But ignoring that one persons warning about PhD still sounded like he had the typical arrogance contained by every person who ever did a PhD (including me). If someone says “don’t do it” and your reaction is to not even follow up with questions perhaps you should acknowledge you already know why they’re saying that! It’s still not clear to me a person of this cadre need a PhD in a technical field in this day and age; they might contribute just as much faster if they chose any other route.
The most disconcerting part for me was coming back to society, realizing that no one actually knows what's going on most of the time, even when they think they do. I wish more people had the same humbling experience I did (not saying, by the way, that we need more PhD students, just the similar experience).
It's curious why you'd think that the only reason for doing a PhD is to contribute faster. Contribute faster to what? The author says he's having a lot of fun with his PhD because he gets to work on topics that are not immediately tied in with Googles business model.
This is exactly why we need PhD students doing fundamental research, because no one can see the immediate benefit. Because on the surface it has no value at all - so who else would look at it if not curious PhD students with little to no financial incentives.
Constraining possible research breakthroughs to what someone thinks is valuable a-priori, seems very naive and counter productive to me.
If you went through your entire PhD without seriously questioning your life choices at all then we definitely did a different PhD for sure! I and my colleagues constantly discussed the pros and cons and why the system is the way it is.
In general I can never present the problems as clearly as Hamming did so I’ll let him give my viewpoint since I agree with everything here - http://www.paulgraham.com/hamming.html
In all seriousness, I totally acknowledge that the variance in satisfaction with a PhD is very large. I'm lucky to have a great advisor and incredible labmates; my happiness with the program is largely due to them.
This. So much this. As in just about every profession, people make or break places. Your advisor and lab mates are everything... choose wisely, and choose them vs a school for its name.
One my favourite quotes
> “I never allow myself to have an opinion on anything that
I don’t know the other side’s argument better than they do.” — Charlie Munger
They might be able to contribute technically but they might not be given the opportunity to do so if they don't have a PhD.
Hey there, thanks for sharing your experience.
Would you mind telling us which technical field? And which country?
I know several PhD students / recent grads that pursued this qualification primarily because of "academic inflation" - that in order to compete for top-paying jobs in the EU, the more qualified you are the better, and having "just a Masters degree" is no longer good enough.
I should add, these are also ex-pats, who left their developing country in pursuit of greener pastures. But it seems to be the case that getting a PhD - as terrible as the experience might be - can also help with career prospects, purely because one has a PhD (irrespective of the details of their thesis topic).
Whereas back home (in said developing country), it's the complete opposite. Qualifications hardly matter - or they certainly don't matter as much - only experience does. And given the thin supply of skilled labour, business owners have the mindset of "we'll take what we can get".
So I do think the choice to pursue a PhD can be an indirectly strategic move depending on your job prospects - and less about the actual topic you plan to research.
Just my 2 cents from a developing country that is experiencing excessive brain-drain! :)
Of course if you can’t or won’t program and want to be in a hard science a PhD might be necessary but it’s become generally annoyingly competitive and fruitlessly bureaucratic to be in the field and depend on it for long term guaranteed career growth. When I said tech I meant software-related: as long as you are interested or willing to be there it still seems the best place to make money without too much sweat and not need a PhD to do it.
This also applies only if you’re good but not cream of the crop. If you’re doing a PhD under a superstar and have nature papers then you’re clearly set up well, but anything lower than that it’s not worth the competition and pain you need to put in to make a good career either in academia or industry.
I did my PhD in an in-demand field at a top uni.
My partner did their PhD in a less in-demand field at a not so top uni.
The difference in our experiences -- and how those experiences were directly impacted by the financial motivations of the department/university -- was beyond staggering. My partner was cheap teaching labor. I was a source of fellowship+grant money and potentially valuable patents/prestige. I could leave and 10x my salary with a single email (ultimately did). My partner would've been lucky to teach high school. In literally every interaction with the university, you could tell.
