired of what you're doing now.
Less than half of CS PhDs have aspirations to become a professor. Most are dead set on industry -- usually machine learning or cutting-edge technology projects at companies.
Fair point, that's not my field. Just be warned if you are interested in an academic career: you won't fix, or even make a dent in, the competence gap in industry by becoming a professor.
If you want a CS Ph.D., demonstrate that you're a REALLY REALLY GOOD programmer; that can make up for lack of significant research experience. I've advised a few dozen students of various ranks, and I still rate programming ability as amongst the top prerequisite ... everything else can be learned more easily in a short time, imho:
They asked linear algebra and probability in the interview for machine learning in my school. Most rospective candidates got asked "what's an eigenvalue?" (among many other questions).
I guess what you should learn depends on the field. But they'll be looking for demonstrable interest and possibly outside learning.
I did a MEng in CS, then went into work in a totally different industry for four years where I hardly touched a computer, and managed to come back in to do a PhD in CS without any research experience.
I literally cold-emailed some potential advisors that I found from looking at university websites and told them what I was passionate about. I got asked in for an interview and pitched what I wanted to work on.
I think if you are grown up and passionate about doing a PhD you are already going to look pretty impressive, as most advisors will usually be dealing with young undergraduates who can often be drifting into a PhD without really knowing what they want.
Someone dumping a paying career they have have already invested most of their 20s into has obviously thought about it very carefully and knows what they want.
Find a topic you like and mail some people in the field directly. If you make it personal you'll probably get a response. I've finished one PhD and dropped out of another. In both cases, it started off with a friendly chat with my potential supervisor.
"It’s not the consequence that makes a problem important, it is that you have a reasonable attack." - That was the most non-obvious and profound thing I noticed when I saw and read Hamming about this.
It practically is a litmus test on answering what you should do, not necessarily in research, but in life.
For those interested, Hamming course on "You and your research" is on Youtube and has a _ton_ of _practical_ advice to future engineers.
> Status. Regardless of whether it should be or not, working towards and eventually getting a PhD degree is culturally revered and recognized as an impressive achievement. You also get to be a Doctor; that’s awesome.
Ha, sarcasm, very funny. Seriously though, outside of academia, if you're at an actual cocktail party or something with normal people, don't ever say that you have a PhD. It's like admitting that you're into some really weird shit. If anyone else mentions it about you, quickly change the subject to something less cringeworthy, like herpes.
And for the love of God, never ever call yourself a "doctor." WTF is wrong with you? No.
If you're still a graduate student ("working towards"), this is all moot, because they shouldn't have let you into the party.
> You’ll sit exhausted and alone in the lab on a beautiful, sunny Saturday scrolling through Facebook pictures of your friends having fun on exotic trips, paid for by their 5-10x larger salaries.
You get around USD 70k per year in Switzerland for a Computer Science PhD in Switzerland (at ETHZ or EPFL). You could get up to five times this amount working in the industry, but usually not much more than that, especially when you have just graduated. It is definitely worth it to take the salary into account when you choose which universities to apply to for a PhD.
I think the larger point is that you won't have time to take those vacations. That being said, a fresh grad that can make it into a top PhD can probably pull 200k+ in Silicon Valley.
> You’ll struggle with the realization that months of your work were spent on a paper with a few citations while your friends do exciting startups with TechCrunch articles or push products to millions of people.
Love Andrej but really, this isn't a common experience.
Here is a blog post on my transition from theoretical physics to data science (and how it made my life much better): http://p.migdal.pl/2015/12/14/sci-to-data-sci.html
I understand that Andriej Karpathy (my favourite author/lecturer in Deep Learning, by a large margin) had a wonderful PhD, in a fast-growing field, with a golden fall-back option. But most PhD students I know (including my former self) do things in disciplines no-one else cares about and are tied to their institute/advisor/place with little to no opportunity to change things when they go awry (cf. it's super easy to change a company). A non-trivial fraction of my friends suffered from depression or had a serious mental breakdown (again, including myself).
In this light, while it contain a large number of helpful tips and valuable pieces of advise, why is it called "survival guide"?