Universities are just another org with budget and headcount and metrics to optimize. Academia is no longer sheltered from the shitty labor politics of finance capitalism. NEVER let anyone tell you otherwise.
That has more to do with the field than the school. A CS grad student at a third-tier school can leave and take a well-compensated corporate job whenever they want. A history grad student at a top-tier school will find that more difficult.
It never was. And politics in general, not just labor.
I had a half dozen tenure-track offers and decided on industry. Honestly, academia is not what it used to be. Lots of professors come close to killing their under-paid grad students so that they can spend 20% of their time on blue sky research ideas. I get that time and then some without throwing poor kids under the bus. Without even asking, really. And make 3x what I would've made as a slave driver to boot.
Academia sucks, especially if you want to do good research. Seriously, outside of tippy-top elite institutions that can get the very best and brightest phd students, colleges and universities are some of the absolute worst places to do research these days.
And if you want to teach CS, don't bother with a PhD. Just ad junct a bit on the side of an industry gig, do not-insanely-bad, and you'll be full time within a year or two. If you want. Which you very likely wont, because teaching one class on the side while engineering is a lot more fun than teaching full time. Teaching is exhausting, even for people who love teaching.
Second, set expectations with your advisor -- you are not there to further your advisor's career, you are pursuing your own career goals and are not just cheap labor. If you start doing random tasks that your advisor needs done, tasks that are not related to your own research, you will find yourself doing those tasks all the time. It is like any other job, you need to make it clear that you are not going to work outside of your role.
Finally, and most important of all, keep your dignity and remember that you can always take a more satisfying job (for better pay). Make sure you pick up a master's degree ~2 years into the PhD program, it helps if you want to leave. You should never feel like you are trapped; years spent in a PhD program will count as years of relevant employment at most tech companies (at least those I am familiar with). The only reason you need a PhD is if you want a tenure track position, and you should know that junior faculty face a new lineup of BS as they work towards tenure.
Don't feed the trolls and instead read both of the replies to your original comment that focused on constructive advice.
I'm not actually sure what semantics you're referring to, btw. AFAIK you went to CMU ECE (not SCS) and not Harvard so I'm not so sure you can make that comparison anyway. How exactly do you think I should prove it?
Didn't you say "its over for you" previously
> You're at Amazon and probably earning more than most people here
I find that unlikely
> Both scenarios seem favorable for your future
>In fact, most of my mentors at Brain encouraged me to enroll in the PhD program. Only one researcher strongly discouraged me from pursuing a PhD, comparing the experience to “psychological torture.” I was so shocked by his dark warning that I didn’t ask any follow-up questions, he didn’t elaborate, and our meeting ended shortly afterwards.
What he did was discounted the unpopular and negative opinion because it was so polarising: “psychological torture”.
Maybe I need to lighten up here, but I find this "AI-first" branding to be really off-putting. What I fear it means is "We're going to add the world's biases and a bit more unpredictability into every single one of our features."
And in some ways it's like like a carpenter deciding to become a "wrench-first" operation. Shouldn't the first priority be the people affected by the things that you do, rather than a particular tool that you inflict upon them? And, great as it can be, is it really the right tool for every job? Maybe some of the problems the AI fairness team has exposed (or tried to) will push Google to realize that it need not be present in everything, let alone first.
Honestly my first reaction was a snarky "Well, your moderation and support channels certainly are" given all the horror stories we hear about people getting locked out of their entire google accounts with no recourse or access to humans to appeal / investigate.
"AI-first" isn't because Sundar thinks that AI is the best solution for Google's problems. "AI-first" is because Sundar thinks that getting good at AI is an important long-term investment for Google.
So this is less like a carpenter deciding to use a wrench for every operation because they think that the results will be good and more like a carpenter deciding that they should find a project to use a wrench on because they want to learn how to use one.
Sounds like some folks didn't like writing! :)
I just don't get some HN assertions...
Looks alphabetical for me
This seems a not too uncommon experience among CS people where machine learning loses its interest once they understand the magic
The electric motor had been invented (by Faraday), but it couldn't do any useful work and had to sit in a pool of mercury to function. Electric lighting had been invented, but the filaments were dim and burned out too quickly to be practical. There was no way to generate or distribute electricity at scale. To most people, electricity looked like a toy (and a terrifying toy at that). It would take decades of invention and refinement to get to the point where electricity was useful, reliable, and ubiquitous. But the seeds for doing so were there in 1850.
So when Sundar Pichai says AI is more profound than electricity, it's possible he's right. It's just that we're still in its 1850, so we can't see it yet.
You can make a claim that "This might be revolutionary! It's just too early to tell." about literally anything.
There was no ML in 1850, and the word "tensor" was invented in 1846. No biochemistry, no genetics, no quantum mechanics.
But I would not say that makes it uninteresting. Nor does it make it irrelevant from a business perspective. There are still lots of beneficial ways to use ML, and lots of different angles from which to derive intellectual satisfaction. I suppose if someone went into the field expecting I Robot type stuff, they might be disappointed, but it's hard to imagine someone with a CS background having such unrealistic expectations of what ML actually is.
Maybe. I went into my program with no preconceived ideas that there was 'magic' happening here. What I got out of it was a real curiosity about how humans think and how that may not be a magical as we think either.
Long-term I cannot see AI being anything but the paradigm-smashing artifact it's sold as. But progress is (relatively) slow on a human scale, so we find incremental progress ultimately lackluster.
If you take it through that lens it becomes more reasonable and you find yourself less skeptical that, just as we have no flying cars, we have no AGI or that all work has been replaced.
I would say it has achieved that status already :)
Deep learning is getting more than its share of hype. Convex optimization is a jewel of computational science.
One question: can you elaborate a little on your point about intellectual diversity?
Of course there's nothing wrong with focusing specifically on AI. It's just that I wanted to develop my research sensibilities in a more diverse environment.
edit: & thank you for the kind words!
But that is just my experience. Some of my labmates had PhD's in which the majority of their work was collaborative, across disciplines.
Good luck with everything Akshay!!
What an interesting way to view the world...
I always find fairly positive accounts of graduate school to be quite interesting. Without a doubt, you can always find someone in the comments (if it's something posted in a format such as this) with a chip on their shoulder from their own graduate school experience (or lack thereof). Also I find it interesting to think about places similar in impact to Xerox PARC, which people usually admire after everything unfolds and becomes a part of history. A good chunk of people from today would probably avoid a place like PARC back then in order to opt for a more stable or known job.
@akshayka can you elaborate a bit more on why you think that
When paired with a robotic medium, we'll be able to automate most pattern driven jobs like construction, welding, surgery, anything really that doesn't require human element to it and is more of a pattern matching behavior.
This is extremely revolutionary and will put a ton of people out of work. Any pattern-dependent job that does not require human interaction will be at risk, even coding (as we've seen by GPT-3's ability to generate code). Lawyers, sales people, cops, these jobs will be ok, but there will be less demand for humongous medical teams, software teams, etc. Bc all the entry-level work can be handled by AI, and just need some QA from a few engineers.
Three lawyers with the right set of tools (e.g. for discovery, writing legal briefs, etc.) can be more productive than twenty lawyers.
The state of the art of today’s DNN, I agree, is far away. But the peak of today’s architecture is yes, replacing construction, welding, and surgery. Those actually might happen quickly.
However, I do believe we are far and away from generalized intelligence.
The comparison to fire and electricity aside --- you're absolutely right, we can do very interesting things with machine learning & automatic differentiation these days!
"Google’s CEO Sundar Pichai (who believes AI is “more profound than electricity or fire”) has emphasized that Google is an “AI-first” company, with the company seeking to implement machine learning in nearly everything do"
Nearly everything they do with your data.
IMO they are clearly betting (parts of) the farm on this as can be seen by the (often) ridiculous results many of us have witnessed in both search results quality and ad matching quality.